Autonomous driving technology is one of the research hotspots in recent years. The development of some basic technologies such as motor sensors, vehicle to everything (V2X) and high-definition map also provides more powerful technical support for autonomous driving. However, it remains a challenge to propose a common decision strategy for autonomous driving. Considering the scenario that the networks can cover various urban roads through road side units (RSUs), this paper proposes a two-satage autonomous driving decision strategy based on environmental awareness and asynchronous advantage actor critic (A3C) algorithm. The first stage proposes a preliminary common model with safety guarantee for all roads; based on which the second stage trains a unique model for each road in consideration of different road features. Simulation results indicate that the proposed strategy can ensure high safety performance and driving efficiency.
A Distributed Driving Decision Scheme Based on Reinforcement Learning for Autonomous Driving Vehicles
2020-05-01
488888 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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