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1
Kayatas, Zafer, Dieter Bestle, Pascal Bestle, and Robin Reick. "Generation of Realistic Cut-In Maneuvers to Support Safety Assessment of Advanced Driver Assistance Systems." Applied Mechanics 4, no. 4 (September 28, 2023): 1066–77. http://dx.doi.org/10.3390/applmech4040054.
Анотація:
Advanced Driver Assistance Systems (ADASs) attract constantly growing attention from academics and industry as more and more vehicles are equipped with such technology. Level-3 ADASs, like the DRIVE PILOT from Mercedes-Benz AG, are expected to appear more and more on the market in the next few years. However, automated driving raises new challenges for the system validation required for series approval. The replacement of a human driver as control instance expands the range of variants to be validated and verified. The scenario-based validation approach meets these challenges by simulating only specific safety-critical driving scenarios using software-in-the-loop simulation. According to the current state of the art, various safety-relevant driving scenarios are parameterized as idealized maneuvers which, however, requires a great modeling effort, and at the same time, such simplifications may bias the safety assessment. Therefore, a novel approach using artificial intelligence methods is taken here to generate more realistic driving scenarios. Namely, a generative model based on a variational autoencoder is trained with real-world data and then used to generate trajectories for a specific driving maneuver. Through a comprehensive analysis of the synthetic trajectories, it becomes clear that the generative model can learn and replicate relevant properties of real driving data as well as their probabilistics much better than the mathematical models used so far. Furthermore, it is proven that both the statistical properties and the time characteristics are almost equal to those of the input data.
2
Ahrenhold, Nils, Ingrid Gerdes, Thorsten Mühlhausen, and Annette Temme. "Validating Dynamic Sectorization for Air Traffic Control Due to Climate Sensitive Areas: Designing Effective Air Traffic Control Strategies." Aerospace 10, no. 5 (April 26, 2023): 405. http://dx.doi.org/10.3390/aerospace10050405.
Анотація:
Dynamic sectorization is a powerful possibility to balance the controller workload with respect to traffic flows changing over time. A multi-objective optimization system analyzes the traffic flow over time and determines suitable time-dependent sectorizations. Our dynamic sectorization system is integrated into a radar display as part of a working environment for air traffic controllers. A use case defining climate-sensitive areas leads to changes in traffic flows. When using the system, three controllers are assessed in two scenarios: the developed controller assistance system and the work in a dynamic airspace sectorization environment. We performed a concept validation in which we evaluated how controllers cope with sectors adapting to the traffic flow. The solution was rated as highly applicable by the involved controllers. The trials revealed the necessity to adapt the current procedures and define new aspects more precisely. In this paper, we present the developed environment and the theoretical background as well as the traffic scenarios. Furthermore, we describe the integration in an Air Traffic Management (ATM) environment and the questionnaires developed to assess the functionality of the dynamic sectorization approach. Finally, we present a proposal to enhance controller guidelines in order to cope with situations emerging from dynamic sectorizations, including naming conventions and phraseology.
3
Tatar, Mugur. "Test and Validation of Advanced Driver Assistance Systems Automated Search for Critical Scenarios." ATZelektronik worldwide 11, no. 1 (February 2016): 54–57. http://dx.doi.org/10.1007/s38314-015-0574-1.
4
Ametller, Adria, and Chris Brace. "A Coupling Architecture for Remotely Validating Powertrain Assemblies." SAE International Journal of Electrified Vehicles 12, no. 2 (March 15, 2023): 279–300. http://dx.doi.org/10.4271/14-12-02-0015.
Анотація:
<div>Among the myriad of potential hybrid powertrain architectures, selecting the optimal for an application is a daunting task. Whenever available, computer models greatly assist in it. However, some aspects, such as pollutant emissions, are difficult to model, leaving no other option than to test. Validating plausible options before building the powertrain prototype has the potential of accelerating the vehicle development even more, doing so without shipping components around the world. This work concerns the design of a system to virtually couple—that is, avoiding physical contact—geographically distant test rigs in order to evaluate the components of a powertrain. In the past, methods have been attempted, either with or without assistance of mathematical models of the coupled components (observers). Existing methods are accurate only when the dynamics of the systems to couple are slow in relation to the communication delay. Also, existing methods seem to overlook the implications of operating a distributed system without a common time frame. In order to overcome the inherent latency arising from long-range communication, the proposed design combines two features: The exploitation of synchronized clocks for the simultaneous introduction of setpoint commands and the use of observers generated through machine learning algorithms. This novel design is subsequently tested in two scenarios: A simple one, involving the virtual coupling of two parts of an elementary device formed by three rotating inertias, and a more complex one, the coupling between an internal combustion engine and an electric motor/generator as representative of a series or parallel hybrid powertrain. Although the results are heavily influenced by the quality of the data-generated observers, the architecture improves the fidelity of the coupling by nearly an order of magnitude compared to the alternative of directly transmitting the signals. It also opens a niche application that leverages the accuracy of low-fidelity models.</div>
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Marcano, Mauricio, José A. Matute, Ray Lattarulo, Enrique Martí, and Joshué Pérez. "Low Speed Longitudinal Control Algorithms for Automated Vehicles in Simulation and Real Platforms." Complexity 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/7615123.
Анотація:
Advanced Driver Assistance Systems (ADAS) acting over throttle and brake are already available in level 2 automated vehicles. In order to increase the level of automation new systems need to be tested in an extensive set of complex scenarios, ensuring safety under all circumstances. Validation of these systems using real vehicles presents important drawbacks: the time needed to drive millions of kilometers, the risk associated with some situations, and the high cost involved. Simulation platforms emerge as a feasible solution. Therefore, robust and reliable virtual environments to test automated driving maneuvers and control techniques are needed. In that sense, this paper presents a use case where three longitudinal low speed control techniques are designed, tuned, and validated using an in-house simulation framework and later applied in a real vehicle. Control algorithms include a classical PID, an adaptive network fuzzy inference system (ANFIS), and a Model Predictive Control (MPC). The simulated dynamics are calculated using a multibody vehicle model. In addition, longitudinal actuators of a Renault Twizy are characterized through empirical tests. A comparative analysis of results between simulated and real platform shows the effectiveness of the proposed framework for designing and validating longitudinal controllers for real automated vehicles.
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Cuma, Mehmet Uğraş, Çağrı Dükünlü, and Emrah Yirik. "Smart Driver Behavior Recognition and 360-Degree Surround-View Camera for Electric Buses." Electronics 12, no. 13 (July 6, 2023): 2979. http://dx.doi.org/10.3390/electronics12132979.
Анотація:
The automotive industry’s focus on driver-oriented issues underscores the critical importance of driver safety. This paper presents the development of advanced driver assistance system (ADAS) algorithms specifically tailored for an electric bus (e-bus) to enhance safety. The proposed approach incorporates two key components: a 360-degree surround-view system and driver behavior recognition utilizing the You Only Look Once V5 (YOLO_V5) method. The adoption of YOLO_V5 in ADASs enables rapid response by processing multiple class probabilities and region proposals within an image instantaneously. Additionally, ADAS implementation includes an image processing-based surround-view system utilizing OpenCV. In order to evaluate the performance of the proposed algorithms regarding a smart e-bus, comprehensive experimental studies were conducted. The driver behavior recognition system underwent rigorous testing using various images captured by an onboard camera. Similarly, the surround-view system’s performance was verified in diverse driving scenarios, including regular driving, parking, and parking in near-to-line situations. The results demonstrate the viability and effectiveness of the proposed system, validating its potential to significantly improve driver safety in electric buses. This paper provides a comprehensive overview of the work accomplished by emphasizing the specific contributions of the 360-degree surround-view system, driver behavior recognition using YOLO_V5, and the experimental validation conducted for an e-bus.
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Lagarda-Leyva, Ernesto A., María Paz Guadalupe Acosta-Quintana, Javier Portugal-Vásquez, Arnulfo A. Naranjo-Flores, and Alfredo Bueno-Solano. "System Dynamics and Sustainable Solution: The Case in a Large-Scale Pallet Manufacturing Company." Sustainability 15, no. 15 (July 31, 2023): 11766. http://dx.doi.org/10.3390/su151511766.
Анотація:
The proposal in the present research study is the result of a more than two-year process developed in a pallet manufacturing company for anchor enterprises in Southern Sonora, Mexico dedicated to beer production and export to the United States of America. Considering the high pallet demand for this supplier, a strategic plan was created in 2021, establishing an important project for developing technological solutions to improve decision making supported by graphical user interface and focused on sustainability. This study shows the application of system dynamics in all the wood and pallet manufacturing processes with a strategic sourcing supply chain. The method used for its development had the following stages: (1) developing the mapping process; (2) creating the causal loop diagram; (3) developing a flow and stock model with the representing mathematical equations; (4) simulating and validating current scenarios; (5) evaluating normal, optimistic, and pessimistic scenarios with multicriteria decision making using Technique to Order Preferences by Similarity and the Ideal Solution (TOPSIS) and Faire Un Choix Adéquat (FUCA); (6) building the graphical interface. The most relevant results for the company were having quantitative information regarding the pallet demand required by the main client for wood availability, which was the main restriction in the supply chain. The solution was based on four validation tests that allowed decision makers to support the production proposals considering the assistance of the dynamic models. The main conclusion demonstrated that using well-defined operation rules and policies—considering the installed capacity and pallet demand through the model solution—allows anticipating decisions on pallet quantity and reducing the risk of out-of-time deliveries.
