Scalability of a Machine Learning Environment for Autonomous Driving Research We study scalability of machine learning environments in the context of mixed collaborative driving. Mixed collaborative driving includes both human controlled vehicles and vehicles controlled by AI (Artificial Intelligence) that share the physical road resources (e.g., intersections and roundabouts). Many such driving situations cannot be easily created nor replicated in the real life. Therefore, development and testing of AI systems is often done with simulators.Machine learning environments must maintain a realtime understanding of their traffic situation. Scaling of the machine environment to multiple distributed nodes is required to support larger number of participating vehicles. Our experimental environment consists of the CARLA simulator, custom AI implemented with the TensorFlow framework, and a corner case search subsystem. With the corner case search subsystem we can automatically evaluate the AI in different driving scenarios. In this paper, we present how scaling of the environment to multiple distributed nodes affects its performance.

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.