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

• URL: http://arxiv.org/abs/2012.11329v1
• Date: Wed, 16 Dec 2020 13:20:39 GMT
• Title: CARLA Real Traffic Scenarios -- novel training ground and benchmark for autonomous driving
• Authors: B{\l}a\.zej Osi\'nski, Piotr Mi{\l}o\'s, Adam Jakubowski, Pawe{\l} Zi\k{e}cina, Micha{\l} Martyniak, Christopher Galias, Antonia Breuer, Silviu Homoceanu, Henryk Michalewski
• Abstract summary: 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.
• Score: 8.287331387095545
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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