Journal articles: 'Autonomous Driving Systems'

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Walch, Marcel, Kristin Mühl, Martin Baumann, and Michael Weber. "Autonomous Driving." International Journal of Mobile Human Computer Interaction 9, no. 2 (April 2017): 58–74. http://dx.doi.org/10.4018/ijmhci.2017040104.

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Autonomous vehicles will need de-escalation strategies to compensate when reaching system limitations. Car-driver handovers can be considered one possible method to deal with system boundaries. The authors suggest a bimodal (auditory and visual) handover assistant based on user preferences and design principles for automated systems. They conducted a driving simulator study with 30 participants to investigate the take-over performance of drivers. In particular, the authors examined the effect of different warning conditions (take-over request only with 4 and 6 seconds time budget vs. an additional pre-cue, which states why the take-over request will follow) in different hazardous situations. Their results indicated that all warning conditions were feasible in all situations, although the short time budget (4 seconds) was rather challenging and led to a less safe performance. An alert ahead of a take-over request had the positive effect that the participants took over and intervened earlier in relation to the appearance of the take-over request. Overall, the authors' evaluation showed that bimodal warnings composed of textual and iconographic visual displays accompanied by alerting jingles and spoken messages are a promising approach to alert drivers and to ask them to take over.

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Yaakub, Salma, and Mohammed Hayyan Alsibai. "A Review on Autonomous Driving Systems." International Journal of Engineering Technology and Sciences 5, no. 1 (June 20, 2018): 1–16. http://dx.doi.org/10.15282/ijets.v5i1.2800.

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Autonomous vehicles are one of the promising solutions to reduce traffic crashes and improve mobility and traffic system. An autonomous vehicle is preferable because it helps in reducing the need for redesigning the infrastructure and because it improves the vehicle power efficiency in terms of cost and time taken to reach the destination. Autonomous vehicles can be divided into 3 types: Aerial vehicles, ground vehicles and underwater vehicles. General, four basic subsystems are integrated to enable a vehicle to move by itself which are: Position identifying and navigation system, surrounding environment situation analysis system, motion planning system and trajectory control system. In this paper, a review on autonomous vehicles and their related technological applications is presented to highlight the aspects of this industry as a part of industry 4.0 concept. Moreover, the paper discusses the best autonomous driving systems to be applied on our wheelchair project which aims at converting a manual wheelchair to a smart electric wheelchair

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Henschke, Adam. "Trust and resilient autonomous driving systems." Ethics and Information Technology 22, no. 1 (November 19, 2019): 81–92. http://dx.doi.org/10.1007/s10676-019-09517-y.

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V S, Amar. "Autonomous Driving using CNN." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 3633–36. http://dx.doi.org/10.22214/ijraset.2021.35771.

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Human beings are currently addicted to automation and robotics technologies. The state-of-the-art in deep learning technologies and AI is the subject of this autonomous driving. Driving with automated driving systems promises to be safe, enjoyable, and efficient.. It is preferable to train in a virtual environment first and then move to a real-world one. Its goal is to enable a vehicle to recognise its surroundings and navigate without the need for human intervention. The raw pixels from a single front-facing camera were directly transferred to driving commands using a convolution neural network (CNN). This end-to-end strategy proved to be remarkably effective, The system automatically learns internal representations of the essential processing stages such as detecting useful road components using only the human steering angle as the training signal. We never expressly taught it to recognise the contour of roadways, for example. In comparison to explicit issue decomposition, such as lane marking detection, Our end-to-end solution optimises all processing processes at the same time, including path planning and control. We believe that this will lead to improved performance and smaller systems in the long run. Internal components will self-optimize to maximise overall system performance, resulting in improved performance.

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Lee, Heung-Gu, Dong-Hyun Kang, and Deok-Hwan Kim. "Human–Machine Interaction in Driving Assistant Systems for Semi-Autonomous Driving Vehicles." Electronics 10, no. 19 (October 1, 2021): 2405. http://dx.doi.org/10.3390/electronics10192405.

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Currently, the existing vehicle-centric semi-autonomous driving modules do not consider the driver’s situation and emotions. In an autonomous driving environment, when changing to manual driving, human–machine interface and advanced driver assistance systems (ADAS) are essential to assist vehicle driving. This study proposes a human–machine interface that considers the driver’s situation and emotions to enhance the ADAS. A 1D convolutional neural network model based on multimodal bio-signals is used and applied to control semi-autonomous vehicles. The possibility of semi-autonomous driving is confirmed by classifying four driving scenarios and controlling the speed of the vehicle. In the experiment, by using a driving simulator and hardware-in-the-loop simulation equipment, we confirm that the response speed of the driving assistance system is 351.75 ms and the system recognizes four scenarios and eight emotions through bio-signal data.

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LIM, Kyung-Il. "Fifth-Generation Technology in Autonomous Driving Systems." Physics and High Technology 29, no. 3 (March 31, 2020): 21–26. http://dx.doi.org/10.3938/phit.29.009.

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Blasinski, Henryk, Joyce Farrell, Trisha Lian, Zhenyi Liu, and Brian Wandell. "Optimizing Image Acquisition Systems for Autonomous Driving." Electronic Imaging 2018, no. 5 (January 28, 2018): 161–1. http://dx.doi.org/10.2352/issn.2470-1173.2018.05.pmii-161.

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Bhat, Anand, Shunsuke Aoki, and Ragunathan Rajkumar. "Tools and Methodologies for Autonomous Driving Systems." Proceedings of the IEEE 106, no. 9 (September 2018): 1700–1716. http://dx.doi.org/10.1109/jproc.2018.2841339.

