Hi-Drive Paper Accepted by IEEE Robotics and Automation Letters (RA-L)

🎊 Excellent news! We are delighted to announce that our paper “Hi-Drive: Hierarchical POMDP Planning for Safe Autonomous Driving in Diverse Urban Environments” has been accepted for publication in IEEE Robotics and Automation Letters (RA-L).

Paper Details

Title: Hi-Drive: Hierarchical POMDP Planning for Safe Autonomous Driving in Diverse Urban Environments

Authors: Xuanjin Jin, Chendong Zeng, Shengfa Zhu, Chunxiao Liu, Panpan Cai

Abstract: Uncertainties in dynamic road environments pose significant challenges for behavior and trajectory planning in autonomous driving. This paper introduces Hi-Drive, a hierarchical planning algorithm addressing uncertainties at both behavior and trajectory levels using a hierarchical Partially Observable Markov Decision Process (POMDP) formulation. Hi-Drive employs driver models to represent uncertain behavioral intentions of other vehicles and uses their parameters to infer hidden driving styles. By treating driver models as high-level decision-making actions, our approach effectively manages the exponential complexity inherent in POMDPs. To further enhance safety and robustness, Hi-Drive integrates a trajectory optimization based on importance sampling, refining trajectories using a comprehensive analysis of critical agents. Evaluations on real-world urban driving datasets demonstrate that Hi-Drive significantly outperforms state-of-the-art planning-based and learning-based methods across diverse urban driving situations in real-world benchmarks.

IEEE Robotics and Automation Letters (RA-L) is a premier publication venue for robotics research, known for its rapid review process and high-quality contributions to the field of robotics and automation. This work represents a significant contribution to autonomous driving research, particularly in addressing the complex challenges of planning under uncertainty in urban environments.

Congratulations to all the authors on this outstanding achievement!