构建3000段自行车碰撞视频数据集，助力自动驾驶安全研究
CycleCrash: A Dataset of Bicycle Collision Videos for Collision Prediction and Analysis
- 构建含43万帧的自行车碰撞视频数据集，涵盖多种危险场景
- 提出VidNeXt模型，在9项任务中实现领先性能
- 适合自动驾驶、交通安全研究者使用
自动驾驶研究常忽视骑行者碰撞问题。为此，我们提出CycleCrash数据集，包含3,000段行车记录仪视频，共436,347帧，涵盖从碰撞到安全交互的各类关键情境。该数据集支持9类骑行者碰撞预测与分类任务，标注了与碰撞、骑行者及场景相关的标签。我们进一步提出VidNeXt方法，采用ConvNeXt空间编码器与非平稳注意力机制，捕捉视频时序动态。为验证方法有效性并建立基准，我们在该数据集上应用并对比7种模型，进行详尽消融实验。相关数据集与代码已开源。
原文摘要 · Abstract (English)
Self-driving research often underrepresents cyclist collisions and safety. To address this, we present CycleCrash, a novel dataset consisting of 3,000 dashcam videos with 436,347 frames that capture cyclists in a range of critical situations, from collisions to safe interactions. This dataset enables 9 different cyclist collision prediction and classification tasks focusing on potentially hazardous conditions for cyclists and is annotated with collision-related, cyclist-related, and scene-related labels. Next, we propose VidNeXt, a novel method that leverages a ConvNeXt spatial encoder and a non-stationary transformer to capture the temporal dynamics of videos for the tasks defined in our dataset. To demonstrate the effectiveness of our method and create additional baselines on CycleCrash, we apply and compare 7 models along with a detailed ablation. We release the dataset and code at https://github.com/DeSinister/CycleCrash/ .
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