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Decision-Making for Intelligent Vehicle Considering Uncertainty of Road Adhesion Coefficient Estimation: Autonomous Emergency Braking Case
Technical Paper
2020-01-5109
ISSN: 0148-7191, e-ISSN: 2688-3627
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Automotive Technical Papers
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English
Abstract
Since data processing methods could not completely eliminate the uncertainty of signals, it is a key issue for stable and robust decision-making for uncertainty tolerance of intelligent vehicles. In this paper, a decision-making for an Autonomous Emergency Braking (AEB) case considering the uncertainty of road adhesion coefficient estimation (RACE) is proposed. Firstly, the 3σ criterion is employed to classify the confidence in order to establish the decision-making mechanism considering the signal uncertainty of RACE. Secondly, the model for AEB with the uncertainty of the road adhesion coefficient estimated is designed based on the Seungwuk Moon model. Thirdly, a CCRs and CCRm scenario was designed to verify the feasibility in reference to the European New Car Assessment Programme (Euro NCAP) standard. Finally, the results of 10,000 cycles test illustrate that the proposed method is stable and could significantly improve the safety confidence both in the CCRs and CCRm scenarios.
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Xiong, L., Qi, Y., Yan, D., Zhang, D. et al., "Decision-Making for Intelligent Vehicle Considering Uncertainty of Road Adhesion Coefficient Estimation: Autonomous Emergency Braking Case," SAE Technical Paper 2020-01-5109, 2020, https://doi.org/10.4271/2020-01-5109.Data Sets - Support Documents
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References
- Paden , B. , Ros , G. , Codevilla , F. , Lopez , A. et al. A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles IEEE Transactions on Intelligent Vehicles 1 1 33 55 2016
- González , D. , Pérez , J. , Milanés , V. , and Nashashibi , F. A Review of Motion Planning Techniques for Automated Vehicles IEEE Trans. Intelligent Transportation Systems 17 4 1135 1145 2016
- Katrakazas , C. , Quddus , M. , Chen , W.H. , and Deka , L. Real-Time Motion Planning Methods for Autonomous On-Road Driving: State-of-the-Art and Future Research Directions Transportation Research Part C: Emerging Technologies 60 416 442 2015
- Veres , S.M. , Molnar , L. , Lincoln , N.K. et al. Autonomous Vehicle Control Systems—A Review of Decision Making Proceedings of the Institution of Mechanical Engineers 225 I2 155 195 2011
- Zhang , P. , He , K. , Ou Yang , Z. , and Zhang , J. Multifunctional Intelligent Outdoor Mobile Robot Testbed-THMR-V Robot 24 2 97 101 2002
- Patz , B.J. , Papelis , Y. , Pillat , R. et al. A Practical Approach to Robotic Design for the DARPA Urban Challenge Journal of Field Robotics 25 8 528 566 2008
- Talebpour , A. , Mahmassani , H.S. , and Hamdar , S.H. Modeling Lane-Changing Behavior in a Connected Environment: A Game Theory Approach Transportation Research Part C 59 216 232 2015
- Xiong , G. , Li , Y. , and Wang , S. Behavior Prediction and Control Method Based on FSM for Intelligent Vehicles in an Intersection Transactions of Bjing Institute of Technology 35 1 34 38 2015
- Urmson , C. , Anhalt , J. , Bagnell , D. et al. Autonomous Driving in Urban Environments: Boss and the Urban Challenge Journal of Field Robotics 25 8 425 466 2008
- Bacha , A. , Bauman , C. , Faruque , R. et al. Odin: Team VictorTango’s Entry in the DARPA Urban Challenge Journal of Field Robotics 25 8 467 492 2008
- Leonard , J. , How , J. , Teller , S. et al. A Perception-Driven Autonomous Urban Vehicle Journal of Field Robotics 25 10 727 774 2008
- Ziegler , J. , Bender , P. , Schreiber , M. et al. Making Bertha Drive—An Autonomous Journey on a Historic Route IEEE Intelligent Transportation Systems Magazine 6 2 8 20 2015
- Du , M. 2016 42 56
- Ross , S. , Melik-Barkhudarov , N. , Shankar , K.S. et al. Learning Monocular Reactive UAV Control in Cluttered Natural Environments IEEE International Conference on Robotics and Automation Karlsruhe 2013 1765 1772
- Santana , E. and Hotz , G. 2016 1 8 https://ui.adsabs.harvard.edu/abs/2016arXiv160801230S
- Palanisamy , P. 2019 1 16 https://ui.adsabs.harvard.edu/abs/2019arXiv191104175P
- Gibbs , S. Google Sibling Waymo Launches Fully Autonomous Ride-Hailing Service The Guardian 7 2017
- Xu , J. , Luo , Q. , Xu , K. et al. An Automated Learning-Based Procedure for Large-Scale Vehicle Dynamics Modeling on Baidu Apollo Platform 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Macau, China 2019 5049 5056
- Yu , Z. , Chen , W. , and Zhu , C. Design of Unmanned Park Vehicle Decision-Making System Based on Uncertainty Information IFAC-PapersOnLine 51 31 924 929 2018
- Seungwuk , M. , and Kyongsu , Y. Human Driving Data-Based Design of a Vehicle Adaptive Cruise Control Algorithm Vehicle System Dynamics 46 8 661 690 2008
- Koskinen , S. Sensor Data Fusion Based Estimation of Tyre-Road Friction to Enhance Collision Avoidance Espoo VTT Publications 2010 730
- Gao , X. , Yu , Z. , Neubeck , J. et al. Sideslip Angle Estimation Based on Input—Output Linearisation with Tire—Road Friction Adaptation Vehicle System Dynamics 48 2 217 234 2010
- Li , L. , Yang , K. , Jia , G. et al. Comprehensive Tire-Road Friction Coefficient Estimation Based on Signal Fusion Method under Complex Maneuvering Operations Mechanical Systems & Signal Processing 56-57 259 276 2015
- Li , S. Study on the Estimation of Vehicle Dynamic State and Road Adhesion Coefficient Shandong University of Technology 2016 48 65