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Expósito Jiménez, Víctor J., Bernhard Winkler, Joaquim M. Castella Triginer, Heiko Scharke, Hannes Schneider, Eugen Brenner, and Georg Macher. "Safety of the Intended Functionality Concept Integration into a Validation Tool Suite." ACM SIGAda Ada Letters 43, no. 2 (June 6, 2024): 69–72. http://dx.doi.org/10.1145/3672359.3672369.
Анотація:
Nowadays, the increasing complexity of Advanced Driver Assistance Systems (ADAS) and Automated Driving (AD) means that the industry must move towards a scenariobased approach to validation rather than relying on established technology-based methods. This new focus also requires the validation process to take into account Safety of the Intended Functionality (SOTIF), as many scenarios may trigger hazardous vehicle behaviour. Thus, this work demonstrates how the integration of the SOTIF process within an existing validation tool suite can be achieved. The necessary adaptations are explained with accompanying examples to aid comprehension of the approach.
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Savino, Giovanni, Marco Pierini, and Michael G. Lenné. "Development of a low-cost motorcycle riding simulator for emergency scenarios involving swerving." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 230, no. 14 (August 5, 2016): 1891–903. http://dx.doi.org/10.1177/0954407015624998.
Анотація:
The development of advanced riding assistance systems requires the analysis of user reactions in emergency situations. Motorcycle riding simulators are an alternative to ‘on-road’ testing so that virtual environment dangerous scenarios can be investigated without risks for the participants. In this paper, we propose a process for validation of a low-cost motorcycle simulator characterized by, first, an elastic resistance on the steering input and, second, a counter-steering strategy. For this, 16 riders tested the simulator in different manoeuvres, including a cornering manouvre in a non-urban environment, a slalom manoeuvre and a lane-change manoeuvre. Objective evaluations and subjective evaluations showed that the simulator was realistic, in particular for investigating lateral avoidance scenarios. The development of suitable motorcycle simulators will significantly advance the field of motorcycle safety research.
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Rundo, Francesco. "Deep LSTM with Dynamic Time Warping Processing Framework: A Novel Advanced Algorithm with Biosensor System for an Efficient Car-Driver Recognition." Electronics 9, no. 4 (April 6, 2020): 616. http://dx.doi.org/10.3390/electronics9040616.
Анотація:
The latest generation of cars are increasingly equipped with driver assistance systems called ADAS (advanced driver assistance systems) which are able to assist the car driver in different driving scenarios, and in the most advanced automation levels, able to take over driving the car if required due to dangerous situations. Therefore, it is essential to adapt the ADAS specifically to the car-driver’s identity in order to better customize the driving assistance. To this end, algorithms that allow correct recognition of the vehicle driver are fundamental and preparatory. In this context, an algorithm for car-driver identity recognition is proposed which allows, with an accuracy close to 99%, recognition of the driver by means of a properly designed pipeline based on the analysis of the car driver PhotoPlethysmoGraphic (PPG) signal. The proposed approach makes use of deep long short-term memory (LSTM) architecture for learning such PPG signal features needed to discriminate one car driver from another. The extended validation and testing of the proposed system confirm the reliability of the proposed pipeline.
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Wang, Wei, Zhening Shen, and Zhengran Zhou. "A Novel Vision- and Radar-Based Line Tracking Assistance System for Drone Transmission Line Inspection." Remote Sensing 16, no. 2 (January 16, 2024): 355. http://dx.doi.org/10.3390/rs16020355.
Анотація:
This paper introduces a position controller for drone transmission line inspection (TLI) utilizing the integral sliding mode control (SMC) method. The controller, leveraging GNSS and visual deviation data, exhibits high accuracy and robust anti-interference capabilities. A deviation correction strategy is proposed to capture high-voltage transmission line information more robustly and accurately. Lateral position deviation is calculated using microwave radar data, attitude angle data, and deviation pixels derived from transmission line recognition via MobileNetV3. This approach enables accurate and stable tracking of transmission lines in diverse and complex environments. The proposed inspection scheme is validated in settings with 10-kilovolt and 110-kilovolt transmission lines using a drone with a diagonal wheelbase of 0.275 m. The experimental process is available in the YouTube link provided. The validation results affirm the effectiveness and feasibility of the proposed scheme. Notably, the absence of a high-precision positioning system in the validation platform highlights the scheme’s versatility, indicating applicability to various outdoor visual-based tracking scenarios using drones.
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Park, Changwoo, Seunghwan Chung, and Hyeongcheol Lee. "Vehicle-in-the-Loop in Global Coordinates for Advanced Driver Assistance System." Applied Sciences 10, no. 8 (April 11, 2020): 2645. http://dx.doi.org/10.3390/app10082645.
Анотація:
Most vehicle controllers are developed and verified with V-model. There are several traditional methods in the automotive industry called “X-in-the-Loop (XIL)”. However, the validation of advanced driver assistance system (ADAS) controllers is more complicated and needs more environmental resources because the controller interacts with the external environment of the vehicle. Vehicle-in-the-Loop (VIL) is a recently being developed approach for simulating ADAS vehicles that ensures the safety of critical test scenarios in real-world testing using virtual environments. This new test method needs both properties of traditional computer simulations and real-world vehicle tests. This paper presents a Vehicle-in-the-Loop topology for execution in global Coordinates system. Also, it has a modular structure with four parts: synchronization module, virtual environment, sensor emulator and visualizer, so each part can be developed and modified separately in combination with other parts. This structure of VIL is expected to save maintenance time and cost. This paper shows its acceptability by testing ADAS on both a real and the VIL system.
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Garcia A., Daniel E., Sergio D. Sierra M., Daniel Gomez-Vargas, Mario F. Jiménez, Marcela Múnera, and Carlos A. Cifuentes. "Semi-Remote Gait Assistance Interface: A Joystick with Visual Feedback Capabilities for Therapists." Sensors 21, no. 10 (May 19, 2021): 3521. http://dx.doi.org/10.3390/s21103521.
Анотація:
The constant growth of pathologies affecting human mobility has led to developing of different assistive devices to provide physical and cognitive assistance. Smart walkers are a particular type of these devices since they integrate navigation systems, path-following algorithms, and user interaction modules to ensure natural and intuitive interaction. Although these functionalities are often implemented in rehabilitation scenarios, there is a need to actively involve the healthcare professionals in the interaction loop while guaranteeing safety for them and patients. This work presents the validation of two visual feedback strategies for the teleoperation of a simulated robotic walker during an assisted navigation task. For this purpose, a group of 14 clinicians from the rehabilitation area formed the validation group. A simple path-following task was proposed, and the feedback strategies were assessed through the kinematic estimation error (KTE) and a usability survey. A KTE of 0.28 m was obtained for the feedback strategy on the joystick. Additionally, significant differences were found through a Mann–Whitney–Wilcoxon test for the perception of behavior and confidence towards the joystick according to the modes of interaction (p-values of 0.04 and 0.01, respectively). The use of visual feedback with this tool contributes to research areas such as remote management of therapies and monitoring rehabilitation of people’s mobility.
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Biebl, Bianca, Max Kuhn, Franziska Stolle, Jing Xu, Klaus Bengler, and Alex R. Bowers. "Knowing me, knowing you—A study on top-down requirements for compensatory scanning in drivers with homonymous visual field loss." PLOS ONE 19, no. 3 (March 1, 2024): e0299129. http://dx.doi.org/10.1371/journal.pone.0299129.
Анотація:
Objective It is currently still unknown why some drivers with visual field loss can compensate well for their visual impairment while others adopt ineffective strategies. This paper contributes to the methodological investigation of the associated top-down mechanisms and aims at validating a theoretical model on the requirements for successful compensation among drivers with homonymous visual field loss. Methods A driving simulator study was conducted with eight participants with homonymous visual field loss and eight participants with normal vision. Participants drove through an urban surrounding and experienced a baseline scenario and scenarios with visual precursors indicating increased likelihoods of crossing hazards. Novel measures for the assessment of the mental model of their visual abilities, the mental model of the driving scene and the perceived attention demand were developed and used to investigate the top-down mechanisms behind attention allocation and hazard avoidance. Results Participants with an overestimation of their visual field size tended to prioritize their seeing side over their blind side both in subjective and objective measures. The mental model of the driving scene showed close relations to the subjective and actual attention allocation. While participants with homonymous visual field loss were less anticipatory in their usage of the visual precursors and showed poorer performances compared to participants with normal vision, the results indicate a stronger reliance on top-down mechanism for drivers with visual impairments. A subjective focus on the seeing side or on near peripheries more frequently led to bad performances in terms of collisions with crossing cyclists. Conclusion The study yielded promising indicators for the potential of novel measures to elucidate top-down mechanisms in drivers with homonymous visual field loss. Furthermore, the results largely support the model of requirements for successful compensatory scanning. The findings highlight the importance of individualized interventions and driver assistance systems tailored to address these mechanisms.