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Vitas, Dijana, Martina Tomic, and Matko Burul. "Traffic Light Detection in Autonomous Driving Systems." IEEE Consumer Electronics Magazine 9, no. 4 (July 1, 2020): 90–96. http://dx.doi.org/10.1109/mce.2020.2969156.

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Baber, J., J. Kolodko, T. Noel, M. Parent, and L. Vlacic. "Cooperative autonomous driving - Intelligent vehicles sharing city roads cooperative autonomous driving." IEEE Robotics & Automation Magazine 12, no. 1 (March 2005): 44–49. http://dx.doi.org/10.1109/mra.2005.1411418.

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Nine, Julkar. "Towards Autonomous Driving Using Vision Based Intelligent Systems." Embedded Selforganising Systems 8, no. 2 (December 21, 2021): 1–2. http://dx.doi.org/10.14464/ess.v8i2.496.

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Vision Based systems have become an integral part when it comes to autonomous driving. The autonomous industry has seen a made large progress in the perception of environment as a result of the improvements done towards vision based systems. As the industry moves up the ladder of automation, safety features are coming more and more into the focus. Different safety measurements have to be taken into consideration based on different driving situations. One of the major concerns of the highest level of autonomy is to obtain the ability of understanding both internal and external situations. Most of the research made on vision based systems are focused on image processing and artificial intelligence systems like machine learning and deep learning. Due to the current generation of technology being the generation of “Connected World”, there is no lack of data any more. As a result of the introduction of internet of things, most of these connected devices are able to share and transfer data. Vision based techniques are techniques that are hugely depended on these vision based data.

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Minnerup, Pascal, David Lenz, Tobias Kessler, and Alois Knoll. "Debugging Autonomous Driving Systems Using Serialized Software Components." IFAC-PapersOnLine 49, no. 15 (2016): 44–49. http://dx.doi.org/10.1016/j.ifacol.2016.07.612.

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Oliveira, Luis, Karl Proctor, Christopher G. Burns, and Stewart Birrell. "Driving Style: How Should an Automated Vehicle Behave?" Information 10, no. 6 (June 25, 2019): 219. http://dx.doi.org/10.3390/info10060219.

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This article reports on a study to investigate how the driving behaviour of autonomous vehicles influences trust and acceptance. Two different designs were presented to two groups of participants (n = 22/21), using actual autonomously driving vehicles. The first was a vehicle programmed to drive similarly to a human, “peeking” when approaching road junctions as if it was looking before proceeding. The second design had a vehicle programmed to convey the impression that it was communicating with other vehicles and infrastructure and “knew” if the junction was clear so could proceed without ever stopping or slowing down. Results showed non-significant differences in trust between the two vehicle behaviours. However, there were significant increases in trust scores overall for both designs as the trials progressed. Post-interaction interviews indicated that there were pros and cons for both driving styles, and participants suggested which aspects of the driving styles could be improved. This paper presents user information recommendations for the design and programming of driving systems for autonomous vehicles, with the aim of improving their users’ trust and acceptance.

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Li, Yuze. "A Review of Research on Deep Learning-based Target Detection Technology for Automated Vehicle Driving Systems." Highlights in Science, Engineering and Technology 27 (December 27, 2022): 19–24. http://dx.doi.org/10.54097/hset.v27i.3716.

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In recent years, with the rapid development of artificial intelligence technology, major technology companies around the world have strategically shifted their attention to the field of autonomous driving, momentarily pushing the research on autonomous driving to a climax. Target detection is one of the core technologies in the field of autonomous driving. For this reason, this paper provides a research review on driverless technology, deep learning target detection algorithms, and briefly summarizes the difficulties faced by autonomous driving target detection, and then introduces five common current target detection algorithms.

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Hwang, Kitae, In Hwan Jung, and Jae Moon Lee. "Implementation of Autonomous Driving on RC-CAR with Raspberry PI and AI Server." Webology 19, no. 1 (January 20, 2022): 4444–58. http://dx.doi.org/10.14704/web/v19i1/web19293.

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A lot of research is being done on autonomous driving vehicles or robots that recognize objects and drive themselves without human intervention. In order to develop autonomous driving technology, there is a fundamental difficulty in securing expensive real cars equipped with various sensors. In this paper, an autonomous driving system development platform was developed using an inexpensive RC-Car, and a test system that can test various algorithms related to autonomous driving was introduced. In the system developed in this study, the single board computer Raspberry PI was mounted on the RC-Car to control the car, and the autonomous driving-related algorithms were implemented in a separate AI server, and they communicated with the message-based ROS protocol. In addition, those who want to develop an autonomous driving system can easily attach desired sensors to the RC-Car, increasing scalability. In this paper, almost all algorithms related to autonomous driving have been implemented. A simple autonomous driving RC-Car system was actually implemented and operation was verified by designing and implementing algorithms such as lane recognition, driving along the lane, obstacle detection and stopping, traffic light recognition, driving between smooth and sharp curves, and autonomous parking. In sharp curves, the angle of the lane was tracked in a short period to prevent the vehicle from crossing the lane. In addition, we developed an Android app that can manually control the car and monitor the video from the camera in time. This study presented and solved various difficulties that could not be known by developing an autonomous driving algorithm using simulators.

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Panarin, Oleg, and Igor Zacharov. "Monitoring Mobile Information Processing Systems." Russian Digital Libraries Journal 23, no. 4 (May 28, 2020): 835–47. http://dx.doi.org/10.26907/1562-5419-2020-23-4-835-847.

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We describe the implementation of the monitoring for the IT systems at the core of the autonomous driving vehicle. The role of the monitoring is to assist in decision to start the driving cycle and continuous assessment for the fitness to drive the vehicle. The requirements for the monitoring system with the increased resiliency and data replication make it sufficiently different from standard monitoring systems and warrant a unique implementation tuned for the autonomous driving requirements. The monitoring system combines the OS events and real-time measurements of sensor data. The information is stored in flat files for emergency access as well as in a Time Series Data Base (TSDB).

