Early Unsafety Detection in Autonomous Vehicles

摘要

Autonomous vehicles have been investigated broadly during the last decade and predicted to decrease road fatalities by shifting control of safety-critical tasks from humans to machines. An early unsafety detection consequently becomes a key feature in every self-driving cars and trucks. In this paper, we present a promising approach for the safety prediction problem in autonomous vehicles by using one dataset collected from the competition CMDC 2019, which can capture multiple safe or unsafe situations from a front car camera put in different autonomous buses. We consider various ways to extract potential features from images provided and apply numerous machine learning techniques to learn an efficient detection algorithm. The experimental results show that by combining Histogram-of-Gradients (HOG) features as well as deep-learning ones computed from both ResNet50 and our proposed deep neural networks (MRNets), we can achieve an auspicious performance in terms of both micro-averaged F1-score and macro-averaged F1-score. The outcome of our papers can give an additional contribution to the current study of the problem.