Xiaoyuan Wang

TL;DR: Driver’s propensity is taken as the study object, physiological and psychological parameters are obtained through analyzing their influencing factors from the related experiments designed, and the influencing factors sequence of driving propensity is obtained.

TL;DR: The experimental verifications show that the result of pedestrian movement intention identification model is consistent with the actual situation, and can provide theoretical support for the realization of the pedestrian active safety under the condition of Internet of Things.

TL;DR: The model built in this paper can objectively reflect the actual operation characteristic of traffic flow on road section and the process of lane selection and can provide the theoretical basis of the research on lane selection for intelligent driving especially anthropomorphic driving under the condition of Internet of Things.

TL;DR: Dynamic recognition model of driver’s propensity based on support vector machine is established taking the vehicle safety controlled technology and respecting and protecting the driver's privacy as precondition, and results show that the established recognition model is reasonable and feasible.