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

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.

References in corpus (8)

• Playing Atari with Deep Reinforcement Learning
• Prioritized Experience Replay
• Dueling Network Architectures for Deep Reinforcement Learning
• Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
• Deep Reinforcement Learning framework for Autonomous Driving
• Deep Reinforcement Learning: An Overview
• Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
• Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving

Cited by in corpus (10)

• A Survey of Deep RL and IL for Autonomous Driving Policy Learning
• Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning
• Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies
• Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning
• Cooperative Lane Changing via Deep Reinforcement Learning
• Deep Reinforcement Learning and Transportation Research: A Comprehensive Review
• Learning Reward Models for Cooperative Trajectory Planning with Inverse Reinforcement Learning and Monte Carlo Tree Search
• Comfortable Priority Handling with Predictive Velocity Optimization for Intersection Crossings
• Informed Reinforcement Learning for Situation-Aware Traffic Rule Exceptions
• Generalizing Decision Making for Automated Driving with an Invariant Environment Representation using Deep Reinforcement Learning