Collecting a large number of dangerous omen scenarios from drivers’ first-person perspective is of great significance for training and improving end-to-end autonomous driving models. In this study, we aim at capturing driver-perspective scenarios when recognizing dangerous omens. Firstly, through the design and implementation of vehicle and virtual driving experiments, the electroencephalogram, electrocardiogram and eye movement data of the subjects are collected. Statistical tests are conducted to analyze the characteristic differences among drivers across three distinct states. It also reveals that the driver can perceive and distinguish the dangerous omen clearly. Secondly, the evolution law of drivers’ perception state is analyzed to accurately judge the time period of drivers’ dangerous omen perception. Thirdly, the Hidden Markov Model is used to build the driver perception state transition model, and then the model is calibrated and verified. Finally, the model is utilized to identify drivers’ dangerous omen perception states and extract the corresponding perspective objective scenarios, which can provide sufficient samples for training end-to-end autonomous driving models. This study is of great significance to enable the capability of vehicles to recognize dangerous omens, advancing end-to-end and other high-level autonomous driving technologies and further securing vehicle safety.
Chen et al. (Thu,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: