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Journal articles on the topic 'Autonomous vehicle testing'
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1
Tulsyan, Ansh, Anshul Bhardwaj, Pranjal Shukla, Piyush Gautam, and Tushar Singh. "Autonomous Vehicle Simulation." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (January 5, 2025): 1–9. https://doi.org/10.55041/ijsrem40459.
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Autonomous vehicles represent a revolutionary advancement in transportation technology, relying heavily on sophisticated simulations for development and testing. This abstract presents an approach to autonomous vehicle simulation utilizing the Udacity Self-Driving Car Nanodegree platform coupled with Convolutional Neural Networks (CNNs) for perception tasks. The simulation environment provided by Udacity offers a realistic representation of urban driving scenarios, allowing developers to test and train autonomous vehicle algorithms in a virtual setting before deploying them on real-world roads. This environment includes various elements such as traffic lights, pedestrians, and other vehicles, providing a comprehensive testbed for algorithmic validation. To enhance the perception capabilities of autonomous vehicles within this simulated environment, Convolutional Neural Networks (CNNs) are employed. CNNs have proven effective in image recognition tasks, making them well-suited for tasks like object detection and classification crucial to autonomous driving. The neural network is trained on a diverse dataset, enabling it to accurately identify and interpret the surrounding environment through sensor data, such as camera images. The integration of CNNs with the Udacity simulation platform enables the autonomous vehicle to make informed decisions based on its perception of the environment. The trained CNN serves as a crucial component in the overall perception pipeline, enhancing the vehicle's ability to recognize and respond to dynamic and complex scenarios. Keywords—Autonomous vehicle simulation, Artificial Intelligence, Object Recognition, Real-Time Processing, convolutional neural networks.
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Dickens, John, Thabisa Maweni, Tiro Setati, and Zubair Suddoo. "Design of HERMES: a mobile autonomous surveillance robot for security patrol." MATEC Web of Conferences 388 (2023): 04005. http://dx.doi.org/10.1051/matecconf/202338804005.
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The HERMES autonomous surveillance robot platform is a low-cost outdoor autonomous surveillance vehicle. It is designed to autonomously patrol outdoor areas performing surveillance and providing automated alerts of detected vehicles and people. The design and testing of this system are covered in this paper. The design philosophy focused on the use of off-the-shelf components wherever possible with the base of the robot being a modified electric quadbike. The testing has verified that the surveillance robot can perform real-time person and vehicle detection, video streaming, manual and autonomous navigation on a low-cost platform. The development of the robot platform is continuing with the current focus being on the improvement of the autonomous navigation, ingress protection (IP) rating and verification of the battery life.
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Abu Bakar, Amirul Ibrahim, Mohd Azman Abas, Mohd Farid Muhamad Said, and Tengku Azrul Tengku Azhar. "Synthesis of Autonomous Vehicle Guideline for Public Road-Testing Sustainability." Sustainability 14, no. 3 (January 27, 2022): 1456. http://dx.doi.org/10.3390/su14031456.
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Autonomous vehicles have the potential to reduce the risk of accidents as they eliminate the element of human error from driving. Lack of attention, poor judgement, or physical limitations may lead to road incidents. Thus, the development and deployment of autonomous vehicles should be a priority. However, before being publicly available, autonomous vehicles must be tested to ensure their viability and safety by conducting public road testing. Autonomous vehicles have been designed and tested since the early 1900s; however, deployment of fully autonomous vehicles on public roads only started in the 2000s. Numerous countries have developed guidelines for public road testing, but those rules are not uniform, and discrepancies occur between nations. Issues such as vehicular safety, registrations, authority, insurance, cybersecurity, and infrastructures weigh differently in each country. Synthesizing these diverse national regulations into global guidelines would promote the safety and sustainability of autonomous vehicle testing and benefit all parties interested in autonomous vehicles.
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Chen, Hong Yun, Yan Qiang Li, Zi Hui Zhang, and Yong Wang. "Test Method for Decision Planning of Autonomous Vehicles Based on DQN Algorithm." E3S Web of Conferences 253 (2021): 03022. http://dx.doi.org/10.1051/e3sconf/202125303022.
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In February 2020, Beijing, China andCalifornia, USA respectively released road test reports of 2019 for autonomous vehicles. Beijing and California respectively represent the highest level of testing and application of autonomous vehicles in the two countries. This article will compare the test items, evaluation criteria and technical defects of each autonomous vehicle company in the road test reports of China and the United States, also analyze the existing problems, and propose an idea for the construction of a comprehensive test site for autonomous vehicles. This article aims to solve the prominently exposed problems in decision-making and planning in autonomous vehicles with DQN algorithm-base vehicle fleet, and to look forward to the future development trend of autonomous driving testing.
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Cao, Yicheng, Haiming Sun, Guisheng Li, Chuan Sun, Haoran Li, Junru Yang, Liangyu Tian, and Fei Li. "Multi-Environment Vehicle Trajectory Automatic Driving Scene Generation Method Based on Simulation and Real Vehicle Testing." Electronics 14, no. 5 (March 1, 2025): 1000. https://doi.org/10.3390/electronics14051000.
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As autonomous vehicles increasingly populate roads, robust testing is essential to ensure their safety and reliability. Due to the limitation that traditional testing methodologies (real-world and simulation testing) are difficult to cover a wide range of scenarios and ensure repeatability, this study proposes a novel virtual-real fusion testing approach that integrates Graph Theory and Artificial Potential Fields (APF) in virtual-real fusion autonomous vehicle testing. Conducted using SUMO software, our strategic lane change and speed adjustment simulation experiments demonstrate that our approach can efficiently handle vehicle dynamics and environmental interactions compared to traditional Rapidly-exploring Random Tree (RRT) methods. The proposed method shows a significant reduction in maneuver completion times—up to 41% faster in simulations and 55% faster in real-world tests. Field experiments at the Vehicle-Road-Cloud Integrated Platform in Suzhou High-Speed Railway New Town confirmed the method’s practical viability and robustness under real traffic conditions. The results indicate that our integrated approach enhances the authenticity and efficiency of testing, thereby advancing the development of dependable, autonomous driving systems. This research not only contributes to the theoretical framework but also has practical implications for improving autonomous vehicle testing processes.
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Kwon, Donghwoon, Ritesh Malaiya, Geumchae Yoon, Jeong-Tak Ryu, and Su-Young Pi. "A Study on Development of the Camera-Based Blind Spot Detection System Using the Deep Learning Methodology." Applied Sciences 9, no. 14 (July 23, 2019): 2941. http://dx.doi.org/10.3390/app9142941.
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One of the recent news headlines is that a pedestrian was killed by an autonomous vehicle because safety features in this vehicle did not detect an object on a road correctly. Due to this accident, some global automobile companies announced plans to postpone development of an autonomous vehicle. Furthermore, there is no doubt about the importance of safety features for autonomous vehicles. For this reason, our research goal is the development of a very safe and lightweight camera-based blind spot detection system, which can be applied to future autonomous vehicles. The blind spot detection system was implemented in open source software. Approximately 2000 vehicle images and 9000 non-vehicle images were adopted for training the Fully Connected Network (FCN) model. Other data processing concepts such as the Histogram of Oriented Gradients (HOG), heat map, and thresholding were also employed. We achieved 99.43% training accuracy and 98.99% testing accuracy of the FCN model, respectively. Source codes with respect to all the methodologies were then deployed to an off-the-shelf embedded board for actual testing on a road. Actual testing was conducted with consideration of various factors, and we confirmed 93.75% average detection accuracy with three false positives.
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Bhavsar, Parth, Plaban Das, Matthew Paugh, Kakan Dey, and Mashrur Chowdhury. "Risk Analysis of Autonomous Vehicles in Mixed Traffic Streams." Transportation Research Record: Journal of the Transportation Research Board 2625, no. 1 (January 2017): 51–61. http://dx.doi.org/10.3141/2625-06.
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The introduction of autonomous vehicles in the surface transportation system could improve traffic safety and reduce traffic congestion and negative environmental effects. Although the continuous evolution in computing, sensing, and communication technologies can improve the performance of autonomous vehicles, the new combination of autonomous automotive and electronic communication technologies will present new challenges, such as interaction with other nonautonomous vehicles, which must be addressed before implementation. The objective of this study was to identify the risks associated with the failure of an autonomous vehicle in mixed traffic streams. To identify the risks, the autonomous vehicle system was first disassembled into vehicular components and transportation infrastructure components, and then a fault tree model was developed for each system. The failure probabilities of each component were estimated by reviewing the published literature and publicly available data sources. This analysis resulted in a failure probability of about 14% resulting from a sequential failure of the autonomous vehicular components alone in the vehicle’s lifetime, particularly the components responsible for automation. After the failure probability of autonomous vehicle components was combined with the failure probability of transportation infrastructure components, an overall failure probability related to vehicular or infrastructure components was found: 158 per 1 million mi of travel. The most critical combination of events that could lead to failure of autonomous vehicles, known as minimal cut-sets, was also identified. Finally, the results of fault tree analysis were compared with real-world data available from the California Department of Motor Vehicles autonomous vehicle testing records.
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Feys, Manon, Evy Rombaut, and Lieselot Vanhaverbeke. "Does a Test Ride Influence Attitude towards Autonomous Vehicles? A Field Experiment with Pretest and Posttest Measurement." Sustainability 13, no. 10 (May 12, 2021): 5387. http://dx.doi.org/10.3390/su13105387.
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Autonomous vehicles have the potential to disrupt the mobility system. Therefore, it is important to understand attitude formation towards autonomous vehicles. The focus of this study is on the private user’s technology acceptance of an autonomous vehicle. The study applies the determinants of technology acceptance to capture users’ attitude towards and intention to adopt autonomous vehicles. A field experiment with 27 participants was conducted to assess changes in determinants before and after a test ride with a level 2 automated vehicle. The automated vehicle was equipped with technology that allowed a hands-off, feet-off experience on a public road in real traffic. The results show that a ride has a positive and significant effect on attitudes towards autonomous vehicles. Additionally, participants with higher ratings of technology anxiety show a remarkable increase in attitude towards autonomous vehicles after the ride compared to participants with lower levels of technology anxiety. These findings indicate that experience with a partially automated vehicle has a potentially positive effect on the acceptance of autonomous vehicles. As such, our study illustrates the importance of continuous pilot testing with private automated vehicles to increase future user acceptance of autonomous vehicles.
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Cao, Hang, and Máté Zöldy. "An Investigation of Autonomous Vehicle Roundabout Situation." Periodica Polytechnica Transportation Engineering 48, no. 3 (August 4, 2019): 236–41. http://dx.doi.org/10.3311/pptr.13762.
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The aim of this paper is to evaluate the impact of connected autonomous behavior in real vehicles on vehicle fuel consumption and emission reductions. Authors provide a preliminary theoretical summary to assess the driving conditions of autonomous vehicles in roundabout, which attempts exploring the impact of driving behavior patterns on fuel consumption and emissions, and including other key factors of autonomous vehicles to reduce fuel consumption and emissions. After summarizing, driving behavior, effective in-vehicle systems, both roundabout physical parameters and vehicle type are all play an important role in energy using. ZalaZONE’s roundabout is selected for preliminary test scenario establishment, which lays a design foundation for further in-depth testing.
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Li, Mu, Yingqi Liu, and Ruiyu Feng. "How Can China’s Autonomous Vehicle Companies Use Digital Empowerment to Improve Innovation Quality?—The Role of Digital Platform Capabilities and Boundary-Spanning Search." Systems 13, no. 1 (January 10, 2025): 45. https://doi.org/10.3390/systems13010045.
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The acquisition, integration, and exchange of digital technologies considerably contribute to the improvement of corporate innovation quality, as autonomous vehicles are a complex amalgamation of multiple industrial chains. In order to address the intense global competition in the autonomous vehicles industry and help China’s enterprises establish a prominent position in technological innovation, this study innovatively integrates the concepts of digital empowerment, digital platform capabilities, and boundary-spanning search into a cohesive framework, examines the pathways of influence, and methodically builds a multiple-chain mediation model. It employs various quantitative models, such as reliability and validity testing, confirmatory factor analysis, common method bias testing, mediation effect analysis, and robustness testing. The study focuses on over a hundred companies related to autonomous vehicles in China, employing software such as SPSS26.0, AMOS26.0, PROCESS4.0, and MPLUS8.3 to conduct this analysis. The findings indicate that digital empowerment is a critical factor in the improvement of innovation quality within autonomous vehicle companies. The relationship between digital empowerment and innovation quality is partially mediated by digital platform capabilities, and the boundary-spanning search also functions as a partial intermediary. Additionally, the quality of innovation and digital empowerment are mediated by the boundary-spanning search and the capabilities of digital platforms. The results of this study provide valuable insights on how to accurately empower the high-quality development of the autonomous vehicle sector with digital technologies, revealing new perspectives on the innovation quality enhancement pathways for autonomous vehicle companies in China, offering pivotal insights amidst the escalating competition within the global autonomous vehicle sector.
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Xu, Zhigang, Mingliang Wang, Fengzhi Zhang, Sheng Jin, Jin Zhang, and Xiangmo Zhao. "PaTAVTT: A Hardware-in-the-Loop Scaled Platform for Testing Autonomous Vehicle Trajectory Tracking." Journal of Advanced Transportation 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/9203251.
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With the advent of autonomous vehicles, in particular its adaptability to harsh conditions, the research and development of autonomous vehicles attract significant attention by not only academia but also practitioners. Due to the high risk, high cost, and difficulty to test autonomous vehicles under harsh conditions, the hardware-in-the-loop (HIL) scaled platform has been proposed as it is a safe, inexpensive, and effective test method. This platform system consists of scaled autonomous vehicle, scaled roadway, monitoring center, transmission device, positioning device, and computers. This paper uses a case of the development process of tracking control for high-speed U-turn to build the tracking control function. Further, a simplified vehicle dynamics model and a trajectory tracking algorithm have been considered to build the simulation test. The experiment results demonstrate the effectiveness of the HIL scaled platform.
