As the demand for autonomous driving increases, it is paramount to ensure safety. Early accident prediction using deep learning methods for driving safety has recently gained much attention. In this task, early accident prediction and a point prediction of where the drivers should look are determined, with the dashcam video as input. We propose to exploit the double actors and regularized critics (DARC) method, for the first time, on this accident forecasting platform. We derive inspiration from DARC since it is currently a state-of-the-art reinforcement learning (RL) model on continuous action space suitable for accident anticipation. Results show that by utilizing DARC, we can make predictions 5\% earlier on average while improving in multiple metrics of precision compared to existing methods. The results imply that using our RL-based problem formulation could significantly increase the safety of autonomous driving.

翻译：随着对自主驾驶的需求增加,确保安全至关重要。最近,利用深层学习方法进行早期事故预测,对驾驶安全进行早期事故预测,引起了人们的极大关注。在这项任务中,早期事故预测和对驾驶员应看何处进行点预测,将破胶片视频作为投入。我们提议首次在这个事故预测平台上利用双重行为者和正规化的批评者(DARC)方法。我们从DARRC得到启发,因为目前这是关于适合事故预测的持续行动空间的最新强化学习模式。结果显示,通过利用DARC,我们可以平均提前预测5 ⁇,同时比现有方法改进多种精确度。结果表明,使用基于RL的问题配置可以大大提高自主驾驶的安全性。