# Reinforcement Learning for Predicting Traffic Accidents

arXiv: https://arxiv.org/abs/2212.04677 · HTML: https://mlsanity.com/paper/2212.04677/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 9 Dec 2022.

## Builds on (most cited first, 13 of 13)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning](https://mlsanity.com/paper/2008.00334/) (2020)
- [DADA-2000: Can Driving Accident be Predicted by Driver Attention? Analyzed by A Benchmark](https://mlsanity.com/paper/1904.12634/) (2019)
- [A Deep Multi-Level Network for Saliency Prediction](https://mlsanity.com/paper/1609.01064/) (2016)
- [Anticipating Traffic Accidents with Adaptive Loss and Large-scale Incident DB](https://mlsanity.com/paper/1804.02675/) (2018)
- [DRIVE: Deep Reinforced Accident Anticipation with Visual Explanation](https://mlsanity.com/paper/2107.10189/) (2021)
- [A Dynamic Spatial-temporal Attention Network for Early Anticipation of Traffic Accidents](https://mlsanity.com/paper/2106.10197/) (2021)
- [Efficient Continuous Control with Double Actors and Regularized Critics](https://mlsanity.com/paper/2106.03050/) (2021)
- [Path Planning of Cleaning Robot with Reinforcement Learning](https://mlsanity.com/paper/2208.08211/) (2022)
- [Two-stage training algorithm for AI robot soccer](https://mlsanity.com/paper/2104.05931/) (2021)
- [Reinforcement Learning for Navigation of Mobile Robot with LiDAR](https://mlsanity.com/paper/2112.02954/) (2021)

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

- [Recent Advances in Traffic Accident Analysis and Prediction: A Comprehensive Review of Machine Learning Techniques](https://mlsanity.com/paper/2406.13968/) (2024)
- [Off-Policy Reinforcement Learning with Loss Function Weighted by Temporal Difference Error](https://mlsanity.com/paper/2212.13175/) (2022)

## Abstract (arXiv)

As the demand for autonomous driving increases, it is paramount to ensure safety. Early accident prediction using deep learning methods for driving safety has recently gained much attention. In this task, early accident prediction and a point prediction of where the drivers should look are determined, with the dashcam video as input. We propose to exploit the double actors and regularized critics (DARC) method, for the first time, on this accident forecasting platform. We derive inspiration from DARC since it is currently a state-of-the-art reinforcement learning (RL) model on continuous action space suitable for accident anticipation. Results show that by utilizing DARC, we can make predictions 5% earlier on average while improving in multiple metrics of precision compared to existing methods. The results imply that using our RL-based problem formulation could significantly increase the safety of autonomous driving.