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DeepAccident

Code for the benchmark - DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving.

Install / Use

/learn @tianqi-wang1996/DeepAccident
About this skill

Quality Score

0/100

Supported Platforms

Universal

README

DeepAccident

The official implementation of the paper DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving.

Installation

Please check installation for installation and data_preparation for preparing the nuScenes dataset.

Getting Started

Please check getting_started for training, evaluation, and visualization of DeepAccident.

Motion & Accident prediction from Multi-View Images

visualization

V2XFormer for perception & prediction

visualization

Acknowledgement

This project is mainly based on the following open-sourced projects: BEVerse, Fiery, open-mmlab.

Bibtex

If this work is helpful for your research, please consider citing the following BibTeX entry.

@article{Wang_2023_DeepAccident,
  title = {DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving},
  author = {Wang, Tianqi and Kim, Sukmin and Ji, Wenxuan and Xie, Enze and Ge, Chongjian and Chen, Junsong and Li, Zhenguo and Ping, Luo},
  journal = {arXiv preprint arXiv:2304.01168},
  year = {2023}
}

Related Skills

View on GitHub
GitHub Stars96
CategoryDevelopment
Updated11d ago
Forks13

Languages

Python

Security Score

80/100

Audited on Mar 24, 2026

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