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- W3116266826 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." @default.
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- W3116266826 date "2020-12-16" @default.
- W3116266826 modified "2023-09-26" @default.
- W3116266826 title "CARLA Real Traffic Scenarios - novel training ground and benchmark for autonomous driving." @default.
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