# Combining Policy Gradient and Safety-Based Control for Autonomous Driving

arXiv: https://arxiv.org/abs/2310.13314 · HTML: https://mlsanity.com/paper/2310.13314/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 20 Oct 2023.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)

## Builds on (most cited first, 8 of 8)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1902.00089/) (2019)
- [Simulated Car Racing Championship: Competition Software Manual](https://mlsanity.com/paper/1304.1672/) (2013)
- [Path Tracking of Highly Dynamic Autonomous Vehicle Trajectories via Iterative Learning Control](https://mlsanity.com/paper/1902.00611/) (2019)
- [Optimizing Coordinated Vehicle Platooning: An Analytical Approach Based on Stochastic Dynamic Programming](https://mlsanity.com/paper/2003.13067/) (2020)

## Abstract (arXiv)

With the advancement of data-driven techniques, addressing continuous con-trol challenges has become more efficient. However, the reliance of these methods on historical data introduces the potential for unexpected decisions in novel scenarios. To enhance performance in autonomous driving and collision avoidance, we propose a symbiotic fusion of policy gradient with safety-based control. In this study, we em-ploy the Deep Deterministic Policy Gradient (DDPG) algorithm to enable autono-mous driving in the absence of surrounding vehicles. By training the vehicle's driving policy within a stable and familiar environment, a robust and efficient learning pro-cess is achieved. Subsequently, an artificial potential field approach is utilized to formulate a collision avoidance algorithm, accounting for the presence of surround-ing vehicles. Furthermore, meticulous consideration is given to path tracking meth-ods. The amalgamation of these approaches demonstrates substantial performance across diverse scenarios, underscoring its potential for advancing autonomous driving while upholding safety standards.