Distributed Multiagent Coordinated Learning for Autonomous Driving in Highways Based on Dynamic Coordination Graphs

By Chao Yu, Xin Wang, Xin Xu, Minjie Zhang, Hongwei Ge, Jiankang Ren, Liang Sun, Bingcai Chen, Guozhen Tan. 2020

125 citations recorded by the source corpus.

Autonomous driving is one of the most important AI applications and has attracted extensive interest in recent years. A large number of studies have successfully applied reinforcement learning techniques in various aspects of autonomous driving, ranging from low-level control of driving maneuvers to higher level of strategic decision-making. However, comparatively less progress has been made in investigating how co-existing autonomous vehicles would interact with each other in a common environment and how reinforcement learning can be helpful in such situations by applying multiagent reinforcement learning techniques in the high-level strategic decision-making of the following or overtaking for a group of autonomous vehicles in highway scenarios. Learning to achieve coordination among vehicles in such situations is challenging due to the unique feature of vehicular mobility, which renders it infeasible to directly apply the existing coordinated learning approaches. To solve this problem, we propose using dynamic coordination graph to model the continuously changing topology during vehicles’ interactions and come up with two basic learning approaches to coordinate the driving maneuvers for a group of vehicles. Several extension mechanisms are then presented to make these approaches workable in a more complex and realistic setting with any number of vehicles. The experimental evaluation has verified the benefits of the proposed coordinated learning approaches, compared with other approaches that learn without coordination or rely on some traditional mobility models based on some expert driving rules.

Subfields: Artificial Intelligence, Automotive Engineering, Control and Systems Engineering

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Citations in the corpus

Deep Reinforcement Learning for Autonomous Driving: A Survey (2020, background): “One important area where MARL techniques could be very beneficial is in high-level decision making and coordination between groups of autonomous vehicles, in scenarios such as overtaking in highway scenarios [149], or negotiating intersections without signalised control.”
Human-Like Decision Making for Autonomous Driving: A Noncooperative Game Theoretic Approach (2020, methodology): “In [5], a Bayesian networks model is applied to decision making, that considers the uncertainties from environmental perception to decision making.”
Enabling Massive IoT Toward 6G: A Comprehensive Survey (2021, background): “To solve this problem, the authors of [108] proposed a strategic distributed learning solution for coordinating multiple vehicles.”
Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey (2020, background): “However, authors in [122] proposed a multi-agent deep RL approach with dynamic coordination graph.”