Abstract
Autonomous vehicles sense the surrounding environment through various sensors and make behavior decisions based on real-time perception information to change their vehicle’s motion state. Most existing studies on behavior use single data, high computational complexity, and single optimization criteria only, which lacks practicality. This work proposes an autonomous vehicle motion behavior decision method. It first extracts the corresponding features according to correlation among adjacent vehicles and predicts driving behavior and trajectory of adjacent vehicles. Then, it abstracts driving states of autonomous vehicles, introduces their state transition process based on a definite state machine, and gives a behavior decision method. Finally, a multi-objective optimization algorithm is used to optimize. Extensive simulation results show that this method can effectively improve the safety, efficiency, and practicability of autonomous vehicle motion behavior decision.
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Acknowledgements
This work was supported in part by the NSFC under Grant 61872271 and 62272344, in part by the Open Foundation of State Key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications) under Grant SKLNST-2020-1-20.
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Cheng, J., Xiong, Y., Feng, S., Yuan, G., Mao, Q., Lu, B. (2022). A Behavior Decision Method for Autonomous Vehicles in an Urban Scene. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13471. Springer, Cham. https://doi.org/10.1007/978-3-031-19208-1_15
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DOI: https://doi.org/10.1007/978-3-031-19208-1_15
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