From AI to CI: A Definition of Cooperative Intelligence in Autonomous Driving With the rapid development of deep learning, artificial intelligence (AI) has been widely used in many fields and gradually replaced a part of human jobs. However, the approach of improving intelligent capability of single agent is not enough to achieve complicated tasks in everchanging environments. Cooperative intelligence (CI) is regarded as a promising way to solve this problem. In this paper, we scientifically define the three key problems of achieving cooperative intelligence, which are cooperative perception, cooperative decision and cooperative learning. We illustrate each problem with a scenario of autonomous driving as well as a brief survey of related research works. Meanwhile, we propose a system architecture and components design of cooperative intelligence system for autonomous driving.

MultiAgent Deep Reinforcement Learning for Cooperative Driving in Crowded Traffic Scenarios For autonomous vehicles, lane changes on crowded roads are difficult to be performed without interactions and cooperation between vehicles. This paper proposes a novel method to learn interaction and cooperate between the multiple vehicles to solve the complex traffic problem through MultiAgent Reinforcement Learning (MARL). The proposed network is designed based on the interaction network to learn optimal control strategies considering interaction between vehicles. By applying the proposed algorithm, the network can control and train the agents regardless of the number of agents. It is a practical advantage because the number of the vehicles is constantly changed in the real environment. The proposed method is evaluated in the connected car environment where all vehicles can exchange information with each other.