Motion Planning under Uncertainty for Autonomous Driving: Opportunities and Challenges

被引：0
作者：
Zhang X. [1 ]
Wang J. [1 ]
He J. [1 ]
Chen S. [1 ]
Zheng N. [1 ]
机构：
[1] Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2023年 / 36卷 / 01期
关键词：
Autonomous Driving; Motion Planning; Partially Observable Markov Decision Process (POMDP); Probability Occupancy Grid Map(POGM);
D O I：
10.16451/j.cnki.issn1003-6059.202301001
中图分类号：
学科分类号：
摘要：
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 . © 2023 Journal of Pattern Recognition and Artificial Intelligence. All rights reserved.
引用
收藏
页码：1 / 21
页数：20
相关论文
共 121 条
  • [1] THRUN S, MONTEMERLO M, DAHLKAMP H, Et al., Stanley: The Robot That Won the DARPA Grand Challenge, The 2005 DARPA Grand Challenge, pp. 1-43, (2007)
  • [2] QIN Z Y, BAI W L, JIA N., Analysis of Technical Path of Intelligent Networked Vehicle Enterprises, Automobile and Parts, 9, pp. 31-33, (2019)
  • [3] PALMEIRO A R, VAN DER KINT S, VISSERS L, Et al., Interaction between Pedestrians and Automated Vehicles: A Wizard of Oz Experiment, Transportation Research Part F(Traffic Psychology and Behaviour), 58, pp. 1005-1020, (2018)
  • [4] JUN M, CHAUDHRY A I, ANDREA R D., The Navigation of Autonomous Vehicles in Uncertain Dynamic Environments: A Case Study, Proc of the 41st IEEE Conference on Decision and Control, pp. 3770-3775, (2002)
  • [5] KIBALOV V, SHIPITKO O., Safe Speed Control and Collision Probability Estimation Under Ego-Pose Uncertainty for Autonomous Vehicle, Proc of the 23rd IEEE International Conference on Intelligent Transportation Systems, (2020)
  • [6] BERNSTEIN D S, GIVAN R, IMMERMAN N, Et al., The Complexity of Decentralized Control of Markov Decision Processes, Mathematics of Operations Research, 27, 4, pp. 819-840, (2002)
  • [7] FANG J H., Research on Point-Based Value Iteration Algorithm in POMDP Domains, (2015)
  • [8] PINEAU J, GORDON G, THRUN S., Point-Based Value Iteration: An Anytime Algorithm for POMDPs, Proc of the 18th International Joint Conference on Artificial Intelligence, pp. 1025-1030, (2003)
  • [9] BAI H Y, CAI S J, YE N, Et al., Intention-Aware Online POMDP Planning for Autonomous Driving in a Crowd, Proc of the IEEE International Conference on Robotics and Automation, pp. 454-460, (2015)
  • [10] LUO Y F, CAI P P, BERA A, Et al., PORCA: Modeling and Planning for Autonomous Driving Among Many Pedestrians, IEEE Robotics and Automation Letters, 3, 4, pp. 3418-3425, (2018)
12345678910