Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States

Personal use of this material is permitted. Abstract-- Making the right decision in traffic is a challenging task that is highly dependent on individual preferences as well as the surrounding environment. Therefore it is hard to model solely based on expert knowledge. In this work we use Deep Reinforcement Learning to learn maneuver decisions based on a compact semantic state representation. This ensures a consistent model of the environment across scenarios as well as a behavior adaptation function, enabling online changes of desired behaviors without retraining. The input for the neural network is a simulated object list similar to that of Radar or Lidar sensors, superimposed by a relational semantic scene description. The state as well as the reward are extended by a behavior adaptation function and a parameterization respectively. With little expert knowledge and a set of mid-level actions, it can be seen that the agent is capable to adhere to traffic rules and learns to drive safely in a variety of situations. While sensors are improving at a staggering pace and actuators as well as control theory are well up to par to the challenging task of autonomous driving, it is yet to be seen how a robot can devise decisions that navigate it safely in a heterogeneous environment that is partially made up by humans who not always take rational decisions or have known cost functions.