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Putter, Roman, Andre Neubohn, Andre Leschke, and Roland Lachmayer. "Predictive Vehicle Safety—Validation Strategy of a Perception-Based Crash Severity Prediction Function." Applied Sciences 13, no. 11 (June 1, 2023): 6750. http://dx.doi.org/10.3390/app13116750.
Анотація:
Traffic accident avoidance and mitigation are the main targets of accident research and vehicle safety development worldwide. Despite improving advanced driver assistance systems (ADAS) and active safety systems, it will not be possible to avoid all vehicle accidents in the near future. Innovative Pre-Crash systems (PCS) should contribute to the accident mitigation of unavoidable accidents. However, there are no standardized testing methods for Pre-Crash systems. In particular, irreversible Pre-Crash systems lead to great challenges in the verification and validation (V&V) process. The reliable and precise real-time crash severity prediction (CSP) is, however, the basic prerequisite for irreversible PCS activation. This study proposes a novel validation and safety assessment strategy for a perception-based crash severity prediction function. In doing so, the intended functionality, safety and validation requirements of PCS are worked out in the context of ISO 26262 and ISO/PAS 21448 standards. In order to reduce the testing effort, a real-data-driven scenario-based testing approach is applied. Therefore, the authors present a novel unsupervised machine learning methodology for the creation of concrete and logical test scenario catalogs based on K-Means++ and k-NN algorithms. The developed methodology is used on the GIDAS database to extract 35 representative clusters of car to car collision scenarios, which are utilized for virtual testing. The limitations of the presented method are disclosed afterwards to help future research to set the right focus.
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Mohammad Sojon Beg, Muhammad Yusri Ismail, and Md. Saef Ullah Miah. "Evaluating the Performance of a Visual Support System for Driving Assistance using a Deep Learning Algorithm." Journal of Advanced Research in Applied Sciences and Engineering Technology 34, no. 1 (November 23, 2023): 38–50. http://dx.doi.org/10.37934/araset.34.1.3850.
Анотація:
The issue of road accidents endangering human life has become a global concern due to the rise in traffic volumes. This article presents the evaluation of an object detection model for University of Malaysia Pahang (UMP) roadside conditions, focusing on the detection of vehicles, motorcycles, and traffic lamps. The dataset consists of the driving distance from Hospital Pekan to the University of Malaysia Pahang. Around one thousand images were selected in Roboflow for the train dataset. The model utilises the YOLO V8 deep learning algorithm in the Google Colab environment and is trained using a custom dataset managed by the Roboflow dataset manager. The dataset comprises a diverse set of training and validation images, capturing the unique characteristics of Malaysian roads. The train model's performance was assessed using the F1 score, precision, and recall, with results of 71%, 88.2%, and 84%, respectively. A comprehensive comparison with validation results has shown the efficacy of the proposed model in accurately detecting vehicles, motorcycles, and traffic lamps in real-world Malaysian road scenarios. This study contributes to the improvement of intelligent transportation systems and road safety in Malaysia.
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Sanchez-Mateo, S., E. Perez-Moreno, F. Jimenez, F. Serradilla, A. Cruz Ruiz, and S. De la Fuente Tamayo. "Validation of an Assistance System for Merging Maneuvers in Highways in Real Driving Conditions." Science & Technique 18, no. 6 (December 5, 2019): 525–31. http://dx.doi.org/10.21122/2227-1031-2019-18-6-525-531.
Анотація:
In the latest study conducted by the National Highway Traffic Safety Administration in 2018, it was published that human error is still considered the major factor in traffic accidents, 94 %, compared with other causes such as vehicles, environment and unknown critical reasons. Some driving scenarios are especially complex, such as highways merging lanes, where the driver obtains information from the environment while making decisions on how to proceed to perform the maneuver smoothly and safely. Ignorance of the intentions of the drivers around him leads to risky situations between them caused by misunderstandings or erroneous assumptions or perceptions. For this reason, Advanced Driver Assistance Systems could provide information to obtain safer maneuvers in these critical environments. In previous works, the behavior of the driver by means of a visual tracking system while merging in a highway was studied, observing a cognitive load in those instants due to the high attentional load that the maneuver requires. For this reason, a driver assistance system for merging situations is proposed. This system uses V2V communications technology and suggests to the driver how to modify his speed in order to perform the merging manoeuver in a safe way considering the available gap and the relative speeds between vehicles. The paper presents the results of the validation of this system for assisting in the merging maneuver. For this purpose, the interface previously designed and validated in terms of usability, has been integrated into an application for a mobile device, located inside the vehicle and tests has been carried out in real driving conditions.
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Stabile, Pietro, Federico Ballo, Giorgio Previati, Giampiero Mastinu, and Massimiliano Gobbi. "Eco-Driving Strategy Implementation for Ultra-Efficient Lightweight Electric Vehicles in Realistic Driving Scenarios." Energies 16, no. 3 (January 30, 2023): 1394. http://dx.doi.org/10.3390/en16031394.
Анотація:
This paper aims to provide a quantitative assessment of the effect of driver action and road traffic conditions in the real implementation of eco-driving strategies. The study specifically refers to an ultra-efficient battery-powered electric vehicle designed for energy-efficiency competitions. The method is based on the definition of digital twins of vehicle and driving scenario. The models are used in a driving simulator to accurately evaluate the power demand. The vehicle digital twin is built in a co-simulation environment between VI-CarRealTime and Simulink. A digital twin of the Brooklands Circuit (UK) is created leveraging the software RoadRunner. After validation with actual telemetry acquisitions, the model is employed offline to find the optimal driving strategy, namely, the optimal input throttle profile, which minimizes the energy consumption over an entire lap. The obtained reference driving strategy is used during real-time driving sessions at the dynamic driving simulator installed at Politecnico di Milano (DriSMi) to include the effects of human driver and road traffic conditions. Results assess that, in a realistic driving scenario, the energy demand could increase more than 20% with respect to the theoretical value. Such a reduction in performance can be mitigated by adopting eco-driving assistance systems.
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Wu, Cheng, Xiang Qiang, Yiming Wang, Changsheng Yan, and Guangyao Zhai. "Efficient detection of obstacles on tramways using adaptive multilevel thresholding and region growing methods." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 232, no. 5 (July 20, 2017): 1375–84. http://dx.doi.org/10.1177/0954409717720840.
Анотація:
With the rapid development of light-rail public transportation, video-based obstacle detection is becoming an essential and foregoing task in driver assistance systems. The system should be able to automatically survey the tramway using an onboard camera. However, the functioning of the system is challenging due to the presence of various ground types, different weather and illumination conditions, as well as varying time of acquisition. This article presents a real-time tramway detection method that deals efficiently with various challenging situations in real-world urban rail traffic scenarios. It first uses an adaptive multilevel thresholding method to segment the regions of interest of the tramway, in which the threshold parameters are estimated using a local accumulated histogram. The approach then adopts the region growing method to decrease the influence of environmental noise and to predict the trend of the tramway. The experiment validation of this study proves that the method is able to correctly detect tramways even in challenging scenarios and uses lesser computational time to meet the real-time demand.
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Huang, Qiao, and Jinlong Liu. "Practical limitations of lane detection algorithm based on Hough transform in challenging scenarios." International Journal of Advanced Robotic Systems 18, no. 2 (March 1, 2021): 172988142110087. http://dx.doi.org/10.1177/17298814211008752.
Анотація:
The vision-based road lane detection technique plays a key role in driver assistance system. While existing lane recognition algorithms demonstrated over 90% detection rate, the validation test was usually conducted on limited scenarios. Significant gaps still exist when applied in real-life autonomous driving. The goal of this article was to identify these gaps and to suggest research directions that can bridge them. The straight lane detection algorithm based on linear Hough transform (HT) was used in this study as an example to evaluate the possible perception issues under challenging scenarios, including various road types, different weather conditions and shades, changed lighting conditions, and so on. The study found that the HT-based algorithm presented an acceptable detection rate in simple backgrounds, such as driving on a highway or conditions showing distinguishable contrast between lane boundaries and their surroundings. However, it failed to recognize road dividing lines under varied lighting conditions. The failure was attributed to the binarization process failing to extract lane features before detections. In addition, the existing HT-based algorithm would be interfered by lane-like interferences, such as guardrails, railways, bikeways, utility poles, pedestrian sidewalks, buildings and so on. Overall, all these findings support the need for further improvements of current road lane detection algorithms to be robust against interference and illumination variations. Moreover, the widely used algorithm has the potential to raise the lane boundary detection rate if an appropriate search range restriction and illumination classification process is added.