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Reddy V, Subba, Venu Madhav, Bhargav M, Anjani Krishna A, Kishore A, Syed Inthiyaz, and Sk Hasane Ahammad. "Hybrid Autonomous Vehicle (Aerial and Grounded)." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 1 (February 6, 2023): 103–9. http://dx.doi.org/10.17762/ijritcc.v11i1.6056.
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This work discusses hybrid autonomous vehicles that are grounded and aerial vehicles that are utilized to select their course based on their environmental characteristics. It includes algorithms for path planning, obstacle avoidance, and trajectory planning. It also has a microcontroller, known as the PIXHAWK Flight Controller, for various transmissions and configurations. Calibration and testing are performed using Mission Planner software. This article shows the different problematic features of an autonomous vehicle with several functionalities.
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Palanichamy, Karthikeyan. "Developing Zero-Cost Solutions for Testing Autonomous Vehicle Software." International Journal of Engineering and Computer Science 13, no. 03 (March 29, 2024): 26068–100. http://dx.doi.org/10.18535/ijecs/v13i03.4806.
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As the development of autonomous vehicles accelerates, the need for robust and cost-effective testing solutions becomes paramount. This paper explores the concept of zero-cost solutions for testing autonomous vehicle software, aiming to reduce development expenses while maintaining high testing efficacy. We examine various methodologies, tools, and strategies that leverage existing resources to create effective testing frameworks without significant financial investment. Case studies and practical implementations illustrate the feasibility and benefits of these approaches in real-world scenarios.
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Wei, Zhiyuan, Hanchu Zhou, and Rui Zhou. "Risk and Complexity Assessment of Autonomous Vehicle Testing Scenarios." Applied Sciences 14, no. 21 (October 28, 2024): 9866. http://dx.doi.org/10.3390/app14219866.
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Autonomous vehicles (AVs) must fulfill adequate safety requirements before formal application, and performing an effective functional evaluation to verify vehicle safety requires extensive testing in different scenarios. However, it is crucial to rationalize the application of different scenarios to support different testing needs; thus, one of the current challenges limiting the development of AVs is the critical evaluation of scenarios, i.e., the lack of quantitative criteria for scenario design. This study introduces a method using the Spherical Fuzzy-Analytical Network Process (SF-ANP) to evaluate these scenarios, addressing their inherent risks and complexities. The method involves constructing a five-layer model to decompose scenario elements and using SF-ANP to calculate weights based on element interactions. The study evaluates 700 scenarios from the China In-depth Traffic Safety Study–Traffic Accident (CIMSS-TA) database, incorporating fuzzy factors and element weights. Virtual simulation of vehicles in the scenarios was performed using Baidu Apollo, and the performance of the scenarios was assessed by collecting the vehicle test results. The correlation between the obtained alternative safety indicators and the quantitative values confirms the validity and scientific validity of this approach. This will provide valuable guidance for categorizing audiovisual test scenarios and selecting corresponding scenarios to challenge different levels of vehicle functionality. At the same time, it can be used as a design basis to generate a large number of effective scenarios to accelerate the construction of scenario libraries and promote commercialization of AVs.
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Crewe, Jacob, Aditya Humnabadkar, Yonghuai Liu, Amr Ahmed, and Ardhendu Behera. "SLAV-Sim: A Framework for Self-Learning Autonomous Vehicle Simulation." Sensors 23, no. 20 (October 23, 2023): 8649. http://dx.doi.org/10.3390/s23208649.
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With the advent of autonomous vehicles, sensors and algorithm testing have become crucial parts of the autonomous vehicle development cycle. Having access to real-world sensors and vehicles is a dream for researchers and small-scale original equipment manufacturers (OEMs) due to the software and hardware development life-cycle duration and high costs. Therefore, simulator-based virtual testing has gained traction over the years as the preferred testing method due to its low cost, efficiency, and effectiveness in executing a wide range of testing scenarios. Companies like ANSYS and NVIDIA have come up with robust simulators, and open-source simulators such as CARLA have also populated the market. However, there is a lack of lightweight and simple simulators catering to specific test cases. In this paper, we introduce the SLAV-Sim, a lightweight simulator that specifically trains the behaviour of a self-learning autonomous vehicle. This simulator has been created using the Unity engine and provides an end-to-end virtual testing framework for different reinforcement learning (RL) algorithms in a variety of scenarios using camera sensors and raycasts.
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Koopman, Philip, and Michael Wagner. "Challenges in Autonomous Vehicle Testing and Validation." SAE International Journal of Transportation Safety 4, no. 1 (April 5, 2016): 15–24. http://dx.doi.org/10.4271/2016-01-0128.
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J. Mennie, James. "Autonomous Vehicle Technology Examination." Muma Business Review 3 (2019): 193–205. http://dx.doi.org/10.28945/4410.
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Autonomous Vehicle (A/V) technology has been advancing at a rapid pace but the prospect of mass deployment has not been achieved. Inherent expected benefits of this technology such as reduced loss of life, productivity improvements, and greater vehicle efficiency utilization have yet to materialize. The American public’s resistance, and governmental regulation have prevented widespread adoption of this technology that while developed and continuously evolving, is still in its testing phase. Public concerns regarding safety are justified as highly publicized (Lohrmann, 2018) failures of the technology have been reported, heightening public concerns.
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Szalay, Zsolt, Tamás Tettamanti, Domokos Esztergár-Kiss, István Varga, and Cesare Bartolini. "Development of a Test Track for Driverless Cars: Vehicle Design, Track Configuration, and Liability Considerations." Periodica Polytechnica Transportation Engineering 46, no. 1 (March 18, 2017): 29. http://dx.doi.org/10.3311/pptr.10753.
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The Research Center for Autonomous Road Vehicles (RECAR) was founded in 2015 upon the initiative of the Faculty of Transportation Engineering and Vehicle Engineering of Budapest University of Technology and Economics. The research center is supported by industrial partners and other academic partners targeting research and educational purposes. In complement to this project, the construction of a new automotive test track is also under development especially for autonomous road vehicle testing serving as automotive proving ground in Zalaegerszeg, Hungary. Accordingly, an intensive research has been started in RECAR center in the field of autonomous vehicle technology. The paper’s goal is to share the main practical and methodological experiences with the scientific audience as well as the industrial sector. Based on the initial research actions we intend to enlighten the upcoming research challenges of driverless vehicles and automated intelligent transport system. Basically, three main topics are concerned. Firstly, the main issues concerning autonomous vehicle research are summarized. Secondly, the requirements for autonomous test track design are concluded. Thirdly, the legal questions that emerge with the appearance of driverless vehicles are investigated, especially concerning liability.
