Multi-lane Cruising Using Hierarchical Planning and Reinforcement Learning

2021-10-01 · Kasra Rezaee, Peyman Yadmellat, Masoud S. Nosrati, Elmira Amirloo Abolfathi, Mohammed Elmahgiubi, Jun Luo

Competent multi-lane cruising requires using lane changes and within-lane maneuvers to achieve good speed and maintain safety. This paper proposes a design for autonomous multi-lane cruising by combining a hierarchical reinforcement learning framework with a novel state-action space abstraction. While the proposed solution follows the classical hierarchy of behavior decision, motion planning and control, it introduces a key intermediate abstraction within the motion planner to discretize the state-action space according to high level behavioral decisions. We argue that this design allows principled modular extension of motion planning, in contrast to using either monolithic behavior cloning or a large set of hand-written rules. Moreover, we demonstrate that our state-action space abstraction allows transferring of the trained models without retraining from a simulated environment with virtually no dynamics to one with significantly more realistic dynamics. Together, these results suggest that our proposed hierarchical architecture is a promising way to allow reinforcement learning to be applied to complex multi-lane cruising in the real world.

📄 PDF Abstract BibTeX arXiv:2110.00650

Code (0)

등록된 구현이 없습니다.

Tasks

Hierarchical Reinforcement Learning Motion Planning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Enhancing High-Speed Cruising Performance of Autonomous Vehicles through Integrated Deep Reinforcement Learning Framework

2024-04-23 · Jinhao Liang, Kaidi Yang, Chaopeng Tan, Jinxiang Wang 외

High-speed cruising scenarios with mixed traffic greatly challenge the road safety of autonomous vehicles (AVs). Unlike existing works that only look at fundamental modules in isolation, this work enhances AV safety in m…

Autonomous Vehicles Decision Making Deep Reinforcement Learning reinforcement-learning+1

Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning

2020-11-09 · Zhiqian Qiao, Jeff Schneider, John M. Dolan

For autonomous vehicles, effective behavior planning is crucial to ensure safety of the ego car. In many urban scenarios, it is hard to create sufficiently general heuristic rules, especially for challenging scenarios th…

Autonomous Vehicles Hierarchical Reinforcement Learning reinforcement-learning Reinforcement Learning+1

Cooperative Cruising: Reinforcement Learning-Based Time-Headway Control for Increased Traffic Efficiency

2024-12-03 · Yaron Veksler, Sharon Hornstein, Han Wang, Maria Laura Delle Monache 외

The proliferation of connected automated vehicles represents an unprecedented opportunity for improving driving efficiency and alleviating traffic congestion. However, existing research fails to address realistic multi-l…

Perception Helps Planning: Facilitating Multi-Stage Lane-Level Integration via Double-Edge Structures

2024-07-16 · Guoliang You, Xiaomeng Chu, Yifan Duan, Wenyu Zhang 외

When planning for autonomous driving, it is crucial to consider essential traffic elements such as lanes, intersections, traffic regulations, and dynamic agents. However, they are often overlooked by the traditional end-…

Autonomous Driving

Spatial parking planning design with mixed conventional and autonomous vehicles

2021-04-05 · Qida Su, David Z. W. Wang

Travellers in autonomous vehicles (AVs) need not to walk to the destination any more after parking like those in conventional human-driven vehicles (HVs). Instead, they can drop off directly at the destination and AVs ca…

Autonomous Vehicles