Training a deep learning model for identifying dangerous vehicles requires a large amount of labeled accident data. However, it is difficult to collect a sufficient amount of accident data in the real world. To address this challenge, we introduce a driving-simulator-based data generator that can arbitrarily produce a wide variety of accident scenarios. Furthermore, in order to reduce the gap between synthetic data and real data, we propose a new domain adaptation algorithm that refines both features and labels. We conduct extensive real-data experiments to demonstrate that our dangerous vehicle classifier can reduce the missed detection rate by at least \(23.2\%\), as compared to those trained only with scarce real data, for an interested scenario in which time-to-collision is 1.6–1.8 s. We also find that our algorithm can identify various accident-related factors (such as wheel angles, vehicle orientations, and velocities of nearby vehicles) to enable high prediction accuracy for complex accident scenes.

训练用于识别危险车辆的深度学习模型需要大量标记的事故数据。然而，在现实世界中很难收集到足够数量的事故数据。为了应对这一挑战，我们引入了一个基于驾驶模拟器的数据生成器，它可以任意生成各种各样的事故场景。此外，为了缩小合成数据和真实数据之间的差距，我们提出了一种新的域自适应算法，可以同时改进特征和标签。我们进行了大量的真实数据实验来证明我们的危险车辆分类器可以将漏检率降低至少\(23.2\%\)，与仅使用稀有真实数据训练的那些相比，对于碰撞时间为 1.6-1.8 秒的感兴趣场景。我们还发现我们的算法可以识别各种与事故相关的因素（例如车轮角度、车辆方向和附近车辆的速度），从而对复杂的事故场景具有高预测精度。