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Paradeda, Raul, Álisson Alves, Daniel Torres, and Althierfson Lima. "The Emotions and Advice in Virtual Assistants: A Dual Study on Emotion Validation and Agent Suggestions in a Gaming Scenario." Journal on Interactive Systems 15, no. 1 (January 1, 2024): 118–29. http://dx.doi.org/10.5753/jis.2024.3725.
Анотація:
In an era where virtual assistants play an increasingly prominent role in our daily lives, this study explores the implications of their advice. We investigate the interplay between trust and virtual agents’ emotional expressions, delving into a critical aspect of human-technology interaction. Conducted through a comprehensive study comprising two interconnected phases, our research examines the dynamics between virtual agents and human decision-making. The first phase involves developing and validating a virtual robotic agent capable of conveying a spectrum of emotions. Through this, gender-based differences in emotional cue perception are disclosed, shedding light on how men and women interpret these cues differently. The second phase employs an interactive memory game, where the virtual agent operates in varied emotional states. Participants’ trust levels and perceptions are meticulously evaluated in different scenarios, ranging from accurate to erroneous agent cues. Our findings elucidate the impact of the agent’s emotional expressions on participants’ perceptions, illustrating how trust is intricately influenced by both the task at hand and the agent’s behavior. This research contributes to understanding the relationship between virtual assistants and human decision-making, emphasizing the necessity of designing more engaging and interactive virtual agents. These insights prepare future research for crafting more effective virtual assistants, fostering increased user trust and engagement.
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Wu, Danny T. Y., David Hanauer, Paul Murdock, V. G. Vinod Vydiswaran, Qiaozhu Mei, and Kai Zheng. "Developing a Semantically Based Query Recommendation for an Electronic Medical Record Search Engine: Query Log Analysis and Design Implications." JMIR Formative Research 7 (September 15, 2023): e45376. http://dx.doi.org/10.2196/45376.
Анотація:
Background An effective and scalable information retrieval (IR) system plays a crucial role in enabling clinicians and researchers to harness the valuable information present in electronic health records. In a previous study, we developed a prototype medical IR system, which incorporated a semantically based query recommendation (SBQR) feature. The system was evaluated empirically and demonstrated high perceived performance by end users. To delve deeper into the factors contributing to this perceived performance, we conducted a follow-up study using query log analysis. Objective One of the primary challenges faced in IR is that users often have limited knowledge regarding their specific information needs. Consequently, an IR system, particularly its user interface, needs to be thoughtfully designed to assist users through the iterative process of refining their queries as they encounter relevant documents during their search. To address these challenges, we incorporated “query recommendation” into our Electronic Medical Record Search Engine (EMERSE), drawing inspiration from the success of similar features in modern IR systems for general purposes. Methods The query log data analyzed in this study were collected during our previous experimental study, where we developed EMERSE with the SBQR feature. We implemented a logging mechanism to capture user query behaviors and the output of the IR system (retrieved documents). In this analysis, we compared the initial query entered by users with the query formulated with the assistance of the SBQR. By examining the results of this comparison, we could examine whether the use of SBQR helped in constructing improved queries that differed from the original ones. Results Our findings revealed that the first query entered without SBQR and the final query with SBQR assistance were highly similar (Jaccard similarity coefficient=0.77). This suggests that the perceived positive performance of the system was primarily attributed to the automatic query expansion facilitated by the SBQR rather than users manually manipulating their queries. In addition, through entropy analysis, we observed that search results converged in scenarios of moderate difficulty, and the degree of convergence correlated strongly with the perceived system performance. Conclusions The study demonstrated the potential contribution of the SBQR in shaping participants' positive perceptions of system performance, contingent upon the difficulty of the search scenario. Medical IR systems should therefore consider incorporating an SBQR as a user-controlled option or a semiautomated feature. Future work entails redesigning the experiment in a more controlled manner and conducting multisite studies to demonstrate the effectiveness of EMERSE with SBQR for patient cohort identification. By further exploring and validating these findings, we can enhance the usability and functionality of medical IR systems in real-world settings.
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Laniel, Sébastien, Dominic Létourneau, François Grondin, Mathieu Labbé, François Ferland, and François Michaud. "Toward enhancing the autonomy of a telepresence mobile robot for remote home care assistance." Paladyn, Journal of Behavioral Robotics 12, no. 1 (January 1, 2021): 214–37. http://dx.doi.org/10.1515/pjbr-2021-0016.
Анотація:
Abstract In health care, a telepresence robot could be used to have a clinician or a caregiver assist seniors in their homes, without having to travel to these locations. However, the usability of these platforms for such applications requires that they can navigate and interact with a certain level of autonomy. For instance, robots should be able to go to their charging station in case of low energy level or telecommunication failure. The remote operator could be assisted by the robot’s capabilities to navigate safely at home and to follow and track people with whom to interact. This requires the integration of autonomous decision-making capabilities on a platform equipped with appropriate sensing and action modalities, which are validated out in the laboratory and in real homes. To document and study these translational issues, this article presents such integration on a Beam telepresence platform using three open-source libraries for integrated robot control architecture, autonomous navigation and sound processing, developed with real-time, limited processing and robustness requirements, so that they can work in real-life settings. Validation of the resulting platform, named SAM, is presented based on the trials carried out in 10 homes. Observations made provide guidance on what to improve and will help identify interaction scenarios for the upcoming usability studies with seniors, clinicians and caregivers.
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Walenta, Kelvin, Simon Genser, and Selim Solmaz. "Bayesian Gaussian Mixture Models for Enhanced Radar Sensor Modeling: A Data-Driven Approach towards Sensor Simulation for ADAS/AD Development." Sensors 24, no. 7 (March 28, 2024): 2177. http://dx.doi.org/10.3390/s24072177.
Анотація:
In the realm of road safety and the evolution toward automated driving, Advanced Driver Assistance and Automated Driving (ADAS/AD) systems play a pivotal role. As the complexity of these systems grows, comprehensive testing becomes imperative, with virtual test environments becoming crucial, especially for handling diverse and challenging scenarios. Radar sensors are integral to ADAS/AD units and are known for their robust performance even in adverse conditions. However, accurately modeling the radar’s perception, particularly the radar cross-section (RCS), proves challenging. This paper adopts a data-driven approach, using Gaussian mixture models (GMMs) to model the radar’s perception for various vehicles and aspect angles. A Bayesian variational approach automatically infers model complexity. The model is expanded into a comprehensive radar sensor model based on object lists, incorporating occlusion effects and RCS-based detectability decisions. The model’s effectiveness is demonstrated through accurate reproduction of the RCS behavior and scatter point distribution. The full capabilities of the sensor model are demonstrated in different scenarios. The flexible and modular framework has proven apt for modeling specific aspects and allows for an easy model extension. Simultaneously, alongside model extension, more extensive validation is proposed to refine accuracy and broaden the model’s applicability.
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Canaza Ccari, Luis F., Ronald Adrian Ali, Erick Valdeiglesias Flores, Nicolás O. Medina Chilo, Erasmo Sulla Espinoza, Yuri Silva Vidal, and Lizardo Pari. "JVC-02 Teleoperated Robot: Design, Implementation, and Validation for Assistance in Real Explosive Ordnance Disposal Missions." Actuators 13, no. 7 (July 2, 2024): 254. http://dx.doi.org/10.3390/act13070254.
Анотація:
Explosive ordnance disposal (EOD) operations are hazardous due to the volatile and sensitive nature of these devices. EOD robots have improved these tasks, but their high cost limits accessibility for security institutions that do not have sufficient funds. This article presents the design, implementation, and validation of a low-cost EOD robot named JVC-02, specifically designed for use in explosive hazardous environments to safeguard the safety of police officers of the Explosives Disposal Unit (UDEX) of Arequipa, Peru. To achieve this goal, the essential requirements for this type of robot were compiled, referencing the capabilities of Rescue Robots from RoboCup. Additionally, the Quality Function Deployment (QFD) methodology was used to identify the needs and requirements of UDEX police officers. Based on this information, a modular approach to robot design was developed, utilizing commercial off-the-shelf components to facilitate maintenance and repair. The JVC-02 was integrated with a 5-DoF manipulator and a two-finger mechanical gripper to perform dexterity tasks, along with a tracked locomotion mechanism, which enables effective movement, and a three-camera vision system to facilitate exploration tasks. Finally, field tests were conducted in real scenarios to evaluate and experimentally validate the capabilities of the JVC-02 robot, assessing its mobility, dexterity, and exploration skills. Additionally, real EOD missions were carried out in which UDEX agents intervened and controlled the robot. The results demonstrate that the JVC-02 robot possesses strong capabilities for real EOD applications, excelling in intuitive operation, low cost, and ease of maintenance.
