Safe Trajectory Planning Using Reinforcement Learning for Self Driving
This addresses safety and comfort concerns in autonomous driving, though it appears incremental as it builds on existing reinforcement learning approaches for trajectory planning.
The paper tackles the problem of safe trajectory planning for self-driving vehicles by applying model-free reinforcement learning, resulting in improved safety, generality, and comfort compared to traditional methods.
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.