Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safety

Abstract

The decision and planning system for autonomous driving in urban environments is hard to design. Most current methods manually design the driving policy, which can be expensive to develop and maintain at scale. Instead, with imitation learning we only need to collect data and the computer will learn and improve the driving policy automatically. However, existing imitation learning methods for autonomous driving are hardly performing well for complex urban scenarios. Moreover, the safety is not guaranteed when we use a deep neural network policy. In this paper, we proposed a framework to learn the driving policy in urban scenarios efficiently given offline connected driving data, with a safety controller incorporated to guarantee safety at test time. The experiments show that our method can achieve high performance in realistic simulations of urban driving scenarios.

arXiv comment7 pages, 7 figures

References in corpus (7)

• Adam: A Method for Stochastic Optimization
• End to End Learning for Self-Driving Cars
• CARLA: An Open Urban Driving Simulator
• Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car
• Inverse Reward Design
• Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving
• Deep Object-Centric Policies for Autonomous Driving

Cited by in corpus (9)

• Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Data
• Model-free Deep Reinforcement Learning for Urban Autonomous Driving
• Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning
• A Survey on Autonomous Vehicle Control in the Era of Mixed-Autonomy: From Physics-Based to AI-Guided Driving Policy Learning
• Guided Policy Search Model-based Reinforcement Learning for Urban Autonomous Driving
• Safe Trajectory Planning Using Reinforcement Learning for Self Driving
• End-to-end Autonomous Driving Perception with Sequential Latent Representation Learning
• A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning
• Efficient Latent Representations using Multiple Tasks for Autonomous Driving