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Lee, Joey, Benedikt Groß, and Raphael Reimann. "Who wants to be a self-driving car?" Information Design Journal 25, no. 1 (December 31, 2019): 21–27. http://dx.doi.org/10.1075/idj.25.1.02lee.

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Abstract Self-driving cars and autonomous transportation systems are projected to create radical societal changes, yet public understanding and trust of self-driving cars and autonomous systems is limited. The authors present a new mixed-reality experience designed to provide its users with insights into the ways that self-driving cars operate. A single-person vehicle equipped with sensors provides its users with data driven visual feedback in a virtual reality headset to navigate in physical space. The authors explore how immersive experiences might provide ‘conceptual affordances’ that lower the entry barrier for diverse audiences to discuss complex topics.

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Vadaszffy, Karl. "Systems Specialists." Electric and Hybrid Vehicle Technology International 2018, no. 1 (July 2018): 155–56. http://dx.doi.org/10.12968/s1467-5560(22)60338-5.

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Li, Youwei, and Jian Qu. "MFPE: A Loss Function based on Multi-task Autonomous Driving." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 16, no. 4 (October 1, 2022): 393–409. http://dx.doi.org/10.37936/ecti-cit.2022164.248304.

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Road tracking, traffic sign recognition, obstacle avoidance, and real-time acceleration and deceleration are some critical sub-tasks in autonomous driving. This research proposed to use a single-sensor (camera) based intelligent driving platform to achieve multi-task (four subtasks) autonomous driving. We adjusted the function combinations and hyperparameters of the model to improve the model training and model testing performance. The experiments showed that the existing function combinations could not significantly improve the autonomous driving performance, and the loss function had a significant impact on the autonomous driving performance of the model. Therefore, we designed a novel loss function (MFPE) based on multi-task autonomous driving. The models with the MFPE loss function outperformed the original and existing models in model training and actual multi-task autonomous driving performance. Meanwhile, the model with the MFPE loss function achieved multi-task autonomous driving under different lighting conditions, untrained routes, and different static obstacles, which indicates that the MFPE loss function enhances the robustness of the model. In addition, the speed of the intelligent driving platform can reach up to 5.4 Km/h.

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Li, W., C. W. Pan, R. Zhang, J. P. Ren, Y. X. Ma, J. Fang, F. L. Yan, et al. "AADS: Augmented autonomous driving simulation using data-driven algorithms." Science Robotics 4, no. 28 (March 27, 2019): eaaw0863. http://dx.doi.org/10.1126/scirobotics.aaw0863.

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Simulation systems have become essential to the development and validation of autonomous driving (AD) technologies. The prevailing state-of-the-art approach for simulation uses game engines or high-fidelity computer graphics (CG) models to create driving scenarios. However, creating CG models and vehicle movements (the assets for simulation) remain manual tasks that can be costly and time consuming. In addition, CG images still lack the richness and authenticity of real-world images, and using CG images for training leads to degraded performance. Here, we present our augmented autonomous driving simulation (AADS). Our formulation augmented real-world pictures with a simulated traffic flow to create photorealistic simulation images and renderings. More specifically, we used LiDAR and cameras to scan street scenes. From the acquired trajectory data, we generated plausible traffic flows for cars and pedestrians and composed them into the background. The composite images could be resynthesized with different viewpoints and sensor models (camera or LiDAR). The resulting images are photorealistic, fully annotated, and ready for training and testing of AD systems from perception to planning. We explain our system design and validate our algorithms with a number of AD tasks from detection to segmentation and predictions. Compared with traditional approaches, our method offers scalability and realism. Scalability is particularly important for AD simulations, and we believe that real-world complexity and diversity cannot be realistically captured in a virtual environment. Our augmented approach combines the flexibility of a virtual environment (e.g., vehicle movements) with the richness of the real world to allow effective simulation.

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Cho, Eunae, and Yoonhyuk Jung. "Consumers’ understanding of autonomous driving." Information Technology & People 31, no. 5 (October 1, 2018): 1035–46. http://dx.doi.org/10.1108/itp-10-2017-0338.

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Purpose The purpose of this paper is to explore consumers’ understanding of autonomous driving by comparing perceptions of occasional drivers (ODs) and frequent drivers (FDs). Design/methodology/approach Data were gathered through semi-structured interviews with 41 drivers. Their responses were categorized into thematic categories or topics on the basis of content analysis, and the topics were structured based on the core-periphery model. Finally, the authors visualized the structure on a perceptual map by adopting a maximum tree approach. Findings Respondents’ understanding of autonomous driving were categorized into 10 topics. There were significant differences in topics and their relationships between ODs and FDs. Findings also show that FD can better detect hazardousness from autonomous driving environments than ODs. Research limitations/implications Differently from prior studies’ focus on its technological aspect and some derived benefits, the study examines it from the viewpoint of consumers, who are critical participants in the dissemination of autonomous driving. Practical implications The findings suggest that rather than focusing on developing the highest level of autonomous cars, developing in an evolutionary way by adding automated functions to existing cars can be the better strategy to dominate the autonomous vehicle market. Originality/value This study is a pioneering work in that it can be an initial empirical work on autonomous driving from the customer standpoint.

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Liu, Jundi, and Linda Ng Boyle. "Analysis of Driver Behavior in Mixed Autonomous and Non-autonomous Traffic Flows." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66, no. 1 (September 2022): 1447–51. http://dx.doi.org/10.1177/1071181322661305.

