# DRIVE: Deep Reinforced Accident Anticipation with Visual Explanation

arXiv: https://arxiv.org/abs/2107.10189 · HTML: https://mlsanity.com/paper/2107.10189/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 50 references that are in the map.
- First posted on arXiv 21 Jul 2021.

## Builds on (most cited first, 30 of 32)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Recurrent Models of Visual Attention](https://mlsanity.com/paper/1406.6247/) (2014)
- [Counterfactual Visual Explanations](https://mlsanity.com/paper/1904.07451/) (2019)
- [Improving Sample Efficiency in Model-Free Reinforcement Learning from Images](https://mlsanity.com/paper/1910.01741/) (2019)
- [Textual Explanations for Self-Driving Vehicles](https://mlsanity.com/paper/1807.11546/) (2018)
- [From Variational to Deterministic Autoencoders](https://mlsanity.com/paper/1903.12436/) (2019)
- [Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention](https://mlsanity.com/paper/1703.10631/) (2017)
- [Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning](https://mlsanity.com/paper/2008.00334/) (2020)
- [Unsupervised Traffic Accident Detection in First-Person Videos](https://mlsanity.com/paper/1903.00618/) (2019)
- [Predicting Driver Attention in Critical Situations](https://mlsanity.com/paper/1711.06406/) (2017)
- [TASED-Net: Temporally-Aggregating Spatial Encoder-Decoder Network for Video Saliency Detection](https://mlsanity.com/paper/1908.05786/) (2019)
- [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)
- [Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning](https://mlsanity.com/paper/2005.14310/) (2020)
- [Anticipating Traffic Accidents with Adaptive Loss and Large-scale Incident DB](https://mlsanity.com/paper/1804.02675/) (2018)
- [CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis](https://mlsanity.com/paper/1809.05782/) (2018)
- [Driving in Dense Traffic with Model-Free Reinforcement Learning](https://mlsanity.com/paper/1909.06710/) (2019)
- [Predicting Head Movement in Panoramic Video: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1710.10755/) (2017)
- [Agent-Centric Risk Assessment: Accident Anticipation and Risky Region Localization](https://mlsanity.com/paper/1705.06560/) (2017)
- [AGIL: Learning Attention from Human for Visuomotor Tasks](https://mlsanity.com/paper/1806.03960/) (2018)
- [Deep 360 Pilot: Learning a Deep Agent for Piloting through 360deg Sports Video](https://mlsanity.com/paper/1705.01759/) (2017)
- [Periphery-Fovea Multi-Resolution Driving Model guided by Human Attention](https://mlsanity.com/paper/1903.09950/) (2019)
- [Global Feature Aggregation for Accident Anticipation](https://mlsanity.com/paper/2006.08942/) (2020)
- [Meta-SAC: Auto-tune the Entropy Temperature of Soft Actor-Critic via Metagradient](https://mlsanity.com/paper/2007.01932/) (2020)

