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

Abstract

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.