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Autonomous vehicles are expected to improve road safety and efficiency in future transportation systems. A driving simulator study was designed to identify driving styles and the cooperation between human drivers and other AVs. The study captured driver’s following behavior in a fully autonomous driving environment at unsignalized intersections. Participants were asked to make a series of maneuvers (straight through intersection, left turn, and right turn) in two different speed conditions (30, 40 mph) and two different traffic density conditions (with or without other traffic). Analysis of Variance showed that drivers had a significantly larger deviation (defined as the area between two trajectories) during left turn maneuvers when they were traveling at higher speeds. Moreover, the first turning operation had smaller deviation than the second turning operation. The findings have implications for the design of driver-assistance guidance systems in future mixed autonomous and non-autonomous traffic flows.

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Bai, Xinyan, Hongbing Chen, and Xintong Jiang. "Research on Market Prospect of High-Level Automatic Driving." BCP Business & Management 27 (September 6, 2022): 130–41. http://dx.doi.org/10.54691/bcpbm.v27i.1958.

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Autonomous driving is considered the future application in the world, but its market is still unknown. This article first examines the economic context in which autonomous driving occurs and uses statistical data to find that most of the population is receptive to autonomous driving as new and unconventional technology. This is good for the autonomous driving market. However, the survey indicates that there are still some threats to autonomous driving systems. Firstly, there are legal restrictions, and secondly, there are bottlenecks in path planning technology. It is also worth mentioning that the Tesla brake failure in 2021 has dealt a double blow to the autonomous driving industry, both socially and technologically. At the same time, the article analyses the consumption patterns of the autonomous driving industry and finds that autonomous driving is a promising industry. Still, to have a good market, it first needs to improve the technology and ensure that the data is accurate.

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R. Sushma and J. Satheesh Kumar. "Dynamic Vehicle Modelling and Controlling Techniques for Autonomous Vehicle Systems." December 2022 4, no. 4 (January 9, 2023): 307–15. http://dx.doi.org/10.36548/jeea.2022.4.007.

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The driving scenario of an automated vehicle is the crucial technology in the design of autonomous cars. This suggested approach aims to address the shortcomings of autonomous cars, such as their poor real- time performance and low control precision. The process for building a virtual simulation environment for autonomous vehicle testing and validation is described in this study. Model Predictive Control and Proportional Integral and Derivative Control are used in MATLAB simulation to build three car models. These are related to the 2D and 3D animation used in collision detection and visualization. The virtual engine visualization is included throughout the model. A variety of test circumstances are used to validate the simulation model, and the model’s performance is assessed in the presence of various barriers. The simulation's findings demonstrate that the autonomous vehicle has a strong potential for self-adaptation even in challenging and complex working environments. No instances of car sideslip or track departure have been noted. It is discovered that this autonomous car performs remarkably well overall when compared to other autonomous vehicles. The suggested approach is essential for enhancing autonomous vehicle driving safety, maintaining vehicle control in challenging situations, and improving the advancement of intelligent vehicle driving assistance.

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Devi, T. Kirthiga, Akshat Srivatsava, Kritesh Kumar Mudgal, Ranjnish Raj Jayanti, and T. Karthick. "Behaviour Cloning for Autonomous Driving." Webology 17, no. 2 (December 21, 2020): 694–705. http://dx.doi.org/10.14704/web/v17i2/web17061.

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The objective of this project is to automate the process of driving a car. The result of this project will surely reduce the number of hazards happening everyday. Our world is in progress and self driving car is on its way to reach consumer‟s door-step but the big question still lies that will people accept such a car which is fully automated and driverless. The idea is to create an autonomous Vehicle that uses only some sensors (collision detectors, temperature detectors etc.) and camera module to travel between destinations with minimal/no human intervention. The car will be using a trained Convolutional Neural Network (CNN) which would control the parameters that are required for smoothly driving a car. They are directly connected to the main steering mechanism and the output of the deep learning model will control the steering angle of the vehicle. Many algorithms like Lane Detection, Object Detection are used in tandem to provide the necessary functionalities in the car.

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Endsley, Mica R. "Autonomous Driving Systems: A Preliminary Naturalistic Study of the Tesla Model S." Journal of Cognitive Engineering and Decision Making 11, no. 3 (February 1, 2017): 225–38. http://dx.doi.org/10.1177/1555343417695197.

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Autonomous and semiautonomous vehicles are currently being developed by over14 companies. These vehicles may improve driving safety and convenience, or they may create new challenges for drivers, particularly with regard to situation awareness (SA) and autonomy interaction. I conducted a naturalistic driving study on the autonomy features in the Tesla Model S, recording my experiences over a 6-month period, including assessments of SA and problems with the autonomy. This preliminary analysis provides insights into the challenges that drivers may face in dealing with new autonomous automobiles in realistic driving conditions, and it extends previous research on human-autonomy interaction to the driving domain. Issues were found with driver training, mental model development, mode confusion, unexpected mode interactions, SA, and susceptibility to distraction. New insights into challenges with semiautonomous driving systems include increased variability in SA, the replacement of continuous control with serial discrete control, and the need for more complex decisions. Issues that deserve consideration in future research and a set of guidelines for driver interfaces of autonomous systems are presented and used to create recommendations for improving driver SA when interacting with autonomous vehicles.

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Yu, Dongyeon, Chanho Park, Hoseung Choi, Donggyu Kim, and Sung-Ho Hwang. "Takeover Safety Analysis with Driver Monitoring Systems and Driver–Vehicle Interfaces in Highly Automated Vehicles." Applied Sciences 11, no. 15 (July 21, 2021): 6685. http://dx.doi.org/10.3390/app11156685.

