Predicting emergency-braking distance is important for the collision avoidance related features, which are the most essential and popular safety features for vehicles. In this study, we first gathered a large data set including a three-dimensional acceleration data and the corresponding emergency-braking distance. Using this data set, we propose a deep-learning model to predict emergency-braking distance, which only requires 0.25 seconds three-dimensional vehicle acceleration data before the break as input. We consider two road surfaces, our deep learning approach is robust to both road surfaces and have accuracy within 3 feet.
翻译:预测紧急刹车距离对于避免碰撞的相关特征非常重要,这些特征是车辆最必要和最受欢迎的安全特征。在本研究中,我们首先收集了大型数据集,包括三维加速数据和相应的应急刹车距离。我们利用这一数据集提出了一个深层学习模型,以预测紧急刹车距离,这只需要0.25秒的三维车辆加速数据作为输入。我们认为,两个路面,我们的深层学习方法对道路表面都十分健全,在三英尺内准确无误。