Dynamic Feature Extraction Method of Driver's Propensity under Complicated Vehicle Group

Abstract

The effect of driver's physiological and psychological characteristics on traffic safety is represented mainly as driver's propensity. Previous researches focus mostly on psychology test and its influence on traffic safety from relative static and macroscopic perspective. However, in the field of vehicle active safety, there are few studies on driver's affective measurement and computing from microcosmic and dynamic perspective, and previous researchers did not consider the influence of environment. The emphasis is about situation factors which directly influence driver's affection in all environment factors under two-lane condition. Various experiments are designed to collect driver's microdynamic information, and characteristics of driver's propensity toward different environments are extracted using genetic simulated annealing algorithm. Results show that the method can provide a basis to establish dynamic recognition model of driver's propensity further which is adapted to multilane environment.

Driver’s Lane Selection Model Based on Phase-Field Coupling and Multiplayer Dynamic Game with Incomplete Information

“…The method in literatures [20,21] was referenced to identify the driving propensity; the propensity type with the largest probability was chosen as the driver's propensity. The specific process of the determination on the order for drivers to take action was shown in Figure 4.…”

Reverse deduction of vehicle group situation based on dynamic Bayesian network

“…The participants driving experience was ranged from 1 to 9 years, and the average driving experience was 3.46 years. Fifty drivers were organized to participate in a psychological questionnaire test referred to the literatures, [4][5][6][7] and the driving propensity of 50 drivers could be obtained. The time series data of influential factors on vehicle group situation (vehicle type, position, etc.)…”

“…The information of driving behavior, vehicle movement, and traffic environment was analyzed, and genetic simulated annealing algorithm was used to extract the feature vectors of driving propensity from different vehicle group situations. 5 And then the transition mechanism of driving propensity along with the evolution of the vehicle group situation under the two-lane condition was revealed. 6 On this basis, the situation of vehicle group in the case of three-lane was divided and reduced, 7 and the transition mechanism of vehicle group situation under dynamic and complex three-lane condition was studied.…”

Driver emotion recognition of multiple‐ECG feature fusion based on BP network and D–S evidence

“…Transportation scholars have explored driver's emotion recognition using different types of physiological data [12][13][14][15]. Wan used the threshold discriminant method to identify driver's tension and calm moods [16], according to face geometric features such as the area ratio between eye and face.…”