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Könnecke, Rainer, and Volker Schneider. "Vulnerable People in Microscopic Evacuation Modelling." Collective Dynamics 5 (August 12, 2020): A94. http://dx.doi.org/10.17815/cd.2020.94.
Анотація:
Computational evacuation modelling as a part of approval procedures or design processes is sometimes concerned with vulnerable people requiring special attention. This vulnerability can be based on external circumstances or on individual characteristics. Microscopic methods are well suited to deal with such specific determinants by their ability to model individual movement and certain behavioural aspects. By reference to case studies the possibilities of up-to-date individual evacuation models to cover egress scenarios including vulnerable people are discussed. The selected examples demonstrate that the evacuation of vulnerable people often depends more on the modelling of individual behaviour rather than on a very detailed description of individual characteristics. Group formation and the guidance or assistance of other people will have a strong impact on the evacuation process and thus require special modelling techniques and respective calibration and validation efforts guided by empirical studies.
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Yi, Qiang, Stanley Chien, Lingxi Li, Wensen Niu, Yaobin Chen, David Good, Chi-Chih Chen, and Rini Sherony. "Development of test scenarios and bicyclist surrogate for the evaluation of bicyclist automatic emergency braking systems." Journal of Intelligent and Connected Vehicles 1, no. 1 (February 5, 2018): 15–27. http://dx.doi.org/10.1108/jicv-02-2018-0005.
Анотація:
Purpose To support the standardized evaluation of bicyclist automatic emergency braking (AEB) systems, test scenarios, test procedures and test system hardware and software tools have been investigated and developed by the Transportation Active Safety Institute (TASI) at Indiana University-Purdue University Indianapolis. This paper aims to focus on the development of test scenarios and bicyclist surrogate for evaluating vehicle–bicyclist AEB systems. Design/methodology/approach The harmonized general estimates system (GES)/FARS 2010-2011 crash data and TASI 110-car naturalistic driving data (NDD) are used to determine the crash geometries and environmental factors of crash scenarios including lighting conditions, vehicle speeds, bicyclist speeds, etc. A surrogate bicyclist including a bicycle rider and a bicycle surrogate is designed to match the visual and radar characteristics of bicyclists in the USA. A bicycle target is designed with both leg pedaling and wheel rotation to produce proper micro-Doppler features and generate realistic motion for camera-based AEB systems. Findings Based on the analysis of the harmonized GES/FARS crash data, five crash scenarios are recommended for performance testing of bicyclist AEB systems. Combined with TASI 110-car naturalistic driving data, the crash environmental factors including lighting conditions, obscuring objects, vehicle speed and bicyclist speed are determined. The surrogate bicyclist was designed to represent the visual and radar characteristics of the real bicyclists in the USA. The height of the bicycle rider mannequin is 173 cm, representing the weighted height of 50th percentile US male and female adults. The size and shape of the surrogate bicycle were determined as 26-inch wheel and mountain/road bicycle frame, respectively. Both leg pedaling motion and wheel rotation are suggested to produce proper micro-Doppler features and support the camera-based AEB systems. Originality/value The results have demonstrated that the developed scenarios, test procedures and bicyclist surrogate will provide effective objective methods and necessary hardware and software tools for the evaluation and validation of bicyclist AEB systems. This is crucial for the development of advanced driver assistance systems.
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Stević, Stevan, Momčilo Krunić, Marko Dragojević, and Nives Kaprocki. "Development of ADAS perception applications in ROS and "Software-In-the-Loop" validation with CARLA simulator." Telfor Journal 12, no. 1 (2020): 40–45. http://dx.doi.org/10.5937/telfor2001040s.
Анотація:
Higher levels of autonomous driving are bringing sophisticated requirements and unpredicted challenges. In order to solve these problems, the set of functionalities in modern vehicles is growing in terms of algorithmic complexity and required hardware. The risk of testing implemented solutions in real world is high, expensive and time consuming. This is the reason for virtual automotive simulation tools for testing are heavily acclaimed. Original Equipment Manufacturers (OEMs) use these tools to create closed sense, compute, act loop to have realistic testing scenarios. Production software is tested against simulated sensing data. Based on these inputs a set of actions is produced and simulated which generates consequences that are evaluated. This creates a possibility for OEMs to minimize design errors and optimize costs of the vehicle production before any physical prototypes are produced. This paper presents the development of simple C++/Python perception applications that can be used in driver assistance functionalities. Using ROS as a prototyping platform these applications are validated and tested with "Software-In-the Loop" (SIL) method. CARLA simulator is used as a generator for input data and output commands of the autonomous platform are executed as simulated actions within simulator. Validation is done by connecting Autoware autonomous platform with CARLA simulator in order to test against various scenes in which applications are applicable. Vision based lane detection, which is one of the prototypes, is also tested in a real world scenario to demonstrate the applicability of algorithms developed with simulators to real-time processing
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Karle, Ujjwala. "Driving Safety through ADAS: An Indian Perspective." ARAI Journal of Mobility Technology 1, no. 1 (November 10, 2021): pp51–60. http://dx.doi.org/10.37285/ajmt.1.0.7.
Анотація:
Analysis of the National Motor Vehicle Crash Causation Survey, conducted by the National Highway Traffic Safety Administration (NHTSA), shows that driver error is a factor in 94% of crashes. Although it is important to remember multiple factors contribute to all crashes, the largest portion of driver error issues involve the driver failing to recognize hazards, including distraction. Around 3,700 people die in traffic every day around the world, and 100,000 are injured. The automotive industry is striving to make driving safer. ADAS in India is comparatively in a nascent stage. However, it is gradually gaining pace. The government's upcoming safety regulations and consumer awareness will give further impetus to this movement. So, Advanced driver-assistance systems (ADAS) is equipping cars and drivers with advance information and technology to make them become aware of the environment and handle potential situations in better way semi-autonomously. High-quality training and test data is essential in the development and validation of ADAS systems which lay the foundation for autonomous driving technology. In addition to this, ADAS systems need to be very safe and robust, with the ability to perform in a variety of driving scenarios, and be very secure, being immune from any external cyber-attacks. In order to make ADAS systems safer, the AV will be required to drive more than a billion miles on real roads, taking tens and sometimes hundreds of years to drive those miles, considering even the most aggressive testing assumptions. Every small update to the AV will require another billion miles of testing to be approved for real world use. Moreover, the more advanced the technology becomes, the more miles will need to de driven. Real word testing plays a very crucial role in ADAS and AV development and testing. Nevertheless, relying only on real world testing will significantly slow down the development and testing of such technologies. This is where simulation comes into play. With the primary objective of road safety improvement, ADAS functionalities will definitely play a big role for automotive industry. In order to tackle Indian specific road infrastructure conditions, and thus improving the safety, a complete tool-chain for developing, deploying and validating ADAS functionalities need to be developed. The presented work shares insights of each and every aspect of this tool-chain with experimental results and real world correlations.
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Ahmed, Asib, Al Artat Bin Ali, Maisha Mahboob, and Fayeeza Humaira. "Comparison between Local and Global Methods to Develop AQI in Representing the Spatial Pattern of Air Quality of Dhaka City." Dhaka University Journal of Earth and Environmental Sciences 11, no. 1 (February 1, 2023): 131–49. http://dx.doi.org/10.3329/dujees.v11i1.63716.
Анотація:
An Air Quality Index (AQI) is a means of evaluating air quality in respect to the relation between air quality and the entire environment of an area. The main objective of the study was to compare the development of AQI using global methods and local methods to identify which could represent the more significant pattern from a spatial perspective in Dhaka city (DNCC and DSCC). The pollutants used in this study are CO, NO2, SO2, O3, CH4, CO2, PM2.5 and PM10. For the global AQI, indexing was performed using the guidelines in ‘Technical Assistance Document for the Reporting of Daily Air Quality – the Air Quality Index’ by USEPA (United States Environmental Protection Agency). This method identified and mapped the most responsible pollutant that represents the pollution pattern of the area. For local AQI, an overlay method was developed where all the relevant pollutants were weighed according to their significance and summed; their weights calculated using a Principal Component Analysis (PCA). Component analysis could find a better-correlated variable to represent the distribution from all the variables, deeming it a good feature-selecting tool. The result from this study suggests that the local AQI better represents the air quality of the study corresponding to certain real scenarios. Mapping the pollution has helped in validating the spatial pattern of the pollutants. Such methods, which include all the variables influencing the atmospheric dynamics, could establish a better pattern for the local environment. The temporal background of the factors could be considered as situations may change over time. For air quality, this could be performed with any variables apart from atmospheric constituents and can be even replicated for any component of the environment, which may help in comprehending the local environmental quality. The study would be useful to devise a proper system to develop AQI considering all the spatial and temporal effects. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 11(1), 2022: 131-149
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Pieroni, Alessio, Claudio Lantieri, Hocine Imine, and Andrea Simone. "LIGHT VEHICLE MODEL FOR DYNAMIC CAR SIMULATOR." TRANSPORT 31, no. 2 (June 28, 2016): 242–49. http://dx.doi.org/10.3846/16484142.2016.1193051.