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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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Moon, Byoung-Joon, and Sung-Jae Ha. "Validation Method for Autonomous Vehicles Using Scenario Analysis and Risk Assessment (SARA)." Korea Industrial Technology Convergence Society 28, no. 3 (September 30, 2023): 107–15. http://dx.doi.org/10.29279/jitr.2023.28.3.107.
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In this study, we propose an autonomous vehicle safety validation method to address the limitations of existing vehicle safety validation methods. Using scenario analysis and risk assessment of the autonomous vehicle safety validation scenario, the proposed method determines the significance of scenario validation, difficulty of scenario implementation, and critical nature of accidents that occur during validation testing. Then, it calculates the autonomous vehicle test level based on these results. Various safety validation tests, such as audits, simulation tests, proving ground tests, and real road tests, were performed according to the calculated level. Based on the validation results, the proposed method enables authorities to request improvement in the original equipment manufacturer address areas and is expected to contribute to providing safe autonomous vehicles to consumers.
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Roseline, Dr S. "Resolving Complexities in the Integration of Autonomous Vehicle." International Scientific Journal of Engineering and Management 03, no. 04 (April 23, 2024): 1–9. http://dx.doi.org/10.55041/isjem01660.
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Smart vehicles become increasingly prevalent, there is a growing need for sophisticated monitoring systems that go beyond basic parameters. Current Smart EV monitoring systems often lack features that address critical aspects such as engine temperature, drunk and drive prevention, and immediate accident response. There is a pressing need for an all-encompassing monitoring system that enhances both vehicle performance and user safety. This project introduces an advanced Smart E-Vehicle Monitoring System designed to offer a comprehensive suite of features, including engine temperature monitoring, drunk and drive testing, accident detection, and remote control via mobile devices. The system not only ensures optimal vehicle performance but also prioritizes user safety through proactive anomaly detection. Keywords: Smart Vehicles, Electric Vehicles, monitoring systems, performance, Engine temperature, mobile devices.
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Li, Peng, Biao Yu, Jun Wang, Xiaojun Zhu, Hui Zhang, Chennian Yu, and Chen Hua. "Generating Realistic Vehicle Trajectories Based on Vehicle–Vehicle and Vehicle–Map Interaction Pattern Learning." World Electric Vehicle Journal 16, no. 3 (March 4, 2025): 145. https://doi.org/10.3390/wevj16030145.
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Diversified and realistic traffic scenarios are a crucial foundation for evaluating the safety of autonomous driving systems in simulations. However, a considerable number of current methods generate scenarios that lack sufficient realism. To address this issue, this paper proposes a vehicle trajectory generation method based on vehicle–vehicle and vehicle–map interaction pattern learning. By leveraging a multihead self-attention mechanism, the model efficiently captures complex dependencies among vehicles, enhancing its ability to learn realistic traffic dynamics. Moreover, the multihead cross-attention mechanism is also used to learn the interaction features between the vehicles and the map, addressing the challenge of trajectory generation’s difficulty in perceiving static environments. This proposed method enhances the model’s ability to learn natural traffic sequences, enable the generation of more realistic traffic flow, and provide strong support for the testing and optimization of autonomous driving systems. Experimental results show that compared to the Trafficgen baseline model, the proposed method achieves a 26% improvement in ADE and a 20% improvement in FDE.
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Tyler, Neil. "A Testing Challenge." New Electronics 52, no. 8 (April 23, 2019): 17–18. http://dx.doi.org/10.12968/s0047-9624(22)61002-2.
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Lowe, Evan, and Levent Guvenc. "General Obstacle Avoidance Capability Assessment for Autonomous Vehicles." Electronics 13, no. 24 (December 12, 2024): 4901. https://doi.org/10.3390/electronics13244901.
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As autonomous vehicle (AV) capabilities expand, it is important to ensure their safety during testing and deployment for public usage. While several testing regulations have been proposed in research, US federal, and even global guidelines for low-speed vehicles in metropolitan settings, regulations for high-speed travel are mainly vacant—this is especially true for regulations relating to AV emergency obstacle avoidance maneuvers (EOAMs). Research in this manuscript introduces a general obstacle avoidance capability assessment (GOACA) for AVs traveling at highway speeds. This GOACA includes test modes including car and bicycle active road objects (AROs) in rural and urban highway settings. These tests were novel in their definitions, methodologies, and execution, especially in the context of AVs driving at highway speeds—critically, this research proposes a test evaluation protocol such that it could serve as a foundation for an official regulation in the future. These GOACA tests included adjacent traffic vehicles which have not been utilized in the prior literature when considering EOAMs within a wholistic AV system context. While the vehicle type will cause simulation results to var, in general, vehicle-to-vehicle (V2V) communication is recommended for usage with AVs at highway speeds for critical safety. This is especially true when considering oncoming traffic and low surface μ conditions.
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Revanth Pathuri. "Shadow Testing in Autonomous Vehicles : A Novel Approach to Validating Full Self-Driving AI Systems." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (November 8, 2024): 308–20. http://dx.doi.org/10.32628/cseit24106165.
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As autonomous vehicles progress towards widespread deployment, ensuring the reliability and safety of Full Self-Driving (FSD) AI systems remains a critical challenge. This article presents a comprehensive analysis of shadow testing, an innovative approach to validating new FSD AI models in real-world conditions without compromising user safety. We examine the methodology of deploying new AI models in "shadow mode," where they process live sensory data alongside the operational system but do not control the vehicle. This approach enables the collection of valuable performance metrics and the identification of edge cases while mitigating risks associated with direct deployment. Our study demonstrates how shadow testing facilitates a continuous feedback loop for iterative improvement, enhancing model reliability through data-driven refinement. We further explore the challenges of implementing shadow testing at scale, including data processing requirements and ethical considerations. Our findings suggest that shadow testing not only accelerates the development of robust FSD systems but also plays a crucial role in building public trust in autonomous vehicle technology. This article contributes to the growing body of knowledge on AI safety validation techniques and provides insights for standardizing testing protocols in the autonomous vehicle industry.
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PARCZEWSKI, Krzysztof, and Kazimierz ROMANISZYN. "The proposition of utilisation the vehicle in 1:5 scale to construction and testing of an autonomous vehicle." Combustion Engines 168, no. 1 (February 1, 2017): 125–28. http://dx.doi.org/10.19206/ce-2017-119.
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In the near future, vehicles will be moving autonomously with little participation or completely without the driver affecting on the vehicle move. Task of driver will be limited to identify the end point of the vehicle route. Vehicles, to meet this challenge, must be equipped with the control systems and supervising the movement of the vehicle, reacting onto the movement of pedestrians and other vehicles, road signs, time of day and weather conditions. The movement of the vehicle will be controlled and driven by a system on board a vehicle, but will also be required to tracking the vehicles position and its movement parameters using of the vehicle satellite navigation systems. In addition, the motion trajectory of the vehicle will be determined and any deviation from the target track will be corrected. The article presents a proposal for determining the trajectory of the vehicle based on the control points specified by the coordinates of the location of the vehicle and ways of correcting the trajectory of the vehicle. For this purpose was utilized the vehicle in scale.
