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GIN: Graph-based Interaction-aware Constraint Policy Optimization for Autonomous Driving. (arXiv:2206.01488v3 [cs.RO] UPDATED)
cs.LG updates on arXiv.org arxiv.org
Applying reinforcement learning to autonomous driving entails particular
challenges, primarily due to dynamically changing traffic flows. To address
such challenges, it is necessary to quickly determine response strategies to
the changing intentions of surrounding vehicles. This paper proposes a new
policy optimization method for safe driving using graph-based interaction-aware
constraints. In this framework, the motion prediction and control modules are
trained simultaneously while sharing a latent representation that contains a
social context. To reflect social interactions, we illustrate the movements …
arxiv autonomous autonomous driving driving graph graph-based optimization policy