# Safe Trajectory Planning Using Reinforcement Learning for Self Driving

arXiv: https://arxiv.org/abs/2011.04702 · HTML: https://mlsanity.com/paper/2011.04702/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving](https://mlsanity.com/paper/2405.01440/) (2024)
- [Informed Reinforcement Learning for Situation-Aware Traffic Rule Exceptions](https://mlsanity.com/paper/2402.04168/) (2024)

## Abstract (arXiv)

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.