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Issler, Maja, Quentin Goss, and Mustafa İlhan Akbaş. "Complexity Evaluation of Test Scenarios for Autonomous Vehicle Safety Validation Using Information Theory." Information 15, no. 12 (December 3, 2024): 772. https://doi.org/10.3390/info15120772.
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The validation of autonomous vehicles remains a vexing challenge for the automotive industry’s goal of fully autonomous driving. The systematic hierarchization of the test scenarios would provide valuable insights for the development, testing, and verification of autonomous vehicles, enabling nuanced performance evaluations based on scenario complexity. In this paper, an information entropy-based quantification method is proposed to evaluate the complexity of autonomous vehicle validation scenarios. The proposed method addresses the dynamic uncertainties within driving scenarios in a comprehensive way which includes the unpredictability of dynamic agents such as autonomous vehicles, human-driven vehicles, and pedestrians. The numerical complexity calculation of the approach and the ranking of the scenarios are presented through sample scenarios. To automate processes and assist with the calculations, a novel software tool with a user-friendly interface is developed. The performance of the approach is also evaluated through six example driving scenarios, then through extensive simulation using an open-source microscopic traffic simulator. The performance evaluation results confirm the numerical classification and demonstrate the method’s adaptability to diverse scenarios with a comparison of complexity calculation ranking to the ratio of collision, near collision, and normal operation tests observed during simulation testing. The proposed quantification method contributes to the improvement of autonomous vehicle validation procedures by addressing the multifaceted nature of scenario complexities. Beyond advancing the field of validation, the approach also aligns with the broad and active drive of the industry for the widespread deployment of fully autonomous driving.
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Masood, Rana Javed, Wang Dao Bo, Zain Anwar Ali, Suhaib Masroor, and Muhammad Shafiq Loya. "Implementation and Testing of Autonomous Quad-Rotor Aerial Vehicle by Using Ardu-Pilot." International Journal of Information and Electronics Engineering 6, no. 2 (2016): 123–29. http://dx.doi.org/10.18178/ijiee.2016.6.2.608.
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Varga, Balázs, Mátyás Szalai, Árpád Fehér, Szilárd Aradi, and Tamás Tettamanti. "Mixed-reality Automotive Testing with SENSORIS." Periodica Polytechnica Transportation Engineering 48, no. 4 (August 3, 2020): 357–62. http://dx.doi.org/10.3311/pptr.15851.
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Highly automated and autonomous vehicles become more and more widespread changing the classical way of testing and validation. Traditionally, the automotive industry has pursued testing rather in real-world or in pure virtual simulation environments. As a new possibility, mixed-reality testing has also appeared enabling an efficient combination of real and simulated elements of testing. Furthermore, vehicles from different OEMs will have a common interface to communicate with a test system. The paper presents a mixed-reality test framework for visualizing perception sensor feeds real-time in the Unity 3D game engine. Thereby, the digital twin of the tested vehicle and its environment are realized in the simulation. The communication between the sensors of the tested vehicle and the central computer running the test is realized via the standard SENSORIS interface. The paper outlines the hardware and software requirements towards such a system in detail. To show the viability of the system a vehicle in the loop test has been carried out.
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Nozari, Sheida, Ali Krayani, Pablo Marin, Lucio Marcenaro, David Martin Gomez, and Carlo Regazzoni. "Modeling Autonomous Vehicle Responses to Novel Observations Using Hierarchical Cognitive Representations Inspired Active Inference." Computers 13, no. 7 (June 28, 2024): 161. http://dx.doi.org/10.3390/computers13070161.
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Equipping autonomous agents for dynamic interaction and navigation is a significant challenge in intelligent transportation systems. This study aims to address this by implementing a brain-inspired model for decision making in autonomous vehicles. We employ active inference, a Bayesian approach that models decision-making processes similar to the human brain, focusing on the agent’s preferences and the principle of free energy. This approach is combined with imitation learning to enhance the vehicle’s ability to adapt to new observations and make human-like decisions. The research involved developing a multi-modal self-awareness architecture for autonomous driving systems and testing this model in driving scenarios, including abnormal observations. The results demonstrated the model’s effectiveness in enabling the vehicle to make safe decisions, particularly in unobserved or dynamic environments. The study concludes that the integration of active inference with imitation learning significantly improves the performance of autonomous vehicles, offering a promising direction for future developments in intelligent transportation systems.
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Li, Yanfeng, Hsin Guan, Xin Jia, and Chunguang Duan. "Decision-Making Model for Dynamic Scenario Vehicles in Autonomous Driving Simulations." Applied Sciences 13, no. 14 (July 23, 2023): 8515. http://dx.doi.org/10.3390/app13148515.
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A scenario vehicle in autonomous driving simulations is a dynamic entity that is expected to perform trustworthy bidirectional interaction tasks with the autonomous vehicle under test. Modeling interactive behavior can not only facilitate better prediction of human drivers’ intentions and motions but also be valuable in generating more human-like decisions and trajectories for autonomous vehicle testing. However, simulations of most of the available scenario vehicles on existing platforms behave conservatively. This study summarizes five driving motivations based on human-need theories of multiple psychologists, namely safety, dominance, achievement, order, and relatedness, and organizes the framework using a behavior tree. The proposed model generates different driving behaviors by simulating the changing psychological needs of human drivers during vehicle operation. Using a self-developed two-dimensional simulator, experiments were conducted by considering multiple scenarios in urban, rural, and highway road sections. The obtained results indicate that the scenario vehicles controlled by the proposed model exhibit a significant interactive nature, facilitating proactive communication rather than providing simple responses.
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Wu, Chien-Chung, Yu-Cheng Wu, and Yu-Kai Liang. "The Development of an Autonomous Vehicle Training and Verification System for the Purpose of Teaching Experiments." Electronics 12, no. 8 (April 15, 2023): 1874. http://dx.doi.org/10.3390/electronics12081874.
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To cultivate students’ skills in building autonomous vehicle neural network models and to reduce development costs, a system was developed for on-campus training and verification. The system includes (a) autonomous vehicles, (b) test tracks, (c) a data collection and training system, and (d) a test and scoring system. In this system, students can assemble the hardware of the vehicle, configure the software, and choose or modify the neural network model in class. They can then collect the necessary data for the model and train the model. Finally, the system’s test and scoring system can be used to test and verify the performance of the autonomous vehicle. The study found that vehicle turning is better controlled by a motor and steering mechanism, and the camera should be mounted in a high position and at the front of the vehicle to avoid interference with the steering mechanism. Additionally, the study revealed that the training and testing speeds of the autonomous vehicle are dependent on each other, and high-quality results cannot be obtained solely by training a model based on camera images.
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Dremliuga, Roman I., and Andrey A. Yakovenko. "РЕГУЛИРОВАНИЕ ТЕСТИРОВАНИЯ БЕСПИЛОТНОГО АВТОТРАНСПОРТА: ОПЫТ ЕВРОПЫ." Азиатско-Тихоокеанский регион: экономика, политика, право 54, no. 1 (2020): 103–17. http://dx.doi.org/10.24866/1813-3274/2020-1/103-117.
