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The decision and planning system for autonomous driving in urban environmentsis hard to design. Most current methods manually design the driving policy,which can be expensive to develop and maintain at scale. Instead, withimitation learning we only need to collect data and the computer will learn andimprove the driving policy automatically. However, existing imitation learningmethods for autonomous driving are hardly performing well for complex urbanscenarios. Moreover, the safety is not guaranteed when we use a deep neuralnetwork policy. In this paper, we proposed a framework to learn the drivingpolicy in urban scenarios efficiently given offline connected driving data,with a safety controller incorporated to guarantee safety at test time. Theexperiments show that our method can achieve high performance in realisticsimulations of urban driving scenarios.

Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safety