ROAR: Robust Accident Recognition and Anticipation for Autonomous Driving
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909894223855616 |
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| author | Liu, Xingcheng Guan, Yanchen Liao, Haicheng He, Zhengbing Li, Zhenning |
| author_facet | Liu, Xingcheng Guan, Yanchen Liao, Haicheng He, Zhengbing Li, Zhenning |
| contents | Accurate accident anticipation is essential for enhancing the safety of autonomous vehicles (AVs). However, existing methods often assume ideal conditions, overlooking challenges such as sensor failures, environmental disturbances, and data imperfections, which can significantly degrade prediction accuracy. Additionally, previous models have not adequately addressed the considerable variability in driver behavior and accident rates across different vehicle types. To overcome these limitations, this study introduces ROAR, a novel approach for accident detection and prediction. ROAR combines Discrete Wavelet Transform (DWT), a self adaptive object aware module, and dynamic focal loss to tackle these challenges. The DWT effectively extracts features from noisy and incomplete data, while the object aware module improves accident prediction by focusing on high-risk vehicles and modeling the spatial temporal relationships among traffic agents. Moreover, dynamic focal loss mitigates the impact of class imbalance between positive and negative samples. Evaluated on three widely used datasets, Dashcam Accident Dataset (DAD), Car Crash Dataset (CCD), and AnAn Accident Detection (A3D), our model consistently outperforms existing baselines in key metrics such as Average Precision (AP) and mean Time to Accident (mTTA). These results demonstrate the model's robustness in real-world conditions, particularly in handling sensor degradation, environmental noise, and imbalanced data distributions. This work offers a promising solution for reliable and accurate accident anticipation in complex traffic environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_06226 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ROAR: Robust Accident Recognition and Anticipation for Autonomous Driving Liu, Xingcheng Guan, Yanchen Liao, Haicheng He, Zhengbing Li, Zhenning Artificial Intelligence Accurate accident anticipation is essential for enhancing the safety of autonomous vehicles (AVs). However, existing methods often assume ideal conditions, overlooking challenges such as sensor failures, environmental disturbances, and data imperfections, which can significantly degrade prediction accuracy. Additionally, previous models have not adequately addressed the considerable variability in driver behavior and accident rates across different vehicle types. To overcome these limitations, this study introduces ROAR, a novel approach for accident detection and prediction. ROAR combines Discrete Wavelet Transform (DWT), a self adaptive object aware module, and dynamic focal loss to tackle these challenges. The DWT effectively extracts features from noisy and incomplete data, while the object aware module improves accident prediction by focusing on high-risk vehicles and modeling the spatial temporal relationships among traffic agents. Moreover, dynamic focal loss mitigates the impact of class imbalance between positive and negative samples. Evaluated on three widely used datasets, Dashcam Accident Dataset (DAD), Car Crash Dataset (CCD), and AnAn Accident Detection (A3D), our model consistently outperforms existing baselines in key metrics such as Average Precision (AP) and mean Time to Accident (mTTA). These results demonstrate the model's robustness in real-world conditions, particularly in handling sensor degradation, environmental noise, and imbalanced data distributions. This work offers a promising solution for reliable and accurate accident anticipation in complex traffic environments. |
| title | ROAR: Robust Accident Recognition and Anticipation for Autonomous Driving |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2511.06226 |