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Развитие технологий автономного автотранспорта является одним из приоритетов в технологическом развитии Российской Федерации. Несмотря на прогресс в данной сфере, вряд ли можно говорить о значимых успехах России во внедрении использования автономного транспорта на дорогах общего пользования. В основе данного исследования лежит утверждение, что правовой режим является либо катализатором, либо замедляющим фактором развития сферы автономного автотранспорта. Цель исследования – провести анализ правовых норм в сфере тестирования и эксплуатации автономного автотранспорта в Европе. Авторы полагают, что исследуемые европейские страны имеют достаточно проработанную нормативную базу в данной сфере, поэтому их опыт может быть полезен в разработке и модификации российской правовой базы для регулирования отношений в сфере использования автономного автотранспорта. Задачи исследования: а) определить нормативную базу, связанную с регулированием отношений по тестированию и эксплуатации автономных транспортных средств; б) выявить позицию лидирующих европейских стран, связанную с вопросами необходимости регулирования таких отношений; в) оценить перспективы возможности заимствования положительной практики регулирования рассматриваемой сферы общественных отношений в правовую действительность Российской Федерации. Методологической основой исследования послужили диалектический метод познания, общенаучные методы абстрагирования, анализа и синтеза, а также специальные юридические методы (сравнительно-правовой, логико-юридический и др.). В статье делается вывод, что в процессе создания нормативно правовой базы власти стремятся детально урегулировать исследуемую область общественных отношений. Рассмотренные страны имеют практически схожую модель регулирования, с жёсткими нормативными рамками. Авторы полагают, что некоторые ограничения являются неблагоприятными для развития сектора цифровой экономики. Тем не менее, опыт европейских стран может быть использован в России в первую очередь для определения проблемных точек регулирования и организации контроля за тестированием и использованием автономного автотранспорта. Ключевые слова: Европейский Союз, регулирование тестирования автономного автотранспорта, беспилотный автотранспорт, цифровая экономика, искусственный интеллект, кибербезопасность, распознавание образов, киберправо, беспилотные транспортные средства, цифровая трансформации, умные вещи, умный город, сравнительное правоведение, сравнительный анализ, новый технологический уклад, право Нидерландов, право Германии, право Великобритании, право информационных технологий, право ИТ. The development of unmanned (autonomous) vehicle technologies is one of the strategic priorities of the Russian Federation. Despite a great deal of attention to this area, one can hardly speak of any significant success of Russia in introducing the use of autonomous vehicles on public roads. This study is based on the assumption that the legal regime is a catalyst or a deterrent to the development of autonomous motor transport or a deterrent to the development of the autonomous motor transport sector. The aim of the study is to analyze the legal norms in the field of testing and operation of autonomous road transport in Europe. The authors believe that the studied European countries have a sufficiently developed regulatory system in this area; therefore, their experience can be useful in developing and modifying the Russian legal framework to regulate the use of autonomous road transport. The objectives of the study are as follows: a) to define the regulatory framework related to the regulation of relations in the testing and operation of autonomous vehicles; b) to identify the position of the leading European countries related to the issues of the need for regulation and its limits; c) to assess the prospects for the possibility of taking the positive practice of regulation of the considered sphere of social relations into the legal reality of the Russian Federation. The dialectical method of cognition, general scientific methods of abstraction, analysis and synthesis, as well as special legal methods (comparative legal, logical-legal, etc.) served as the methodological basis for the research. The article concludes that in the process of creating a legal system the authorities seek to regulate in detail the area of public relations under study. The countries under study have almost similar model of regulation with rigid normative frameworks. The authors believe that some restrictions are unfavorable for the development of digital economy sectors. Nevertheless, the experience of European countries can be used in Russia first of all to identify problem points of regulation and organization of control over testing and use of autonomous motor vehicles.
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Lubal, Y. S., Ajinkya Pramod Purekar, Shriya Sanjay Pattanshetti, and Ankita Ashok Waghmode. "Revolutionizing Roads: The Impact of Autonomous Vehicles on Future Travel." Journal of Big Data Analytics and Business Intelligence 1, no. 3 (September 23, 2024): 1–9. http://dx.doi.org/10.46610/jobdabi.2024.v01i03.001.
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This project focuses on developing an autonomous self-driving car by integrating advanced algorithms with cutting-edge hardware. The vehicle is designed to perceive its environment, make informed decisions, and navigate complex traffic scenarios autonomously by leveraging machine learning and computer vision technologies. Essential methods include using deep learning algorithms for object detection and classification, sensor fusion techniques to combine data from cameras, LiDAR, and radar, and reinforcement learning for decision-making in dynamic environments. The project emphasizes the critical role of AI in enabling the car to interpret and respond to dynamic road conditions while ensuring reliable performance through rigorous testing and validation in simulated and real-world scenarios. Initial results from testing indicate that the car can successfully navigate complex traffic situations, such as merging onto highways and responding to pedestrians, with a high degree of accuracy. By pushing the boundaries of autonomous vehicle technology, this research aspires to contribute to the ongoing evolution of self-driving cars and their potential to revolutionize mobility. With a focus on safety, efficiency, and innovation, this endeavor represents a significant step forward in developing autonomous vehicles, offering new possibilities for the future of transportation. The project promises to improve travel convenience and significantly reduce traffic accidents, ultimately contributing to a safer and more efficient road system.
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Wu, Weiting. "Progress made in researching autonomous vehicle systems based on BeiDou navigation." Highlights in Science, Engineering and Technology 119 (December 11, 2024): 859–66. https://doi.org/10.54097/w5069a56.
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The evolution of smart transport systems has made autonomous vehicles a central focus for research. The BeiDou navigation system's highly accurate location data is vital for environmental detection, autonomous vehicle decision-making, and navigation. This research aims to develop and actualize a system for autonomous vehicles to plan routes and evade obstacles, utilizing the BeiDou satellite navigational framework. Consequently, this document introduces an algorithm for planning paths and avoiding obstacles that integrate BeiDou navigation data, capable of adjusting to intricate traffic conditions and enhancing the safety and efficiency of autonomous vehicles. Verification of the suggested algorithm was conducted on the environment simulation platform within the ROS operating system, revealing the algorithm's capability to create the ideal path and successfully evade dynamic obstacles. The practical vehicle testing verifies the algorithm's practicality and steadiness in traffic situations. Ultimately, the document encapsulates the study's findings and outlines prospective research avenues, encompassing the acquisition of both image and depth information, along with conducting image recognition and car tracking.
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Landersheim, Volker, Matthias Jurisch, Riccardo Bartolozzi, Georg Stoll, Riccardo Möller, and Heiko Atzrodt. "Simulation-Based Testing of Subsystems for Autonomous Vehicles at the Example of an Active Suspension Control System." Electronics 11, no. 9 (May 3, 2022): 1469. http://dx.doi.org/10.3390/electronics11091469.