Анотація:
Driving simulators have been becoming little by little a suitable tool oriented to improve the knowledge about the domain of driving research. The investigations that can be conducted with this type of tool concern the driver’s behaviour, the design/control of vehicles, testing assistance systems for driving and the roadway infrastructure’s impact. The benefits of simulation studies are many: lack of any real risk to users, reproducible situations, time savings and reduced testing costs. In addition, their flexibility allows to test situations that do not exist in reality or at least they rarely and randomly exist. The topic of the present work concerns the development of a brand new dynamic model for an existing car simulator owned by LEPSIS laboratory (Laboratoire d’Expliotation, Perception, Simulateurs et Silulations – Laboratory for Road Operations, Perception, Simulators and Simulations) belonging to COSYS (COmposants et SYStems), which is a department of IFSTTAR institute (Institut Français des Sciences et Technologies des Transports, de l’Aménagement et des Réseaux – French Institute of Science and Technology for Transport, Spatial Planning, Development and Networks) site. Once uses and advantages of driving simulators are listed and described, imperfections and limitations of the existing driving vehicle model belonging to the two Degrees of Freedom (DoF) driving simulator of the laboratory are highlighted. Subsequently, structure of the brand new vehicle model, designed by means of Matlab Simulink software, are illustrated through the theoretical framework. Since the vehicle model must refer to a real one, an instrumented Peugeot 406 has been chosen because all its technical features are provided and inserted both on the present model and Prosper/Callas 4.9 by OKTAL software to create a highly sophisticated and accurate virtual version of the commercial car. The validation of this new vehicle model is performed, where the results returned by several different driving scenarios are compared with the ones provided by Prosper software. All the scenarios are simulated with both existing and new vehicle model uploaded in the driving simulator, and the outputs are subsequently compared with the ones returned by Prosper in order to demonstrate the improvements done. Finally, being the number of outputs provided by the new model definitively higher with respect to previous one, additional validations concerning the further results are accomplished.
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Qi, Chunyang, Hongxiang Zhao, Chuanxue Song, Naifu Zhang, Sinxin Song, Haigang Xu, and Feng Xiao. "Monocular Depth and Velocity Estimation Based on Multi-Cue Fusion." Machines 10, no. 5 (May 19, 2022): 396. http://dx.doi.org/10.3390/machines10050396.
Анотація:
Many consumers and scholars currently focus on driving assistance systems (DAS) and intelligent transportation technologies. The distance and speed measurement technology of the vehicle ahead is an important part of the DAS. Existing vehicle distance and speed estimation algorithms based on monocular cameras still have limitations, such as ignoring the relationship between the underlying features of vehicle speed and distance. A multi-cue fusion monocular velocity and ranging framework is proposed to improve the accuracy of monocular ranging and velocity measurement. We use the attention mechanism to fuse different feature information. The training method is used to jointly train the network through the distance velocity regression loss function and the depth loss as an auxiliary loss function. Finally, experimental validation is performed on the Tusimple dataset and the KITTI dataset. On the Tusimple dataset, the average speed mean square error of the proposed method is less than 0.496 m2/s2, and the average mean square error of the distance is 5.695 m2. On the KITTI dataset, the average velocity mean square error of our method is less than 0.40 m2/s2. In addition, we test in different scenarios and confirm the effectiveness of the network.
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Chi, X., H. Huang, J. Yang, J. Zhao, and X. Zhang. "DATASET AND IMPROVED YOLOV7 FOR TEXT-BASED TRAFFIC SIGN DETECTION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 13, 2023): 881–88. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-881-2023.
Анотація:
Abstract. Traffic sign detection is an important part of autonomous driving technology, and it is also important to have a large-scale dataset applicable to Chinese traffic scenarios. The article proposes a text-based self-labelled traffic sign dataset which consists of 3153 images, of which 2903 images are used for training and 250 images are used for validation. And an improved YOLOv7 algorithm is provided that incorporates the BiFormer attention mechanism into the YOLOv7 network to enhance its ability to detect small objects. This approach has the advantage of improved accuracy but may increase runtime. To mitigate this problem, the improved YOLOv7 network undergoes model pruning to compress the model size and increase its speed. Experimental results show that the improved YOLOv7 network in this paper improves the average accuracy by 2.9% while maintaining almost the same speed as the original network. After testing, the model has a real-time effect and practical significance. In conclusion, the text-based self-annotated dataset and the improved YOLOv7 network proposed in this paper have important reference values for text-based traffic sign recognition in automatic driving assistance systems.
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Butt, Muhammad Atif, Faisal Riaz, Shehzad Khalid, Samia Abid, Muhammad Asif Habib, Sarmad Shafique, and Kijun Han. "Micro-electromechanical system based optimized steering angle estimation mechanism for customized self-driving vehicles." Measurement and Control 54, no. 3-4 (March 2021): 429–38. http://dx.doi.org/10.1177/00202940211000076.
Анотація:
In an automated steering system of the self-driving vehicles, the steering wheel angle is measured by the absolute angular displacement sensors or relative angle sensors. However, these sensors either encompass global navigation satellite systems (GNSS)/gyroscope – Micro Electromechanical-Sensor (MEMS) based solutions or comprise of the complex gear-based mechanical structure which results in latency and additive bias in the accumulative steering angle assessment. To address these issues, we propose a novel steering angle assessment system based on enhanced gear mechanism along with the adapted rotation paradigm for the customized self-driving vehicles. Additionally, a digital signal processing system has been introduced to resolve the issues in the identification of absolute central and max-bounding steering wheels position in self-driving vehicles. In assistance with the proposed mechanism, an algorithm has also been proposed to optimize the computed steering angle to minimalize the effect of additive bias in the accuracy. The proposed mechanism has been installed in the customized self-driving testbed vehicle and rigor validation has been performed in the straight and curvy road scenarios. Finally, the comparison study has been carried out between the conventional relative sensor and the proposed mechanism to show the accuracy and effectiveness of the proposed mechanism in terms of error rate, stability, and deviation.
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KLUEGL, PETER, MARTIN TOEPFER, PHILIP-DANIEL BECK, GEORG FETTE, and FRANK PUPPE. "UIMA Ruta: Rapid development of rule-based information extraction applications." Natural Language Engineering 22, no. 1 (October 8, 2014): 1–40. http://dx.doi.org/10.1017/s1351324914000114.
Анотація:
AbstractRule-based information extraction is an important approach for processing the increasingly available amount of unstructured data. The manual creation of rule-based applications is a time-consuming and tedious task, which requires qualified knowledge engineers. The costs of this process can be reduced by providing a suitable rule language and extensive tooling support. This paper presents UIMA Ruta, a tool for rule-based information extraction and text processing applications. The system was designed with focus on rapid development. The rule language and its matching paradigm facilitate the quick specification of comprehensible extraction knowledge. They support a compact representation while still providing a high level of expressiveness. These advantages are supplemented by the development environment UIMA Ruta Workbench. It provides, in addition to extensive editing support, essential assistance for explanation of rule execution, introspection, automatic validation, and rule induction. UIMA Ruta is a useful tool for academia and industry due to its open source license. We compare UIMA Ruta to related rule-based systems especially concerning the compactness of the rule representation, the expressiveness, and the provided tooling support. The competitiveness of the runtime performance is shown in relation to a popular and freely-available system. A selection of case studies implemented with UIMA Ruta illustrates the usefulness of the system in real-world scenarios.
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Devianti, Wanda, and Mita Anggaryani. "Virtual Museum of Tsunami Project for Increasing Awareness of Disaster Risk Potential in Physics Class." Berkala Ilmiah Pendidikan Fisika 10, no. 3 (November 21, 2022): 271. http://dx.doi.org/10.20527/bipf.v10i3.13380.
Анотація:
Virtual Reality (VR) development is expected to answer the problem of learning loss in education, especially in disaster mitigation studies. Using VR with artificial environments of scenes and objects appearing to be real, various disaster scenarios were made possible to learn. When this computer-enhanced learning was introduced to high school students, it could reduce both limitations and challenges of real-world learning. Visena (Virtual Museum of Tsunami) is virtual reality-based learning media in the form of a virtual museum which helps students understand the concept of waves and increase awareness of disaster risk potential in earthquakes and tsunamis. This study aims to test the validity, effectiveness, and practicality of Visena when it is implemented in a classroom setting. Visena was developed during the study using a 4D model of define, design, develop, and disseminate. Visena learning media were classified as very valid according to the validation process results. Student’s response were categorized very good despite a limited trial of Visena in class. Some students showed good performance with significant results when using Visena. It is recommended to use a smartphone to reduce dizziness when using glasses, and the material should be adjusted to the characteristics of the students. Therefore, an introduction to learning material and teacher assistance is needed.
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Khan, Muneeb A., and Heemin Park. "Exploring Explainable Artificial Intelligence Techniques for Interpretable Neural Networks in Traffic Sign Recognition Systems." Electronics 13, no. 2 (January 10, 2024): 306. http://dx.doi.org/10.3390/electronics13020306.
