# Combining Deep Reinforcement Learning and Safety Based Control for Autonomous Driving

arXiv: https://arxiv.org/abs/1612.00147 · HTML: https://mlsanity.com/paper/1612.00147/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2016.

## Built on by (most cited first, 9 of 9)

- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services](https://mlsanity.com/paper/2304.14271/) (2023)
- [Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: simulations and experiments](https://mlsanity.com/paper/2103.01886/) (2021)
- [Hybrid Transfer Reinforcement Learning: Provable Sample Efficiency from Shifted-Dynamics Data](https://mlsanity.com/paper/2411.03810/) (2024)
- [Autonomous Driving at Unsignalized Intersections: A Review of Decision-Making Challenges and Reinforcement Learning-Based Solutions](https://mlsanity.com/paper/2409.13144/) (2024)
- [Motion Comfort Optimization for Autonomous Vehicles: Concepts, Methods, and Techniques](https://mlsanity.com/paper/2306.09462/) (2023)
- [A Safety-Critical Decision Making and Control Framework Combining Machine Learning and Rule-based Algorithms](https://mlsanity.com/paper/2201.12819/) (2022)
- [Adaptive Road Configurations for Improved Autonomous Vehicle-Pedestrian Interactions using Reinforcement Learning](https://mlsanity.com/paper/2303.12289/) (2023)
- [Review of Cloud Service Composition for Intelligent Manufacturing](https://mlsanity.com/paper/2408.01795/) (2024)

## Abstract (arXiv)

With the development of state-of-art deep reinforcement learning, we can efficiently tackle continuous control problems. But the deep reinforcement learning method for continuous control is based on historical data, which would make unpredicted decisions in unfamiliar scenarios. Combining deep reinforcement learning and safety based control can get good performance for self-driving and collision avoidance. In this passage, we use the Deep Deterministic Policy Gradient algorithm to implement autonomous driving without vehicles around. The vehicle can learn the driving policy in a stable and familiar environment, which is efficient and reliable. Then we use the artificial potential field to design collision avoidance algorithm with vehicles around. The path tracking method is also taken into consideration. The combination of deep reinforcement learning and safety based control performs well in most scenarios.