Abstract
Due to the limitation of current technologies and product costs, humans are still in the driving loop, especially for public traffic. One key problem of cooperative driving is determining the time when assistance is required by a driver. To overcome the disadvantage of the driver state-based detection algorithm, a new index called the correction ability of the driver is proposed, which is further combined with the driving risk to evaluate the driving capability. Based on this measurement, a degraded domain (DD) is further set up to detect the degradation of the driving capability. The log normal distribution is used to model the boundary of DD according to the bench test data, and an online algorithm is designed to update its parameter interactively to identify individual driving styles. The bench validation results show that the identification algorithm of the DD boundary converges finely and can reflect the individual driving characteristics. The proposed degradation detection algorithm can be used to determine the switching time from manual to automatic driving, and this DD-based cooperative driving system can drive the vehicle in a safe condition.
Highlights
With the growing number of vehicles, traffic safety has become a hot social concern
Due to the limitation of current technologies and product costs, the driverless car running in public traffic is still under research and humans will be in the driving loop for a long time [5,6]
The rest of the paper is organized as follows: Section 2 introduces the fundamentals of the detection method for the driving capability; Section 3 establishes the model of driver behavior under normal conditions; in Section 4, the degraded domain to detect the degradation of the driving capability is designed; the proposed strategy is validated and analyzed by a bench test in Section 5 and applied to the cooperative driving system in Section 6; Section 7 concludes the paper
Summary
With the growing number of vehicles, traffic safety has become a hot social concern. The driver error, caused by visual distraction, fatigue, etc., contributes to most traffic accidents [1,2]. The developed assistance system can be applied to roads with different curvatures and running velocities Since both visual distraction and fatigue increase the driving risk, Benloucif et al defined a variable to evaluate these two types of driver states together [17]. To comprehensively measure the driving capability and avoid unnecessary interventions on drivers, a new index called the correction ability of drivers is designed for the first time and is combined with the driving risk to form the evaluation space for the driving capability Based on this two-dimensional evaluation space, a degraded domain (DD) is set up to detect the degradation of the driving capability. According to the simulated driving data, the log normal distribution is used to model the boundary of DD, and an online algorithm is designed for the estimation of its parameter to characterize the individual driving style This new degradation detection method for cooperative driving is more accurate than that only using the driver state. The rest of the paper is organized as follows: Section 2 introduces the fundamentals of the detection method for the driving capability; Section 3 establishes the model of driver behavior under normal conditions; in Section 4, the degraded domain to detect the degradation of the driving capability is designed; the proposed strategy is validated and analyzed by a bench test in Section 5 and applied to the cooperative driving system in Section 6; Section 7 concludes the paper
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