Анотація:
Traffic Sign Recognition (TSR) plays a vital role in intelligent transportation systems (ITS) to improve road safety and optimize traffic management. While existing TSR models perform well in challenging scenarios, their lack of transparency and interpretability hinders reliability, trustworthiness, validation, and bias identification. To address this issue, we propose a Convolutional Neural Network (CNN)-based model for TSR and evaluate its performance on three benchmark datasets: German Traffic Sign Recognition Benchmark (GTSRB), Indian Traffic Sign Dataset (ITSD), and Belgian Traffic Sign Dataset (BTSD). The proposed model achieves an accuracy of 98.85% on GTSRB, 94.73% on ITSD, and 92.69% on BTSD, outperforming several state-of-the-art frameworks, such as VGG19, VGG16, ResNet50V2, MobileNetV2, DenseNet121, DenseNet201, NASNetMobile, and EfficientNet, while also providing faster training and response times. We further enhance our model by incorporating explainable AI (XAI) techniques, specifically, Local Interpretable Model-Agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (Grad-CAM), providing clear insights of the proposed model decision-making process. This integration allows the extension of our TSR model to various engineering domains, including autonomous vehicles, advanced driver assistance systems (ADAS), and smart traffic control systems. The practical implementation of our model ensures real-time, accurate recognition of traffic signs, thus optimizing traffic flow and minimizing accident risks.
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Miao, Xingyu, and Yongqi Ge. "Energy Management for Energy Harvesting-Based Embedded Systems: A Systematic Mapping Study." Journal of Electrical and Computer Engineering 2020 (October 30, 2020): 1–19. http://dx.doi.org/10.1155/2020/5801850.
Анотація:
Energy management for energy harvesting-based embedded systems (EHES) is an emerging field, which aims to collect renewable energy from the environment to power an embedded system. In this work, we use the systematic mapping method to study the relevant literature, with the objective of exploring and analysing the state of the art in energy management for EHES, as well as to provide assistance for subsequent literature reviews. To this end, we conducted extensive searches to find articles related to energy harvesting, embedded systems, energy consumption, and energy management. We searched for papers from January 2005 to July 2019 from three mainstream databases, ACM, IEEE Xplore, and Web of Science, and found more than 3000 papers about EHES. Finally, we selected 142 eligible papers. We have completed the system mapping research from five aspects, namely, (1) research type (validation research, evaluation research, solution proposal, philosophical paper, opinion, and experience), (2) research goals (application or theory), (3) application scenarios, (4) tools or methods, and (5) paper distribution, such as publication year and authors’ nationality. The results showed that the major research type of the EHES papers is validation research, accounting for 65%, which indicated research is still in the theoretical stage and many researchers focus on how to improve the efficiency of harvesting energy, develop a reasonable energy supply plan, and adapt EHES for real-world requirements. Furthermore, this work reviews the tools used for EHES. As the future development direction, it is indispensable to provide tools to EHES for research, testing, development, and so on. The results of our analysis provide significant contributions to understanding the existing knowledge and highlighting potential future research opportunities in the EHES field.
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Schöttle, Markus. "Verifying and Validating Driver Assistance Systems." ATZelectronics worldwide 14, no. 3 (March 2019): 14–15. http://dx.doi.org/10.1007/s38314-019-0029-1.
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Nazir, Umber, Noralfishah Sulaiman, and Sheikh Kamran Abid. "Rise of Digital Humanitarian Network (DHN) in Southeast Asia: Social Media Insights for Crisis Mapping in Disaster Risk Reduction (DRR)." International Journal of Safety and Security Engineering 11, no. 5 (October 31, 2021): 573–83. http://dx.doi.org/10.18280/ijsse.110509.
Анотація:
Digital technologies and big data speedily change humanitarian crisis response and transform the processes from traditional to digital. Digital Humanitarian Network (DHN) for disaster risk reduction (DRR) using crisis mapping of the vulnerable population is becoming increasingly common during any disaster response process. To get the information and provide in time support, the critical Source of data is social media. In Southeast Asia, Facebook is the most used social media platform. Communities often rely on social media to seek in time assistance and guidance. Emerging social media and networks are remarkably well-compatible with intelligent data-centric systems, which foster an effective disaster management plan under disaster scenarios. During previous disasters in Southeast Asia, it was believed to be the fastest response medium. However, validation is essential to obtain important data, and after years of research, there are still many undiscovered features of social media that can be used in emergencies. This paper aims to determine Southeast Asian countries' readiness to utilise social media for DRR activities and understand the criteria of DHN by integrating crisis mapping. A qualitative research design is applied to gain an insight into the humanitarian disaster network for disaster risk reduction. Data were collected through document analysis. I argue that digital humanitarians can offer a unique combination of speed and safe access while escaping some of the traditional constraints of the aid-media relationship. The study concluded that DHN provides a collaborative environment for the organizations to collaborate and act fast to assist.
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Buerkle, Achim, Harveen Matharu, Ali Al-Yacoub, Niels Lohse, Thomas Bamber, and Pedro Ferreira. "An adaptive human sensor framework for human–robot collaboration." International Journal of Advanced Manufacturing Technology 119, no. 1-2 (November 23, 2021): 1233–48. http://dx.doi.org/10.1007/s00170-021-08299-2.
Анотація:
AbstractManufacturing challenges are increasing the demands for more agile and dexterous means of production. At the same time, these systems aim to maintain or even increase productivity. The challenges risen from these developments can be tackled through human–robot collaboration (HRC). HRC requires effective task distribution according to each party’s distinctive strengths, which is envisioned to generate synergetic effects. To enable a seamless collaboration, the human and robot require a mutual awareness, which is challenging, due to the human and robot “speaking” different languages as in analogue and digital. This challenge can be addressed by equipping the robot with a model of the human. Despite a range of models being available, data-driven models of the human are still at an early stage. For this purpose, this paper proposes an adaptive human sensor framework, which incorporates objective, subjective, and physiological metrics, as well as associated machine learning. Thus, it is envisioned to adapt to the uniqueness and dynamic nature of human behavior. To test the framework, a validation experiment was performed, including 18 participants, which aims to predict perceived workload during two scenarios, namely a manual and an HRC assembly task. Perceived workloads are described to have a substantial impact on a human operator’s task performance. Throughout the experiment, physiological data from an electroencephalogram (EEG), an electrocardiogram (ECG), and respiration sensor was collected and interpreted. For subjective metrics, the standardized NASA Task Load Index was used. Objective metrics included task completion time and number of errors/assistance requests. Overall, the framework revealed a promising potential towards an adaptive behavior, which is ultimately envisioned to enable a more effective HRC.
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Ashfaq, Farzeen, Rania M. Ghoniem, N. Z. Jhanjhi, Navid Ali Khan, and Abeer D. Algarni. "Using Dual Attention BiLSTM to Predict Vehicle Lane Changing Maneuvers on Highway Dataset." Systems 11, no. 4 (April 14, 2023): 196. http://dx.doi.org/10.3390/systems11040196.
Анотація:
In this research, we address the problem of accurately predicting lane-change maneuvers on highways. Lane-change maneuvers are a critical aspect of highway safety and traffic flow, and the accurate prediction of these maneuvers can have significant implications for both. However, current methods for lane-change prediction are limited in their ability to handle naturalistic driving scenarios and often require large amounts of labeled data. Our proposed model uses a bidirectional long short-term memory (BiLSTM) network to analyze naturalistic vehicle trajectories recorded from multiple sensors on German highways. To handle the temporal aspect of vehicle behavior, we utilized a sliding window approach, considering both the preceding and following vehicles’ trajectories. To tackle class imbalances in the data, we introduced rolling mean computed weights. Our extensive feature engineering process resulted in a comprehensive feature set to train the model. The proposed model fills the gap in the state-of-the-art lane change prediction methods and can be applied in advanced driver assistance systems (ADAS) and autonomous driving systems. Our results show that the BiLSTM-based approach with the sliding window technique effectively predicts lane changes with 86% test accuracy and a test loss of 0.325 by considering the context of the input data in both the past and future. The F1 score of 0.52, precision of 0.41, recall of 0.75, accuracy of 0.86, and AUC of 0.81 also demonstrate the model’s high ability to distinguish between the two target classes. Furthermore, the model achieved an accuracy of 83.65% with a loss value of 0.3306 on the other half of the data samples, and the validation accuracy was observed to improve over these epochs, reaching the highest validation accuracy of 92.53%. The F1 score of 0.51, precision of 0.36, recall of 0.89, accuracy of 0.82, and AUC of 0.85 on this data sample also demonstrate the model’s strong ability to identify both positive and negative classes. Overall, our proposed approach outperforms existing methods and can significantly contribute to improving highway safety and traffic flow.
