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

TL;DR

This paper presents a deep reinforcement learning approach for autonomous driving that uses compact semantic states to enable behavior adaptation and safe maneuver decision-making across diverse traffic scenarios.

Contribution

It introduces a novel semantic state representation and a behavior adaptation mechanism allowing online behavior changes without retraining.

Findings

Agent learns to drive safely in various situations

Adheres to traffic rules effectively

Enables online behavior adaptation

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 on-line changes of desired behaviors without re-training. 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.