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According to SAE J3016, autonomous driving can be divided into six levels, and partially automated driving is possible from level three up. A partially or highly automated vehicle can encounter situations involving total system failure. Here, we studied a strategy for safe takeover in such situations. A human-in-the-loop simulator, driver–vehicle interface, and driver monitoring system were developed, and takeover experiments were performed using various driving scenarios and realistic autonomous driving situations. The experiments allowed us to draw the following conclusions. The visual–auditory–haptic complex alarm effectively delivered warnings and had a clear correlation with the user’s subjective preferences. There were scenario types in which the system had to immediately enter minimum risk maneuvers or emergency maneuvers without requesting takeover. Lastly, the risk of accidents can be reduced by the driver monitoring system that prevents the driver from being completely immersed in non-driving-related tasks. We proposed a safe takeover strategy from these results, which provides meaningful guidance for the development of autonomous vehicles. Considering the subjective questionnaire evaluations of users, it is expected to improve the acceptance of autonomous vehicles and increase the adoption of autonomous vehicles.

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Hemmati, Maryam, Morteza Biglari-Abhari, and Smail Niar. "Adaptive Real-Time Object Detection for Autonomous Driving Systems." Journal of Imaging 8, no. 4 (April 11, 2022): 106. http://dx.doi.org/10.3390/jimaging8040106.

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Accurate and reliable detection is one of the main tasks of Autonomous Driving Systems (ADS). While detecting the obstacles on the road during various environmental circumstances add to the reliability of ADS, it results in more intensive computations and more complicated systems. The stringent real-time requirements of ADS, resource constraints, and energy efficiency considerations add to the design complications. This work presents an adaptive system that detects pedestrians and vehicles in different lighting conditions on the road. We take a hardware-software co-design approach on Zynq UltraScale+ MPSoC and develop a dynamically reconfigurable ADS that employs hardware accelerators for pedestrian and vehicle detection and adapts its detection method to the environment lighting conditions. The results show that the system maintains real-time performance and achieves adaptability with minimal resource overhead.

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MORINO, Hiroaki. "Enhanced autonomous driving and driver assistance with communication systems." Proceedings of Mechanical Engineering Congress, Japan 2019 (2019): F25205. http://dx.doi.org/10.1299/jsmemecj.2019.f25205.

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Santos, Vitor, Angel D. Sappa, and Miguel Oliveira. "Special Issue on Autonomous Driving and Driver Assistance Systems." Robotics and Autonomous Systems 91 (May 2017): 208–9. http://dx.doi.org/10.1016/j.robot.2017.01.011.

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Kolodko, J., and L. Vlacic. "Cooperative autonomous driving at the Intelligent Control Systems Laboratory." IEEE Intelligent Systems 18, no. 4 (July 2003): 8–11. http://dx.doi.org/10.1109/mis.2003.1217622.

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Davis, James, Asisat Animashaun, Edward Schoenherr, and Kaleb McDowell. "Evaluation of semi-autonomous convoy driving." Journal of Field Robotics 25, no. 11-12 (November 2008): 880–97. http://dx.doi.org/10.1002/rob.20263.

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Boric, Sandra, Edgar Schiebel, Christian Schlögl, Michaela Hildebrandt, Christina Hofer, and Doris M. Macht. "Research in Autonomous Driving – A Historic Bibliometric View of the Research Development in Autonomous Driving." International Journal of Innovation and Economic Development 7, no. 5 (December 2021): 27–44. http://dx.doi.org/10.18775/ijied.1849-7551-7020.2015.74.2003.

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Autonomous driving has become an increasingly relevant issue for policymakers, the industry, service providers, infrastructure companies, and science. This study shows how bibliometrics can be used to identify the major technological aspects of an emerging research field such as autonomous driving. We examine the most influential publications and identify research fronts of scientific activities until 2017 based on a bibliometric literature analysis. Using the science mapping approach, publications in the research field of autonomous driving were retrieved from Web of Science and then structured using the bibliometric software BibTechMon by the AIT (Austrian Institute of Technology). At the time of our analysis, we identified four research fronts in the field of autonomous driving: (I) Autonomous Vehicles and Infrastructure, (II) Driver Assistance Systems, (III) Autonomous Mobile Robots, and (IV) IntraFace, i.e., automated facial image analysis. Researchers were working extensively on technologies that support the navigation and collection of data. Our analysis indicates that research was moving towards autonomous navigation and infrastructure in the urban environment. A noticeable number of publications focused on technologies for environment detection in automated vehicles. Still, research pointed at the technological challenges to make automated driving safe.

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Boric, Sandra, Edgar Schiebel, Christian Schlögl, Michaela Hildebrandt, Christina Hofer, and Doris M. Macht. "Research in Autonomous Driving – A Historic Bibliometric View of the Research Development in Autonomous Driving." International Journal of Innovation and Economic Development 7, no. 5 (December 2021): 27–44. http://dx.doi.org/10.18775/10.18775/ijied.1849-7551-7020.2015.75.2003.

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Autonomous driving has become an increasingly relevant issue for policymakers, the industry, service providers, infrastructure companies, and science. This study shows how bibliometrics can be used to identify the major technological aspects of an emerging research field such as autonomous driving. We examine the most influential publications and identify research fronts of scientific activities until 2017 based on a bibliometric literature analysis. Using the science mapping approach, publications in the research field of autonomous driving were retrieved from Web of Science and then structured using the bibliometric software BibTechMon by the AIT (Austrian Institute of Technology). At the time of our analysis, we identified four research fronts in the field of autonomous driving: (I) Autonomous Vehicles and Infrastructure, (II) Driver Assistance Systems, (III) Autonomous Mobile Robots, and (IV) IntraFace, i.e., automated facial image analysis. Researchers were working extensively on technologies that support the navigation and collection of data. Our analysis indicates that research was moving towards autonomous navigation and infrastructure in the urban environment. A noticeable number of publications focused on technologies for environment detection in automated vehicles. Still, research pointed at the technological challenges to make automated driving safe.

