Planning for Negotiations in Autonomous Driving using Reinforcement Learning

Abstract

Planning autonomous driving behaviors in dense traffic is challenging. Human drivers are able to influence their road environment to achieve (otherwise unachievable) goals, by communicating their intents to other drivers. An autonomous system that is required to drive in the presence of human traffic must thus possess this fundamental negotiation capability. This work presents a novel benchmark that includes a stochastic driver negotiation model and a framework for training policies to drive and negotiate based on reinforcement learning. It is shown that driving policies trained in this framework lead to greater safety, higher mission accomplishment rates and more driving comfort, and can generalize across scenarios.