Accurate prediction of vehicle lane-change behaviour is beneficial for traffic efficiency and safety. This paper incorporates driver behavioural information indicators into the prediction of lane change behaviour. A driving simulator was used to collect driver behavioural characteristics and vehicle operating parameters during lane keeping and lane changing, and through theoretical analysis and data testing, seven indicators were obtained that could be used to predict lane changing behaviour: target area gaze time, head level turning angle, vehicle speed, lane drift angle, distance to the vehicle in front, and time to collision. Using the experimentally collected variable data, a logistic prediction model was developed and validated to have satisfactory prediction accuracy.
In autonomous driving tasks, the driver only needs to perform supervision task and would be free from complex driving tasks, which leads to great reduction of brain workload. However, it is known that low level of brain workload will cause driver to enter passive fatigue state naturally. Passive fatigue occurs in autonomous driving condition definitely for most people. Due to the situation above and the high demands of driving security, this article will study the impact of passive fatigue on driving behavior. Compared with manual driving, autonomous driving performed worse in the state of passive fatigue by analyzing emergency response time. In addition, an effected deeper fatigue state as well as the increased takeover time appeared under the condition of passive fatigue. It is disclosed that passive fatigue developed faster than how manual fatigue developed in the same test. Eventually, it has been proved that passive fatigue can be relieved by takeover, through the test with the demonstration of the changes of both subjective evaluation scores and objective indicators in the same way under the research.
The driver’s decision and behavior are important factors affecting traffic safety. This paper uses 22 participants to participate in a simulated driving experiment, integrated driver's driving behavior parameters and vehicle operating parameters for in-depth research as well as analysis, additionally statistical analysis on various indicators in lane change intention phase through independent sample T test, etc. The study has disclosed the effectively influence of the factors, which includes eye movement, head movement, and the vehicle related factors e.g.t vehicle speed, vehicle deviation angle, lateral acceleration, the distance to vehicle in front and the time to collision, etc. These factors change significantly between the different intention phases of lane keeping and lane change. Herein a steering prediction model is constructed based on logistic regression, which is verified to be highly effective. The accuracy of the prediction model in the experiment reached 94.4%, which means the effective complement of the prediction of lane change behavior.
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