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Garg, Animesh, Anju Toor, Sahil Thakkar, Shiwangi Goel, Sachin Maheshwari, and Satish Chand. "The Autotrix: Design and Implementation of an Autonomous Urban Driving System." Advanced Materials Research 403-408 (November 2011): 3884–91. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3884.

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The Autotrix is an interactive, intelligent, Autonomous Guided Vehicle (AGV) designed to serve in urban environments. Autonomous ground vehicle navigation requires the integration of many technologies such as path planning, odometry, control, obstacle avoidance and situational awareness. The objective of this project is for this prototype to navigate autonomously in an urban environment and reach its destination while detecting and avoiding obstacles on the path .This will be achieved by extracting information from multiple sources of real-time data including digital camera, GPS &ultra sonic sensors, collecting data from this extracted information, processing this data and send controlling instructions to our platform (Autotrix). The significance of this work is in presenting the methods needed for real time navigation; GPS based continuous mapping and obstacle avoidance for intelligent autonomous driving systems.

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Klauer, Christian, Manuel Schwabe, and Hamid Mobalegh. "Path Tracking Control for Urban Autonomous Driving." IFAC-PapersOnLine 53, no. 2 (2020): 15705–12. http://dx.doi.org/10.1016/j.ifacol.2020.12.2569.

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Xiao, Yineng, and Zhao Liu. "Accident Liability Determination of Autonomous Driving Systems Based on Artificial Intelligence Technology and Its Impact on Public Mental Health." Journal of Environmental and Public Health 2022 (August 31, 2022): 1–12. http://dx.doi.org/10.1155/2022/2671968.

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With the rise of self-driving technology research, the establishment of a scientific and perfect legal restraint and supervision system for self-driving vehicles has been gradually paid attention to. The determination of tort liability subject of traffic accidents of self-driving cars is different from that of ordinary motor vehicle traffic accident tort, which challenges the application of traditional fault liability and product liability. The tort issue of self-driving cars should be discussed by distinguishing two kinds of situations: assisted driving cars and highly automated driving, and typological analysis of each situation is needed. When the car is in the assisted driving mode, the accident occurs due to the quality defect or product damage of the self-driving car, and there is no other fault cause; then, the producer and seller of the car should bear the product liability according to the no-fault principle; if the driver has a subjective fault and fails to exercise a high degree of care; the owner and user of the car should bear the fault liability. This paper analyzes the study of the impact of autonomous driving public on public psychological health, summarizes the key factors affecting the public acceptance of autonomous driving, and dissects its impact on public psychological acceptance. In order to fully study the responsibility determination of autonomous driving system accidents and their impact on public psychological health, this paper proposes an autonomous driving risk prediction model based on artificial intelligence technology, combined with a complex intelligent traffic environment vehicle autonomous driving risk prediction method, to complete the risk target detection. The experimental results in the relevant dataset demonstrate the effectiveness of the proposed method.

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Shea-Blymyer, Colin, and Houssam Abbas. "Algorithmic Ethics: Formalization and Verification of Autonomous Vehicle Obligations." ACM Transactions on Cyber-Physical Systems 5, no. 4 (October 31, 2021): 1–25. http://dx.doi.org/10.1145/3460975.

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In this article, we develop a formal framework for automatic reasoning about the obligations of autonomous cyber-physical systems, including their social and ethical obligations. Obligations, permissions, and prohibitions are distinct from a system's mission, and are a necessary part of specifying advanced, adaptive AI-equipped systems. They need a dedicated deontic logic of obligations to formalize them. Most existing deontic logics lack corresponding algorithms and system models that permit automatic verification. We demonstrate how a particular deontic logic, Dominance Act Utilitarianism (DAU) [23], is a suitable starting point for formalizing the obligations of autonomous systems like self-driving cars. We demonstrate its usefulness by formalizing a subset of Responsibility-Sensitive Safety (RSS) in DAU; RSS is an industrial proposal for how self-driving cars should and should not behave in traffic. We show that certain logical consequences of RSS are undesirable, indicating a need to further refine the proposal. We also demonstrate how obligations can change over time, which is necessary for long-term autonomy. We then demonstrate a model-checking algorithm for DAU formulas on weighted transition systems and illustrate it by model-checking obligations of a self-driving car controller from the literature.

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Funkhouser, Kelly, and Frank Drews. "Reaction Times When Switching From Autonomous to Manual Driving Control." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (September 2016): 1854–58. http://dx.doi.org/10.1177/1541931213601423.

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As autonomous vehicles become more prevalent in our everyday lives, we must succumb to the realities of technological deficiencies. Although a future of fully autonomous vehicles would be the pinnacle of safety and efficiency, the current reality leaves us in a transitional state requiring human interaction with autonomous systems. Therefore it is imperative to understand human-system interaction with the autonomous features in current and future technologies. To gain an improved understanding, we designed an investigational study to gain a better understanding of human performance parameters at the moment they relieve and regain control of autonomous systems. The current findings show that reaction time increases as time disengaged from the task of driving increases, regardless of cognitive engagement.

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Schӓbe, H. "Autonomous Driving – How to Apply Safety Principles." Dependability 19, no. 3 (September 17, 2019): 21–33. http://dx.doi.org/10.21683/1729-2646-2019-19-3-21-33.

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We discuss safety principles of autonomous driving road vehicles. First, we provide a comparison between principles and experience of autonomous or automatic systems on rails and on the road. An automatic metro operates in a controlled and well-defined environment, passengers and third persons are separated from driving trains by fences, tunnels, etc. A road vehicle operates in a much more complex environment. Further, we discuss safety principles. The application of safety principles (e.g. fail-safe or safe-life) is used to design and implement a safe system that eventually fulfils the requirements of the functional safety standards. The different responsibility of human driver and technical driving system in different automation levels for autonomous driving vehicles require the application of safety principles. We consider, which safety principles have to be applied using general safety principles and analysing the relevant SAE level based on the experience from projects for the five levels of automated driving as defined by the SAE. Depending on the level of automation, the technical systems are implemented as fail-silent, fails-safe or as safe-life.