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Automated driving functions are expected to increase both the safety and ride comfort of future vehicles. Ensuring their functional safety and optimizing their performance requires thorough testing. Costs and duration of tests can be reduced if more tests can be performed numerically in a feasible simulation framework. This simulation setup must include all subsystems of the autonomous vehicle, which significantly interact with the system under test. In this paper, a modular model chain is presented, which is developed for testing systems with an impact on vehicle motion. It includes trajectory planning, motion control, and a model of the vehicle dynamics in a closed loop. Each subsystem can easily be exchanged to adapt the model chain with respect to the simulation objectives. As a use case, the testing of an active suspension control system is discussed. It is designed directly for use in autonomous cars and uses inputs from the vehicle motion planning subsystem for planning the suspension actuator motion. Using the presented closed-loop model chain, the effect of different actuator control strategies on ride comfort is compared, such as curve tilting. Furthermore, the impact of the active suspension system on lateral vehicle motion is shown.
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Tarulescu, Radu, Ciprian Olteanu, and Sorin Zamfira. "Variation of Start Acceleration for Wheeled and Tracked Autonomous Mini-Vehicles." Applied Mechanics and Materials 822 (January 2016): 299–304. http://dx.doi.org/10.4028/www.scientific.net/amm.822.299.
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In this paper is presented a comparison between the obtained start-acceleration of a vehicle with wheels and one tracked. Experiments performed in this regard were conducted on three types of surfaces like parquet, carpet and sand. The vehicles chosen for testing were a remote-controlled mini-vehicle (wheeled locomotion system) and Spy Video TRAKR (locomotion system tracked). In order to determine the acceleration, the vehicles were equipped with ultrasonic sensor DT020-1 and data acquisition system MultiLogPRO, running connected to a PC or as a standalone device.
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Mahmoud, Yaqub, Yuichi Okuyama, Tomohide Fukuchi, Tanaka Kosuke, and Iori Ando. "Optimizing Deep-Neural-Network-Driven Autonomous Race Car Using Image Scaling." SHS Web of Conferences 77 (2020): 04002. http://dx.doi.org/10.1051/shsconf/20207704002.
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In this work we propose scaling down the image resolution of an autonomous vehicle and measuring the performance difference using pre-determined metrics. We formulated a testing strategy and provided suitable testing metrics for RC driven autonomous vehicles. Our goal is to measure and prove that scaling down an image will result in faster response time and higher speeds. Our model shows an increase in response rate of the neural models, improving safety and results in the car having higher speeds.
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Saputro, Joko Slamet, Miftahul Anwar, Feri Adriyanto, Agus Ramelan, Putra Maulana Yusuf, Fakih Irsyadi, Rendra Dwi Firmansyah, and Tri Wahyu Oktaviana Putri. "Design of intelligent cruise control system using fuzzy-PID control on autonomous electric vehicles prototypes." Journal of Mechatronics, Electrical Power, and Vehicular Technology 15, no. 1 (July 31, 2024): 105–16. http://dx.doi.org/10.55981/j.mev.2024.877.
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Electric vehicles provide a solution for using alternative fuels, namely, electricity. Electric vehicles are used for short distances and intercity travel over long distances, increasing the risk of accidents. Cruise Control is a technology embedded in vehicles to maintain stable speeds; this system will automatically adjust the vehicle's speed when motion changes cause changes in vehicle speed. This study aims to apply lidar sensors to detect distance in the Intelligent Cruise Control (ICC) system using the Fuzzy-PID control method. Testing results were obtained at safe distance inputs of 5, 6, and 7 meters with various object distances. All the tests were carried out; the response systems were obtained with an average settling time of 5 seconds and an average overshoot of 1.53%. Therefore, the proposed Fuzzy-PID method works well for controlling Intelligent Cruise Control systems in autonomous electric vehicle prototypes.
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Silva, Ivo, Hélder Silva, Fabricio Botelho, and Cristiano Pendão. "Realistic 3D Simulators for Automotive: A Review of Main Applications and Features." Sensors 24, no. 18 (September 10, 2024): 5880. http://dx.doi.org/10.3390/s24185880.
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Recent advancements in vehicle technology have stimulated innovation across the automotive sector, from Advanced Driver Assistance Systems (ADAS) to autonomous driving and motorsport applications. Modern vehicles, equipped with sensors for perception, localization, navigation, and actuators for autonomous driving, generate vast amounts of data used for training and evaluating autonomous systems. Real-world testing is essential for validation but is complex, expensive, and time-intensive, requiring multiple vehicles and reference systems. To address these challenges, computer graphics-based simulators offer a compelling solution by providing high-fidelity 3D environments to simulate vehicles and road users. These simulators are crucial for developing, validating, and testing ADAS, autonomous driving systems, and cooperative driving systems, and enhancing vehicle performance and driver training in motorsport. This paper reviews computer graphics-based simulators tailored for automotive applications. It begins with an overview of their applications and analyzes their key features. Additionally, this paper compares five open-source (CARLA, AirSim, LGSVL, AWSIM, and DeepDrive) and ten commercial simulators. Our findings indicate that open-source simulators are best for the research community, offering realistic 3D environments, multiple sensor support, APIs, co-simulation, and community support. Conversely, commercial simulators, while less extensible, provide a broader set of features and solutions.
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Betts, Kevin M., and Mikel D. Petty. "Automated Search-Based Robustness Testing for Autonomous Vehicle Software." Modelling and Simulation in Engineering 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/5309348.
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Autonomous systems must successfully operate in complex time-varying spatial environments even when dealing with system faults that may occur during a mission. Consequently, evaluating the robustness, or ability to operate correctly under unexpected conditions, of autonomous vehicle control software is an increasingly important issue in software testing. New methods to automatically generate test cases for robustness testing of autonomous vehicle control software in closed-loop simulation are needed. Search-based testing techniques were used to automatically generate test cases, consisting of initial conditions and fault sequences, intended to challenge the control software more than test cases generated using current methods. Two different search-based testing methods, genetic algorithms and surrogate-based optimization, were used to generate test cases for a simulated unmanned aerial vehicle attempting to fly through an entryway. The effectiveness of the search-based methods in generating challenging test cases was compared to both a truth reference (full combinatorial testing) and the method most commonly used today (Monte Carlo testing). The search-based testing techniques demonstrated better performance than Monte Carlo testing for both of the test case generation performance metrics: (1) finding the single most challenging test case and (2) finding the set of fifty test cases with the highest mean degree of challenge.
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Betz, Johannes, Venkat Krovi, Rahul Mangharam, and Madhur Behl. "Editorial: Special Issue on Opportunities and Challenges with Autonomous Racing." Field Robotics 4, no. 1 (January 10, 2024): 246–48. http://dx.doi.org/10.55417/fr.2024008.
