Several key scenarios, such as intersection navigation, lane changing, and ramp merging, are active areas of research in autonomous driving. In order to properly navigate these scenarios, autonomous vehicles must implicitly negotiate with human drivers. Prior work in driving behaviors presents reinforcement learning as a promising technique, as it can leverage data as well as the underlying decision-making structure of driving with interaction. We apply a hierarchical approach to decision-making, where we train a high-level policy using reinforcement learning, and execute the policy's output on a low-level controller. This hierarchical structure helps increase the policy's overall safety, and allows the learning component to be agnostic to the low-level control scheme. We validate our approach on a simulation using real-world highway data and find improved results compared to prior work in ramp merging.
Learning Highway Ramp Merging Via Reinforcement Learning with Temporally-Extended Actions
2020 IEEE Intelligent Vehicles Symposium (IV) ; 1595-1600
2020-10-19
393773 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch