Human drivers can recognise fast abnormal driving situations to avoid accidents. Similar to humans, automated vehicles are supposed to perform anomaly detection. In this work, we propose the spatio-temporal graph auto-encoder for learning normal driving behaviours. Our innovation is the ability to jointly learn multiple trajectories of a dynamic number of agents. To perform anomaly detection, we first estimate a density function of the learned trajectory feature representation and then detect anomalies in low-density regions. Due to the lack of multi-agent trajectory datasets for anomaly detection in automated driving, we introduce our dataset using a driving simulator for normal and abnormal manoeuvres. Our evaluations show that our approach learns the relation between different agents and delivers promising results compared to the related works. The code, simulation and the dataset are publicly available on https://github.com/againerju/maad_highway.
翻译:人类驾驶员可以识别快速异常驾驶状况以避免事故。 与人类类似, 自动车辆应该进行异常检测。 在这项工作中, 我们建议使用时速图自动编码器来学习正常驾驶行为。 我们的创新是能够共同学习多种动态物剂的轨迹。 为了进行异常检测, 我们首先估计学习的轨迹特征表现的密度功能, 然后在低密度地区检测异常。 由于缺乏用于自动驾驶中异常检测的多剂轨迹数据集, 我们用一个驱动模拟器来引入我们的数据集, 用于正常和异常的动作。 我们的评估显示, 我们的方法可以了解不同物剂之间的关系, 并比相关作品带来有希望的结果 。 代码、 模拟和数据集可以在 https:// github. com/ congenerju/maad_highway 上公开查阅 。