# Autonomous Braking System via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1702.02302 · HTML: https://mlsanity.com/paper/1702.02302/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2017.

## Built on by (most cited first, 18 of 18)

- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [Improved Deep Reinforcement Learning with Expert Demonstrations for Urban Autonomous Driving](https://mlsanity.com/paper/2102.09243/) (2021)
- [Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors](https://mlsanity.com/paper/2305.11239/) (2023)
- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [Modeling driver's evasive behavior during safety-critical lane changes:Two-dimensional time-to-collision and deep reinforcement learning](https://mlsanity.com/paper/2209.15133/) (2022)
- [Risk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous Driving](https://mlsanity.com/paper/2306.03220/) (2023)
- [Dual-AEB: Synergizing Rule-Based and Multimodal Large Language Models for Effective Emergency Braking](https://mlsanity.com/paper/2410.08616/) (2024)
- [Adversarial Mixture Density Networks: Learning to Drive Safely from Collision Data](https://mlsanity.com/paper/2107.04485/) (2021)
- [Deep Reinforcement Learning for Advanced Longitudinal Control and Collision Avoidance in High-Risk Driving Scenarios](https://mlsanity.com/paper/2404.19087/) (2024)
- [Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance](https://mlsanity.com/paper/2109.15266/) (2021)
- [Safe Inverse Reinforcement Learning via Control Barrier Function](https://mlsanity.com/paper/2212.02753/) (2022)
- [Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages](https://mlsanity.com/paper/2103.09726/) (2021)
- [Robust Deep Reinforcement Learning Scheduling via Weight Anchoring](https://mlsanity.com/paper/2304.10176/) (2023)
- [A Multi-Task Approach to Robust Deep Reinforcement Learning for Resource Allocation](https://mlsanity.com/paper/2304.12660/) (2023)
- [CIER: A Novel Experience Replay Approach with Causal Inference in Deep Reinforcement Learning](https://mlsanity.com/paper/2405.08380/) (2024)
- [Advanced Longitudinal Control and Collision Avoidance for High-Risk Edge Cases in Autonomous Driving](https://mlsanity.com/paper/2504.18931/) (2025)
- [Drowsiness-Aware Adaptive Autonomous Braking System based on Deep Reinforcement Learning for Enhanced Road Safety](https://mlsanity.com/paper/2604.13878/) (2026)
- [Scaling Learning-based AEB with Massive Unlabeled Data](https://mlsanity.com/paper/2606.18864/) (2026)

## Abstract (arXiv)

In this paper, we propose a new autonomous braking system based on deep reinforcement learning. The proposed autonomous braking system automatically decides whether to apply the brake at each time step when confronting the risk of collision using the information on the obstacle obtained by the sensors. The problem of designing brake control is formulated as searching for the optimal policy in Markov decision process (MDP) model where the state is given by the relative position of the obstacle and the vehicle's speed, and the action space is defined as whether brake is stepped or not. The policy used for brake control is learned through computer simulations using the deep reinforcement learning method called deep Q-network (DQN). In order to derive desirable braking policy, we propose the reward function which balances the damage imposed to the obstacle in case of accident and the reward achieved when the vehicle runs out of risk as soon as possible. DQN is trained for the scenario where a vehicle is encountered with a pedestrian crossing the urban road. Experiments show that the control agent exhibits desirable control behavior and avoids collision without any mistake in various uncertain environments.