As an emerging technology, Connected Autonomous Vehicles are believed to be able to pass intersections with greater efficiency, and related researches have been conducted for decades, however, compared to pre-designed model-based or optimization-based scheduling passing plan, the application of distributed reinforcement learning in the field of autonomous intersection management (AIM) has only begun to emerge in the past two years and confronts many challenges. Our study design a multi-level learning framework with various observation scope, action steps and reward period to make full use of information around vehicle and help to figure out the best interactive strategy for all vehicles. Our experiment has proven this framework can significantly enhance safety compared to RL without it, and improve efficiency compared to baselines.
翻译:作为一项新兴技术,联通自治车辆被认为能够以更高的效率通过交叉路口,而且已经进行了数十年的相关研究,然而,与预先设计的基于模型或基于优化的时间安排通路计划相比,在自主交叉管理领域应用分布式强化学习在过去两年才开始出现,面临许多挑战。我们的研究设计了一个具有各种观察范围、行动步骤和奖励期的多层次学习框架,以充分利用车辆周围的信息,并帮助为所有车辆制定最佳互动战略。我们的实验证明,这一框架可以大大加强安全,而没有这种安全,与基线相比,可以提高效率。