GRRL: Goal-Guided Risk-Inspired Reinforcement Learning for Efficient Autonomous Driving in Off-Road Environment

Yang Chen, Rongchuan Wang, Yi Qu, Chongshang Yan, Wenjie Song

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

When unmanned ground vehicles (UGVs) are deployed for rescue, pathfinding, and other tasks in off-road scenarios, existing conventional path planners often fail to generate both feasible and efficient trajectories and also require high computational resources. To address these problems, this article introduces a goal-guided, risk-inspired reinforcement learning framework for efficient autonomous driving in off-road environments. This tightly coupled navigation framework integrates a perception network based on a Transformer feature extractor with an actor-critic reinforcement learning network. First, terrain risk maps are constructed from point clouds and converted into bird’s-eye view risk images as the input of the learning framework. Then, terrain risk features and goal information are combined to generate safe actions for the UGV to move to the goal in off-road environments. Moreover, a joint reward function consisting of goal heuristic and driving feasibility is also designed to ensure both the efficiency and safety of the driving process. Sufficient experiments were carried out on simulated off-road terrains to verify the effectiveness of the proposed system. Experiment results show that, compared with other methods, our method achieves a higher success rate and enhances the UGV’s risk avoidance ability in off-road scenarios.

Original languageEnglish
Title of host publicationProceedings - 2024 China Automation Congress, CAC 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2496-2501
Number of pages6
ISBN (Electronic)9798350368604
DOIs
Publication statusPublished - 2024
Event2024 China Automation Congress, CAC 2024 - Qingdao, China
Duration: 1 Nov 20243 Nov 2024

Publication series

NameProceedings - 2024 China Automation Congress, CAC 2024

Conference

Conference2024 China Automation Congress, CAC 2024
Country/TerritoryChina
CityQingdao
Period1/11/243/11/24

Keywords

  • Autonomous driving
  • deep reinforcement learning
  • off-road path planning

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