摘要
For autonomous driving,drivers’intervention may be required when vehicles fail or are in a dilemma to detect emergent and unprogrammed events.In such situations,non-driving related tasks may have a great impact on the safety of drivers’critical intervention behav-ior thus leading to traffic accidents.Therefore,exploring the impacts of non-driving-related tasks on drivers’critical intervention behavior,quantifying and predicting the correspond-ing risks have become important.In this paper,driving simulation experiments are carried out to obtain the vehicle driving state data and visual behavior information of drivers dur-ing the autonomous driving scenarios that require critical interventions.To construct the risk quantification model for drivers’critical intervention behavior,the fuzzy comprehen-sive evaluation method and the criteria importance though intercriteria correlation(CRITIC)weighting method are employed.Then,for risk prediction,a model is constructed based on the visual behavior information before the occurrences of intervention.Multivariate logistic regression(MLR)and support vector machine are compared.The results show that non-driving tasks significantly postpone driver’s critical intervention responses,increasing crash risks of the driving.For prediction,SVM performs better than the MLR in terms of metrics including the precision,the recall,and the overall accuracy.This paper examines the risks during situations requiring drivers’critical intervention,associated with different non-driving tasks,which has remained much unexplored in the previous research.The methodology of this paper can be applied to smart vehicle systems in alerting vehicles for take-over reactions,with recognizing and predicting potential risks.
基金
supported by the National Natural Science Foundation of China(No.52172348)
Shanghai Municipal Science and Technology Major Project of China(No.2021SHZDZX0100)
the Shanghai Municipal Commission of Science and Technology Project of China(No.19511132101)
the Fundamental Research Funds for the Central Universities.