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Lindgren, Thomas, Vaike Fors, Sarah Pink, and Katalin Osz. "Anticipatory experience in everyday autonomous driving." Personal and Ubiquitous Computing 24, no. 6 (May 6, 2020): 747–62. http://dx.doi.org/10.1007/s00779-020-01410-6.

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AbstractIn this paper, we discuss how people’s user experience (UX) of autonomous driving (AD) cars can be understood as a shifting anticipatory experience, as people experience degrees of AD through evolving advanced driver assistance systems (ADAS) in their everyday context. We draw on our ethnographic studies of five families, who had access to AD research cars with evolving ADAS features in their everyday lives for a duration of 1½ years. Our analysis shows that people gradually adopt AD cars, through a process that involves anticipating if they can trust them, what the ADAS features will do and what the longer-term technological possibilities will be. It also showed that this anticipatory UX occurs within specific socio-technical and environmental circumstances, which could not be captured easily in experimental settings. The implication is that studying anticipation offers us new insights into how people adopt AD in their everyday commute driving.

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Zimmermann, Martin, and Franz Wotawa. "An adaptive system for autonomous driving." Software Quality Journal 28, no. 3 (July 4, 2020): 1189–212. http://dx.doi.org/10.1007/s11219-020-09519-w.

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Abstract Having systems that can adapt themselves in case of faults or changing environmental conditions is of growing interest for industry and especially for the automotive industry considering autonomous driving. In autonomous driving, it is vital to have a system that is able to cope with faults in order to enable the system to reach a safe state. In this paper, we present an adaptive control method that can be used for this purpose. The method selects alternative actions so that given goal states can be reached, providing the availability of a certain degree of redundancy. The action selection is based on weight models that are adapted over time, capturing the success rate of certain actions. Besides the method, we present a Java implementation and its validation based on two case studies motivated by the requirements of the autonomous driving domain. We show that the presented approach is applicable both in case of environmental changes but also in case of faults occurring during operation. In the latter case, the methods provide an adaptive behavior very much close to the optimal selection.

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Alabdulkreem, Eatedal, Jaber Alzahrani, Nadhem Nemri, Olayan Alharbi, Abdullah Mohamed, Radwa Marzouk, and Anwer Hilal. "Computational Intelligence with Wild Horse Optimization Based Object Recognition and Classification Model for Autonomous Driving Systems." Applied Sciences 12, no. 12 (June 20, 2022): 6249. http://dx.doi.org/10.3390/app12126249.

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Presently, autonomous systems have gained considerable attention in several fields such as transportation, healthcare, autonomous driving, logistics, etc. It is highly needed to ensure the safe operations of the autonomous system before launching it to the general public. Since the design of a completely autonomous system is a challenging process, perception and decision-making act as vital parts. The effective detection of objects on the road under varying scenarios can considerably enhance the safety of autonomous driving. The recently developed computational intelligence (CI) and deep learning models help to effectively design the object detection algorithms for environment perception depending upon the camera system that exists in the autonomous driving systems. With this motivation, this study designed a novel computational intelligence with a wild horse optimization-based object recognition and classification (CIWHO-ORC) model for autonomous driving systems. The proposed CIWHO-ORC technique intends to effectively identify the presence of multiple static and dynamic objects such as vehicles, pedestrians, signboards, etc. Additionally, the CIWHO-ORC technique involves the design of a krill herd (KH) algorithm with a multi-scale Faster RCNN model for the detection of objects. In addition, a wild horse optimizer (WHO) with an online sequential ridge regression (OSRR) model was applied for the classification of recognized objects. The experimental analysis of the CIWHO-ORC technique is validated using benchmark datasets, and the obtained results demonstrate the promising outcome of the CIWHO-ORC technique in terms of several measures.

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Sun, Chen, Jean M. Uwabeza Vianney, Ying Li, Long Chen, Li Li, Fei-Yue Wang, Amir Khajepour, and Dongpu Cao. "Proximity based automatic data annotation for autonomous driving." IEEE/CAA Journal of Automatica Sinica 7, no. 2 (March 2020): 395–404. http://dx.doi.org/10.1109/jas.2020.1003033.

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Shin, Seok-San, Ho-Joon Kang, and Seong-Jin Kwon. "A Study on Data Analysis for Improving Driving Safety in Field Operational Test (FOT) of Autonomous Vehicles." Machines 10, no. 9 (September 7, 2022): 784. http://dx.doi.org/10.3390/machines10090784.

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In this study, an autonomous driving test was conducted from the perspective of FOT (field operational test). For data analysis and improvement methods, scenarios for FOT were classified and defined by considering autonomous driving level (SAE J3016) and the viewpoints of the vehicle, driver, road, environment, etc. To obtain data from FOT, performance indicators were selected, a data collection environment was implemented in the test cases, and driving roads were selected to obtain driving data from the vehicle while it was driven on an actual road. In the pilot FOT course, data were collected in various driving situations using a test vehicle, and the effect of autonomous driving-related functions on improving driving safety was studied through data analysis of discovered major events.

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Wang, Pengwei, Song Gao, Liang Li, Shuo Cheng, and Hailan Zhao. "Research on driving behavior decision making system of autonomous driving vehicle based on benefit evaluation model." Archives of Transport 53, no. 1 (April 30, 2020): 21–36. http://dx.doi.org/10.5604/01.3001.0014.1740.

