Safe Trajectory Planning Using Reinforcement Learning for Self Driving

TL;DR

This paper introduces a reinforcement learning approach for trajectory planning in self-driving cars, aiming to improve safety, generality, and comfort over traditional methods.

Contribution

It demonstrates the application of model-free reinforcement learning specifically for trajectory planning in autonomous vehicles, addressing safety and adaptability.

Findings

Reinforcement learning enhances safety in trajectory planning.

The approach improves generalization across diverse driving scenarios.

It results in more comfortable driving behaviors.

Abstract

Self-driving vehicles must be able to act intelligently in diverse and difficult environments, marked by high-dimensional state spaces, a myriad of optimization objectives and complex behaviors. Traditionally, classical optimization and search techniques have been applied to the problem of self-driving; but they do not fully address operations in environments with high-dimensional states and complex behaviors. Recently, imitation learning has been proposed for the task of self-driving; but it is labor-intensive to obtain enough training data. Reinforcement learning has been proposed as a way to directly control the car, but this has safety and comfort concerns. We propose using model-free reinforcement learning for the trajectory planning stage of self-driving and show that this approach allows us to operate the car in a more safe, general and comfortable manner, required for the task of self driving.