Multi-Step Continuous Decision Making and Planning in Uncertain Dynamic Scenarios Through Parallel Spatio-Temporal Trajectory Searching

被引:0
作者
Li, Delun [1 ]
Cheng, Siyuan [2 ]
Yang, Shaoyu [2 ]
Huang, Wenchao [1 ]
Song, Wenjie [1 ]
机构
[1] Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China
[2] Huawei Technol Co Ltd, Noahs Ark Lab, Planning & Control Grp, Beijing 100081, Peoples R China
来源
IEEE ROBOTICS AND AUTOMATION LETTERS | 2024年 / 9卷 / 10期
关键词
Trajectory; Vehicle dynamics; Planning; Decision making; Uncertainty; Roads; Pedestrians; Autonomous vehicle navigation; intelligent transportation systems; motion and path planning; VEHICLES; STRATEGY;
D O I
10.1109/LRA.2024.3443495
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
Autonomous driving in urban scenarios faces uncertain dynamic changes, especially in China, where a dense mixture of cars, cyclists and pedestrians travel together on roads with random uncertain behaviors and high-risk road crossing. This letter proposes a Multi-step Continuous Decision Making and Spatio-temporal Trajectory Planning framework to achieve stable continuous decision making and high-quality trajectory planning in such uncertain and highly dynamic environments. Firstly, a 3D spatio-temporal probabilistic map is constructed to represent the uncertain future driving environment. Based on the map, parallel spatio-temporal trajectory search is performed to obtain multi-strategy feasible spatio-temporal trajectories that satisfy the short-term deterministic and long-term uncertain environmental constraints. Then considering the continuity and consistency of decision making, risk-aware rolling-fusion of trajectory sequences is proposed, achieving efficient and exploratory far-end planning with a stable and safe near-end driving trajectory. To validate the proposed framework, we collected the Hard Case data from real Chinese urban roads, containing challenging scenarios such as dense traffic flows, mixed vehicle-pedestrian roads, and complex intersections, which are widely recognized barriers to the successful real-world deployment of autonomous driving. Moreover, the SMARTS simulator is used to build closed-loop simulation scenarios to verify the effectiveness of the framework. Experimental results show the superior performance of our proposed framework in complex uncertain dynamic scenarios.
引用
收藏
页码:8282 / 8289
页数:8
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