CARLA Real Traffic Scenarios -- novel training ground and benchmark for autonomous driving

This work introduces interactive traffic scenarios in the CARLA simulator, which are based on real-world traffic. We concentrate on tactical tasks lasting several seconds, which are especially challenging for current control methods. The CARLA Real Traffic Scenarios (CRTS) is intended to be a training and testing ground for autonomous driving systems. To this end, we open-source the code under a permissive license and present a set of baseline policies. CRTS combines the realism of traffic scenarios and the flexibility of simulation. We use it to train agents using a reinforcement learning algorithm. We show how to obtain competitive polices and evaluate experimentally how observation types and reward schemes affect the training process and the resulting agent's behavior.

Simulation-based reinforcement learning for real-world autonomous driving

We use synthetic data and a reinforcement learning algorithm to train a system controlling a full-size real-world vehicle in a number of restricted driving scenarios. The driving policy uses RGB images as input. We analyze how design decisions about perception, control and training impact the real-world performance.