  With the rapid development of autonomous driving, collision avoidance has
attracted attention from both academia and industry. Many collision avoidance
strategies have emerged in recent years, but the dynamic and complex nature of
driving environment poses a challenge to develop robust collision avoidance
algorithms. Therefore, in this paper, we propose a decentralized framework
named RACE: Reinforced Cooperative Autonomous Vehicle Collision AvoidancE.
Leveraging a hierarchical architecture we develop an algorithm named Co-DDPG to
efficiently train autonomous vehicles. Through a security abiding channel, the
autonomous vehicles distribute their driving policies. We use the relative
distances obtained by the opponent sensors to build the VANET instead of
locations, which ensures the vehicle's location privacy. With a leader-follower
architecture and parameter distribution, RACE accelerates the learning of
optimal policies and efficiently utilizes the remaining resources. We implement
the RACE framework in the widely used TORCS simulator and conduct various
experiments to measure the performance of RACE. Evaluations show that RACE
quickly learns optimal driving policies and effectively avoids collisions.
Moreover, RACE also scales smoothly with varying number of participating
vehicles. We further compared RACE with existing autonomous driving systems and
show that RACE outperforms them by experiencing 65% less collisions in the
training process and exhibits improved performance under varying vehicle
density.