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In the ever-evolving landscape of autonomous vehicles, competition and research of high-speed autonomous racing emerged as a captivating frontier, pushing the limits of perception, planning, and control. Autonomous racing presents a setup where the intersection of cutting-edge software and hardware development sparks unprecedented opportunities and confronts unique challenges. The motorsport axiom, “If everything seems under control, then you are not going fast enough,” resonates in this special issue, underscoring the demand for algorithms and hardware that can navigate at the cutting edge of control, traction, and agility. In pursuing autonomy at high speeds, the racing environment becomes a crucible, pushing autonomous vehicles to execute split-second decisions with high precision. Autonomous racing, we believe, offers a litmus test for the true capabilities of self-driving software. Just as racing has historically served as a proving ground for automotive technology, autonomous racing now presents itself as the crucible for testing self-driving algorithms. While routine driving situations dominate much of the autonomous vehicle operations, focusing on extreme situations and environments is crucial to support investigation into safety benefits. The urgency of advancing highspeed autonomy is palpable in burgeoning autonomous racing competitions like Formula Student Driverless, F1TENTH autonomous racing, Roborace, and the Indy Autonomous Challenge. These arenas provide a literal testbed for testing perception, planning, and control algorithms and symbolize the accelerating traction of autonomous racing as a proving ground for agile and safe autonomy. Our special issue focuses on cutting-edge research into software and hardware solutions for highspeed autonomous racing. We sought contributions from the robotics and autonomy communities that delve into the intricacies of head-to-head multi-agent racing: modeling vehicle dynamics at high speeds, developing advanced perception, planning, and control algorithms, as well as the demonstration of algorithms, in simulation and in real-world vehicles. While presenting recent developments for autonomous racing, we believe these special issue papers will also create an impact in the broader realm of autonomous vehicles.
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Pisarov, Jelena, and Gyula Mester. "The use of autonomous vehicles in transportation." Tehnika 76, no. 2 (2021): 171–77. http://dx.doi.org/10.5937/tehnika2102171p.
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The objective of this paper is to examine the types of autonomous vehicles already employed in transportation. It exemplifies the segments of transportation which already use autonomous vehicles. This paper gives an insight of the places in the world where autonomous vehicles are put in use. Furthermore, companies such as Volvo, Volkswagen, Airbus and WAYMO have showed their models of driverless vehicles ranging from trucks, buses, shuttles and helicopters. Moreover, the paper provides a description, testing methods, use and features of the each vehicle presented. In short, this paper aims to raise consciousness about the future of not just public transportation but also transportation of any other type such as good's transportation, delivery, vehicles provided for sightseeing, aircrafts etc.
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Zhang, Zikai. "Exploring rounD Dataset for Domain Generalization in Autonomous Vehicle Trajectory Prediction." Sensors 24, no. 23 (November 26, 2024): 7538. http://dx.doi.org/10.3390/s24237538.
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This paper analyzes the rounD dataset to advance motion forecasting algorithms for autonomous vehicles navigating complex roundabout environments. We develop a trajectory prediction framework inspired by Gated Recurrent Unit (GRU) networks and graph-based modules to effectively model vehicle interactions. Our primary objective is to evaluate the generalizability of the proposed model across diverse training and testing datasets. Through extensive experiments, we investigate how varying data distributions—such as different road configurations and recording times—impact the model’s prediction accuracy and robustness. This study provides key insights into the challenges of domain generalization in autonomous vehicle trajectory prediction.
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Cormell, Darren. "A new tool in the subsea industry: the Autonomous Inspection Vehicle (AIV)." APPEA Journal 52, no. 2 (2012): 659. http://dx.doi.org/10.1071/aj11073.
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A new tool for subsea inspection in the offshore oil and gas industry is now going through performance and qualification testing. The Autonomous Inspection Vehicle (AIV) has been designed and built by Subsea7 and SeeByte Ltd to provide the industry with a valuable tool capable of making a positive contribution to life-of-field operations. The station keeping and hovering ability of the AIV is the next step in the evolution of autonomous systems in the marine environment. Survey class autonomous vehicles have already shown their value with improved data quality and efficiencies compared with traditional methods. The first commercial AIV will be capable of many of the inspection tasks presently carried out by remotely operated vehicles (ROV). Regular inspection data of risers, pipelines, and seabed equipment can be gathered using a single AIV operating directly from an offshore facility. A more rapid assessment of a field can be made using multiple systems operating together from a single support vessel. This has not been done before with a commercial vehicle; hence, AIV is leading-edge technology. This extended abstract outlines some of the technical challenges in creating the vehicle and how the use of advanced simulation linked to practical testing is being used to ensure the performance of the system. Also discussed is a parallel with the evolution of subsea infrastructure that has fully enabled the capability of the ROV; how the introduction of autonomous technology should be considered with confidence is also demonstrated.
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Tejada, Juan C., Alejandro Toro-Ossaba, Santiago Muñoz Montoya, and Santiago Rúa. "A Systems Engineering Approach for the Design of an Omnidirectional Autonomous Guided Vehicle (AGV) Testing Prototype." Journal of Robotics 2022 (March 21, 2022): 1–13. http://dx.doi.org/10.1155/2022/7712312.
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This paper addresses the mechanical and electrical design of an autonomous guided vehicle (AGV) test prototype based on a systems engineering approach. First, the different phases of the systems engineering approach are described. The conceptual design begins with the house of quality, which weighs the relevance of each user requirement and ends with a functional representation of the vehicle. Then, the mechanical and electrical design are presented considering different subsystems such as the chassis, cargo platform, suspension system, power, and control components. Finally, different tests were carried out on the prototype, validating its movement and load capacities. The systems engineering approach as a methodology for the construction of complex systems has proven to be an excellent tool for the development of autonomous guided vehicles.
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Moukahal, Lama J., Mohammad Zulkernine, and Martin Soukup. "Vulnerability-Oriented Fuzz Testing for Connected Autonomous Vehicle Systems." IEEE Transactions on Reliability 70, no. 4 (December 2021): 1422–37. http://dx.doi.org/10.1109/tr.2021.3112538.
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Kim, Baekgyu, Yusuke Kashiba, Siyuan Dai, and Shinichi Shiraishi. "Testing Autonomous Vehicle Software in the Virtual Prototyping Environment." IEEE Embedded Systems Letters 9, no. 1 (March 2017): 5–8. http://dx.doi.org/10.1109/les.2016.2644619.
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Lv, Chen, Dongpu Cao, Yifan Zhao, Daniel J. Auger, Mark Sullman, Huaji Wang, Laura Millen Dutka, Lee Skrypchuk, and Alexandros Mouzakitis. "Analysis of autopilot disengagements occurring during autonomous vehicle testing." IEEE/CAA Journal of Automatica Sinica 5, no. 1 (January 2018): 58–68. http://dx.doi.org/10.1109/jas.2017.7510745.
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Yang, Lan, Songyan Liu, Shuo Feng, Hong Wang, Xiangmo Zhao, Guangyue Qu, and Shan Fang. "Generation of critical pedestrian scenarios for autonomous vehicle testing." Accident Analysis & Prevention 214 (May 2025): 107962. https://doi.org/10.1016/j.aap.2025.107962.
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