## Built on by (most cited first, 30 of 46)

- [Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review](https://mlsanity.com/paper/2402.10086/) (2024)
- [Abductive Ego-View Accident Video Understanding for Safe Driving Perception](https://mlsanity.com/paper/2403.00436/) (2024)
- [Vision-Based Traffic Accident Detection and Anticipation: A Survey](https://mlsanity.com/paper/2308.15985/) (2023)
- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [Exploring Event-driven Dynamic Context for Accident Scene Segmentation](https://mlsanity.com/paper/2112.05006/) (2021)
- [Nexar Dashcam Collision Prediction Dataset and Challenge](https://mlsanity.com/paper/2503.03848/) (2025)
- [MADiff: Motion-Aware Mamba Diffusion Models for Hand Trajectory Prediction on Egocentric Videos](https://mlsanity.com/paper/2409.02638/) (2024)
- [Cognitive Accident Prediction in Driving Scenes: A Multimodality Benchmark](https://mlsanity.com/paper/2212.09381/) (2022)
- [CRASH: Crash Recognition and Anticipation System Harnessing with Context-Aware and Temporal Focus Attentions](https://mlsanity.com/paper/2407.17757/) (2024)
- [Real-time Accident Anticipation for Autonomous Driving Through Monocular Depth-Enhanced 3D Modeling](https://mlsanity.com/paper/2409.01256/) (2024)
- [FBLNet: FeedBack Loop Network for Driver Attention Prediction](https://mlsanity.com/paper/2212.02096/) (2022)
- [Where, What, Why: Towards Explainable Driver Attention Prediction](https://mlsanity.com/paper/2506.23088/) (2025)
- [Real-time Traffic Accident Anticipation with Feature Reuse](https://mlsanity.com/paper/2505.17449/) (2025)
- [ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos](https://mlsanity.com/paper/2604.09819/) (2026)
- [Explainability in Deep Reinforcement Learning, a Review into Current Methods and Applications](https://mlsanity.com/paper/2207.01911/) (2022)
- [Domain-Enhanced Dual-Branch Model for Efficient and Interpretable Accident Anticipation](https://mlsanity.com/paper/2507.12755/) (2025)
- [An Attention-guided Multistream Feature Fusion Network for Localization of Risky Objects in Driving Videos](https://mlsanity.com/paper/2209.07922/) (2022)
- [Exploring the Potential of Multi-Modal AI for Driving Hazard Prediction](https://mlsanity.com/paper/2310.04671/) (2023)
- [When, Where, and What? A Novel Benchmark for Accident Anticipation and Localization with Large Language Models](https://mlsanity.com/paper/2407.16277/) (2024)
- [BADAS: Context Aware Collision Prediction Using Real-World Dashcam Data](https://mlsanity.com/paper/2510.14876/) (2025)
- [Accident Anticipation via Temporal Occurrence Prediction](https://mlsanity.com/paper/2510.22260/) (2025)
- [When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network](https://mlsanity.com/paper/2506.17457/) (2025)
- [PaSBench-Video: A Streaming Video Benchmark for Proactive Safety Warning](https://mlsanity.com/paper/2606.02443/) (2026)
- [Reinforcement Learning for Predicting Traffic Accidents](https://mlsanity.com/paper/2212.04677/) (2022)
- [Augmenting Ego-Vehicle for Traffic Near-Miss and Accident Classification Dataset using Manipulating Conditional Style Translation](https://mlsanity.com/paper/2301.02726/) (2023)
- [Gated Driver Attention Predictor](https://mlsanity.com/paper/2308.02530/) (2023)
- [Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach](https://mlsanity.com/paper/2411.13302/) (2024)
- [Towards Vision Zero: The TUM Traffic Accid3nD Dataset](https://mlsanity.com/paper/2503.12095/) (2025)
- [EQ-TAA: Equivariant Traffic Accident Anticipation via Diffusion-Based Accident Video Synthesis](https://mlsanity.com/paper/2506.10002/) (2025)
- [Causal-Entity Reflected Egocentric Traffic Accident Video Synthesis](https://mlsanity.com/paper/2506.23263/) (2025)

## Abstract (arXiv)

Traffic accident anticipation aims to accurately and promptly predict the occurrence of a future accident from dashcam videos, which is vital for a safety-guaranteed self-driving system. To encourage an early and accurate decision, existing approaches typically focus on capturing the cues of spatial and temporal context before a future accident occurs. However, their decision-making lacks visual explanation and ignores the dynamic interaction with the environment. In this paper, we propose Deep ReInforced accident anticipation with Visual Explanation, named DRIVE. The method simulates both the bottom-up and top-down visual attention mechanism in a dashcam observation environment so that the decision from the proposed stochastic multi-task agent can be visually explained by attentive regions. Moreover, the proposed dense anticipation reward and sparse fixation reward are effective in training the DRIVE model with our improved reinforcement learning algorithm. Experimental results show that the DRIVE model achieves state-of-the-art performance on multiple real-world traffic accident datasets. Code and pre-trained model are available at urlhttps://www.rit.edu/actionlab/drive.