Human emotions are the revulsive of intentions. It's an important premise to identify drivers' emotions dynamically and correctly for the realization of drivers' intentions identification, active vehicle security warning and mind control driving. It is also an essential requirement for the microscopic research of traffic flow theory. Taking the car-following condition as an example, multi-source and dynamic data of human-vehicle-environment under drivers' different emotional states was obtained through emotions induced experiments, actual driving experiments and virtual driving experiments in this paper. The main influencing factors of typical driving emotions were extracted with factor analysis method and emotion identification model was established based on the fuzzy comprehensive evaluation and PAD emotional model. The emotions of joy, anger, sadness and fear can be identified online. The rationality and validity of the emotions feature extraction and identification model were verified through the experiments of actual driving, virtual driving and interactive simulation. The theoretical foundation for the study of emotion guidance mechanism of drivers' intentions can be provided. (C) 2019 Elsevier Ltd. All rights reserved.

With a view to the background of Internet of Things, the vehicle group situation, especially the propensity of different drivers, and the corresponding vehicle types were given a comprehensive consideration on the basis of the factors which influence driving behavior, and the driver's lane selection model based on multi-player dynamic game with complete information was built. The driver's payoffs under different combination of lane selection strategies were analyzed, and on this basis, the subgame perfect Nash equilibrium solution was solved through backward induction, and the driver's optimal lane selection strategy was obtained. Next-Generation Simulation datasets and other data were applied for model calibration and validation. The results showed that the proposed model can objectively reflect the actual operation characteristic of traffic flow on road section. So, it is of great significance to apply the multi-person dynamic game in the field of intelligent transportation. And the theoretical basis of the research on lane selection decision-making can be provided for the command system of intelligent driving especially anthropomorphic driving under the condition of Internet of Things.

Emotion is the external way to express human’s inner thoughts, which has a significant influence on human behaviors. It is an important prerequisite for studying the intrinsic affect mechanism of emotions on behaviors certain. In this article, drivers’ emotional induction experiment, actual and virtual driving experiments are designed to obtain the multi-source dynamic data of human–vehicle–environment under the condition of different emotions. The influences of emotions’ changes on car movement characteristics of different types of drivers are explored. Changing law of car movement characteristics under the condition of different emotions can be obtained finally. The research can provide theoretical basis for the future research of driver assistance system, which is of great significance to realize active vehicle safety warning and unmanned driving in the future.

Pedestrian is an important factor for traffic safety and capacity in the sections mixed with bicycle. It is important for improving active safety to implement timely pedestrian safety warning under the condition of Internet of pedestrian and bicycle. The mutual influence of pedestrian and its surrounding traffic participants in mixed pedestrian-bicycle sections was comprehensively analyzed, and the phase of pedestrian-bicycle traffic was partitioned and reduced on the basis of previous researches and phase-field coupling theory. Fuzzy logic method was used to build the pedestrian movement intention identification model. The pedestrian safety and the satisfaction for walking speed and space were synthetically considered in this model. The experimental verifications show that the result of pedestrian movement intention identification model is consistent with the actual situation, and this study can provide theoretical support for the realization of the pedestrian active safety under the condition of Internet of Things.

针对集群车辆驾驶员的车道选择行为,着眼物联网背景,综合考虑车辆集群态势、驾驶倾向性等影响驾驶员行为的因素,建立基于完全信息多人动态博弈的车道选择模型.通过分析不同策略组合下的驾驶员收益,运用逆向归纳法,求解子博弈精炼纳什均衡,得到驾驶员的最优车道选择策略.应用实车实验等手段验证模型,结果表明,所建模型能够较为客观地反映驾驶员车道选择行为及交通流特性.

The adoption of automobile safety assistance and driving alert systems is an effective way to ensure traffic safety because such applications can accurately predict vehicle aggregation situations. Improving the drivers' lane selection process is not only the most fundamental reason for transforming vehicle aggregating but also the basic component of traffic flow research. However, the effects of factors such as the characteristics of individual vehicles and drivers, the types of manipulators used in complex vehicle aggregation situations, and the influence of vehicle conflict on lane selection have not been addressed in previous studies. This paper assesses the characteristics of various traffic manipulators, vehicles, and drivers to develop a lane selection model of basic urban expressway segments based on mixed fuzzy many-person, multi-objective, non-cooperative games. By analyzing drivers' profits under different combinations of lane selection behavior, Nash equilibrium was confirmed in a single game process and optimal lane selection behavior was obtained in a dynamic game. The results show that the model's prediction accuracy of lane changing is 85.2%.