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Autonomous driving vehicle could increase driving efficiency, reduce traffic congestion and improve driving safety, it is considered as the solution of current traffic problems. Decision making systems for autonomous driving vehicles have significant effects on driving performance. The performance of decision making system is affected by its framework and decision making model. In real traffic scenarios, the driving condition of autonomous driving vehicle faced is random and time-varying, the performance of current decision making system is unable to meet the full scene autonomous driving requirements. For autonomous driving vehicle, the division between different driving behaviors needs clear boundary conditions. Typically, in lane change scenario, multiple reasonable driving behavior choices cause conflict of driving state. The fundamental cause of conflict lies in overlapping boundary conditions. To design a decision making system for autonomous driving vehicles, firstly, based on the decomposition of human driver operation process, five basic driving behavior modes are constructed, a driving behavior decision making framework for autonomous driving vehicle based on finite state machine is proposed. Then, to achieve lane change decision making for autonomous driving vehicle, lane change behavior characteristics of human driver lane change maneuver are analyzed and extracted. Based on the analysis, multiple attributes such as driving efficiency and safety are considered, all attributes benefits are quantified and the driving behavior benefit evaluation model is established. By evaluating the benefits of all alternative driving behaviors, the optimal driving behavior for current driving scenario is output. Finally, to verify the performances of the proposed decision making model, a series of real vehicle tests are implemented in different scenarios, the real time performance, effectiveness, and feasibility performance of the proposed method is accessed. The results show that the proposed driving behavior decision making model has good feasibility, real-time performance and multi-choice filtering performance in dynamic traffic scenarios.

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Holen, Martin, Kristian Muri Knausgård, and Morten Goodwin. "Development of a Simulator for Prototyping Reinforcement Learning-Based Autonomous Cars." Informatics 9, no. 2 (April 15, 2022): 33. http://dx.doi.org/10.3390/informatics9020033.

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Autonomous driving is a research field that has received attention in recent years, with increasing applications of reinforcement learning (RL) algorithms. It is impractical to train an autonomous vehicle thoroughly in the physical space, i.e., the so-called ’real world’; therefore, simulators are used in almost all training of autonomous driving algorithms. There are numerous autonomous driving simulators, very few of which are specifically targeted at RL. RL-based cars are challenging due to the variety of reward functions available. There is a lack of simulators addressing many central RL research tasks within autonomous driving, such as scene understanding, localization and mapping, planning and driving policies, and control, which have diverse requirements and goals. It is, therefore, challenging to prototype new RL projects with different simulators, especially when there is a need to examine several reward functions at once. This paper introduces a modified simulator based on the Udacity simulator, made for autonomous cars using RL. It creates reward functions, along with sensors to create a baseline implementation for RL-based vehicles. The modified simulator also resets the vehicle when it gets stuck or is in a non-terminating loop, making it more reliable. Overall, the paper seeks to make the prototyping of new systems simple, with the testing of different RL-based systems.

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Hjalmarsson-Jordanius, Anders, Mikael Edvardsson, Martin Romell, Johan Isacson, Carl-Johan Aldén, and Niklas Sundin. "Autonomous Transport: Transforming Logistics through Driverless Intelligent Transportation." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 7 (September 17, 2018): 24–33. http://dx.doi.org/10.1177/0361198118796968.

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How can autonomous technology be used beyond end-customer autonomous driving features? This position paper addresses this problem by exploring a novel autonomous transport solution applied in the automotive logistics domain. We propose that factory-complete cars can be transformed to become their own autonomous guided vehicles and thus transport themselves when being moved from the factory for shipment. Cars equipped with such a system are driverless and use an onboard autonomous transport solution combined with the advanced driver assistance systems pre-installed in the car for end-customer use. The solution uses factory-equipped sensors as well as the connectivity infrastructure installed in the car. This means that the solution does not require any extra components to enable the car to transport itself autonomously to complete a transport mission in the logistics chain. The solution also includes an intelligent off-board traffic control system that defines the transport mission and manages the interaction between vehicles during systems operation. A prototype of the system has been developed which was tested successfully in live trials at the Volvo Car Group plant in Gothenburg Sweden in 2017. In the paper, autonomous transport is positioned in between autonomous guided vehicles and autonomous driving technology. A review of the literature on autonomous vehicle technology offers contextual background to this positioning. The paper also presents the solution and displays lessons learned from the live trials. Finally, other use areas are introduced for driverless autonomous transport beyond the automotive logistics domain that is the focus of this paper.

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Orgován, László, Tamás Bécsi, and Szilárd Aradi. "Autonomous Drifting Using Reinforcement Learning." Periodica Polytechnica Transportation Engineering 49, no. 3 (September 1, 2021): 292–300. http://dx.doi.org/10.3311/pptr.18581.

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Autonomous vehicles or self-driving cars are prevalent nowadays, many vehicle manufacturers, and other tech companies are trying to develop autonomous vehicles. One major goal of the self-driving algorithms is to perform manoeuvres safely, even when some anomaly arises. To solve these kinds of complex issues, Artificial Intelligence and Machine Learning methods are used. One of these motion planning problems is when the tires lose their grip on the road, an autonomous vehicle should handle this situation. Thus the paper provides an Autonomous Drifting algorithm using Reinforcement Learning. The algorithm is based on a model-free learning algorithm, Twin Delayed Deep Deterministic Policy Gradients (TD3). The model is trained on six different tracks in a simulator, which is developed specifically for autonomous driving systems; namely CARLA.

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Islam, Mhafuzul, Mashrur Chowdhury, Hongda Li, and Hongxin Hu. "Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 12 (July 31, 2019): 494–507. http://dx.doi.org/10.1177/0361198119855606.

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Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.

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Journal articles on the topic 'Autonomous Driving Systems'

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