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Bieck, Richard, Katharina Heuermann, Martin Sorge, Thomas Neumuth, and Markus Pirlich. "Saliency-assisted multi-label classification for explainable deep learning applications in endoscopic ENT navigation." Current Directions in Biomedical Engineering 8, no. 2 (August 1, 2022): 596–99. http://dx.doi.org/10.1515/cdbme-2022-1152.
Анотація:
Abstract Introduction: In endoscopic procedures of the nasal sinuses, a critical issue for classification tasks is the ambiguous anatomical representations due to their complex composition. We investigated the potential of multi-label image-based classifications of sinus landmark combinations together with explainability methods for machine learning in an assistance function at the application level. By combining image classification and pixel attribution in a navigation function, we provide the surgeon with label predictions and additional localization cues of important pixels relevant to the model output with regard to the input image. Methods: We used 3500 label annotated video sequences from 30 recorded sinus surgeries to fine-tune a pretrained ResNet50 as the feature extractor and a classification head using binary cross-entropy on one-hot encoded target vectors of landmark classes with a RAdam optimizer over 28-32 epochs. Image augmentation and a focal loss function were added to counter over-fitting. An explainability function used the trained model to produce pixel attribution maps for predicted classes regarding individual input images. These gradient maps were summed over all classes, and pixel values above 0.20 were clustered using weighted k-means based on the gradient value of each pixel coordinate. The resulting cluster centroids were then overlapped into the endoscopic image with the predicted landmark classes. Three surgeons investigated three different overlay scenarios in a validation study. Results: The top-1 predictions reached a mean f1-score of 0.47, with the highest values of 0.71 and the lowest with 0.28. Despite over-fitting mitigation, prediction results largely depended on over- and underrepresented classes. Conclusion: The provided explainability function at the application level showed the strong potential of delivering visual cues from prediction results to surgeons at runtime to support human-machine interaction.
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Elsherbiny, Osama, Ahmed Elaraby, Mohammad Alahmadi, Mosab Hamdan, and Jianmin Gao. "Rapid Grapevine Health Diagnosis Based on Digital Imaging and Deep Learning." Plants 13, no. 1 (January 3, 2024): 135. http://dx.doi.org/10.3390/plants13010135.
Анотація:
Deep learning plays a vital role in precise grapevine disease detection, yet practical applications for farmer assistance are scarce despite promising results. The objective of this research is to develop an intelligent approach, supported by user-friendly, open-source software named AI GrapeCare (Version 1, created by Osama Elsherbiny). This approach utilizes RGB imagery and hybrid deep networks for the detection and prevention of grapevine diseases. Exploring the optimal deep learning architecture involved combining convolutional neural networks (CNNs), long short-term memory (LSTM), deep neural networks (DNNs), and transfer learning networks (including VGG16, VGG19, ResNet50, and ResNet101V2). A gray level co-occurrence matrix (GLCM) was employed to measure the textural characteristics. The plant disease detection platform (PDD) created a dataset of real-life grape leaf images from vineyards to improve plant disease identification. A data augmentation technique was applied to address the issue of limited images. Subsequently, the augmented dataset was used to train the models and enhance their capability to accurately identify and classify plant diseases in real-world scenarios. The analyzed outcomes indicated that the combined CNNRGB-LSTMGLCM deep network, based on the VGG16 pretrained network and data augmentation, outperformed the separate deep network and nonaugmented version features. Its validation accuracy, classification precision, recall, and F-measure are all 96.6%, with a 93.4% intersection over union and a loss of 0.123. Furthermore, the software developed through the proposed approach holds great promise as a rapid tool for diagnosing grapevine diseases in less than one minute. The framework of the study shows potential for future expansion to include various types of trees. This capability can assist farmers in early detection of tree diseases, enabling them to implement preventive measures.
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Buyer, Julia, Alexander Oeser, Nora Grieb, Andreas Dietz, Thomas Neumuth, and Matthaeus Stoehr. "Decision Support for Oropharyngeal Cancer Patients Based on Data-Driven Similarity Metrics for Medical Case Comparison." Diagnostics 12, no. 4 (April 15, 2022): 999. http://dx.doi.org/10.3390/diagnostics12040999.
Анотація:
Making complex medical decisions is becoming an increasingly challenging task due to the growing amount of available evidence to consider and the higher demand for personalized treatment and patient care. IT systems for the provision of clinical decision support (CDS) can provide sustainable relief if decisions are automatically evaluated and processed. In this paper, we propose an approach for quantifying similarity between new and previously recorded medical cases to enable significant knowledge transfer for reasoning tasks on a patient-level. Methodologically, 102 medical cases with oropharyngeal carcinoma were analyzed retrospectively. Based on independent disease characteristics, patient-specific data vectors including relevant information entities for primary and adjuvant treatment decisions were created. Utilizing the ϕK correlation coefficient as the methodological foundation of our approach, we were able to determine the predictive impact of each characteristic, thus enabling significant reduction of the feature space to allow for further analysis of the intra-variable distances between the respective feature states. The results revealed a significant feature-space reduction from initially 19 down to only 6 diagnostic variables (ϕK correlation coefficient ≥ 0.3, ϕK significance test ≥ 2.5) for the primary and 7 variables (from initially 14) for the adjuvant treatment setting. Further investigation on the resulting characteristics showed a non-linear behavior in relation to the corresponding distances on intra-variable level. Through the implementation of a 10-fold cross-validation procedure, we were further able to identify 8 (primary treatment) matching cases with an evaluation score of 1.0 and 9 (adjuvant treatment) matching cases with an evaluation score of 0.957 based on their shared treatment procedure as the endpoint for similarity definition. Based on those promising results, we conclude that our proposed method for using data-driven similarity measures for application in medical decision-making is able to offer valuable assistance for physicians. Furthermore, we consider our approach as universal in regard to other clinical use-cases, which would allow for an easy-to-implement adaptation for a range of further medical decision-making scenarios.
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Dzida, W., and R. Freitag. "Making use of scenarios for validating analysis and design." IEEE Transactions on Software Engineering 24, no. 12 (1998): 1182–96. http://dx.doi.org/10.1109/32.738346.
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Cablé, Baptiste, Sophie Loriette, and Jean-Marc Nigro. "Using simple scenarios for wheelchair driving assistance." Expert Systems 30, no. 3 (August 9, 2012): 255–69. http://dx.doi.org/10.1111/j.1468-0394.2012.00636.x.
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Srdjevic, Bojan, Zorica Srdjevic, and Milena Lakicevic. "Validating the importance of criteria for assessing climate change scenarios." Journal of Water and Climate Change 9, no. 3 (May 7, 2018): 570–83. http://dx.doi.org/10.2166/wcc.2018.157.
Анотація:
Abstract While evaluating climate impacts within different climate change scenarios, analysts and stakeholders may have different goals and therefore it is usually difficult to define a common decision-making framework applicable for various practical uses. In this paper, we combine two different group decision-making methodologies to prioritise criteria for assessing output information from regional climate models. The first is based on use of a multi-criteria analytic hierarchy process (AHP) to determine weights of criteria as cardinal information about their importance. The second methodology uses two voting methods, namely Borda count and Approval voting, to generate ordinal information (ranks) for criteria. A set of five criteria is assessed by 16 PhD students from the field of climatology, and generated decisions about their importance in the validation of regional climate models' quality are summarised, compared, and critically discussed. The paper is closed with recommendations for further research.
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King, Jayde, John Kleber, Ashlee Harris, Barbara Chaparro, and Beth Blickensderfer. "Preflight Weather Decision Support Tool (PWDST): User-Centered Design Process and Usability Validation." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 1915–19. http://dx.doi.org/10.1177/1071181319631435.
Анотація:
General Aviation flight operations have been negatively affected by the slow decreasing weather related accident rate for the last 20 years. Upon further investigation, research suggests, that poor preflight planning and a lack of aviation weather experience and knowledge may be contributing factors to the stagnant weather related accident rate. Our team developed a Preflight Weather Decision Support Tool (PWDST) to help novice pilots access, interpret, and apply weather information. We used a user-centered design process which involved an initial task analysis, low-fidelity prototyping, low-fidelity usability testing, user interviews and expert review. This study assessed and compared the perceived usability, difficulty, and the system assistance satisfaction of the PWDST. Participants (n=9) completed a usability study and a series of surveys during, as well as, after the completion of the preflight planning scenario. A series of Mann-Whitney U Tests were conducted to compare the difference between Private Pilot and Certified Flight Instructors (CFI) perceived usability, difficulty, and system assistance satisfaction ratings. Results indicated, there were no significant differences between group ratings. Overall, both groups reported above average usability, system assistance and low difficulty rating for the PWDST. Future research and possible implications are discussed.
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Gao, Ruiqin, Jin Liu, Robert Johnson, Jiandong Wang, and Ling Hu. "Validating an ethical decision-making model of assessment using authentic scenarios." Studies in Educational Evaluation 62 (September 2019): 187–96. http://dx.doi.org/10.1016/j.stueduc.2019.05.003.