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Pattern Recognition and Artificial Intelligence  2023, Vol. 36 Issue (1): 1-21    DOI: 10.16451/j.cnki.issn1003-6059.202301001
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Motion Planning under Uncertainty for Autonomous Driving:Opportunities and Challenges
ZHANG Xiaotong1, WANG Jiacheng1, HE Jingtao1, CHEN Shitao1, ZHENG Nanning1
1. Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049

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Abstract  

Motion planning algorithm, as an important part of autonomous driving systems, draws increasing attention from researchers. However, most existing motion planning algorithms only consider their application in deterministic structured environments, neglecting potential uncertainties in dynamic traffic environments. In this paper, motion planning algorithms are divided into two categories for the uncertain environment: partially observable Markov decision process and probability occupancy grid map.The two categories are introduced for three aspects: theoretical foundation, solution algorithm and practical application. The strategy with the maximum discounted reward in the future is calculated by partially observable Markov decision process based on the current confidence state. Probability occupancy grid map utilizes probability to represent the occupancy status of corresponding grids, measuring the possibility of dynamic changes in traffic flow, and well representing the uncertainty. Finally, the main challenges and future research directions for motion planning in uncertain environments are summarized .

Key wordsAutonomous Driving      Motion Planning      Partially Observable Markov Decision Process(POMDP)      Probability Occupancy Grid Map(POGM)     
Received: 20 December 2022     
ZTFLH: TP181  
Corresponding Authors: ZHENG Nanning, Ph.D., professor. His research interests include computer vision and pattern recognition.   
About author:: ZHANG Xiaotong, master student. Her research interests include motion planning for autonomous vehicles.WANG Jiacheng, master student. His research interests include path planning for autonomous vehicles.HE Jingtao, master student. His research interests include autonomous navigation based on reinforcement learning.CHEN Shitao, Ph.D., assistant professor. His research interests include driveless vehicles.
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ZHANG Xiaotong
WANG Jiacheng
HE Jingtao
CHEN Shitao
ZHENG Nanning
Cite this article:   
ZHANG Xiaotong,WANG Jiacheng,HE Jingtao等. Motion Planning under Uncertainty for Autonomous Driving:Opportunities and Challenges[J]. Pattern Recognition and Artificial Intelligence, 2023, 36(1): 1-21.
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