Jinglei Zhang

Bio: Jinglei Zhang is an academic researcher from Shandong University of Technology. The author has contributed to research in topics: Information processing & Support vector machine. The author has an hindex of 6, co-authored 10 publications receiving 72 citations.

Study on influencing factors selection of driver’s propensity

Abstract: The influence of driver’s physiological and psychological factors on traffic safety is represented greatly as driver’s propensity, namely the driver’s psychological experience toward traffic condition and the corresponding preference of driver’s decisions and behaviors under the influence of all dynamic factors. It reflects driver’s psychological emotional states in the process of vehicle operation and movement. It is essential to the research of driver-assistance and active safety warning systems and also an important concept for traffic flow theory, especially driving behavior. Driver’s propensity is taken as the study object in this paper, physiological and psychological parameters are obtained through analyzing their influencing factors from the related experiments designed. Gray relation entropy analysis method is adopted to extract and sort the principal factors of physiology and psychology, and the influencing factors sequence of driving propensity is obtained. The results provide a good basis for further study of the driver’s propensity prediction.

Driver's Behavior and Decision-Making Optimization Model in Mixed Traffic Environment

Abstract: Driving process is an information treating procedure going on unceasingly. It is very important for the research of traffic flow theory, to study on drivers' information processing pattern in mixed...

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.

Lane-changing model with dynamic consideration of driver's propensity

Abstract: Lane-changing is the driver's selection result of the satisfaction degree in different lane driving conditions. There are many different factors influencing lane-changing behavior, such as diversity, randomicity and difficulty of measurement. So it is hard to accurately reflect the uncertainty of drivers' lane-changing behavior. As a result, the research of lane-changing models is behind that of car-following models. Driver's propensity is her/his emotion state or the corresponding preference of a decision or action toward the real objective traffic situations under the influence of various dynamic factors. It represents the psychological characteristics of the driver in the process of vehicle operation and movement. It is an important factor to influence lane-changing. In this paper, dynamic recognition of driver's propensity is considered during simulation based on its time-varying discipline and the analysis of the driver's psycho-physic characteristics. The Analytic Hierarchy Process (AHP) method is used to quantify the hierarchy of driver's dynamic lane-changing decision-making process, especially the influence of the propensity. The model is validated using real data. Test results show that the developed lane-changing model with the dynamic consideration of a driver's time-varying propensity and the AHP method are feasible and with improved accuracy.

High-Resolution Vehicle Trajectory Extraction and Denoising From Aerial Videos

Abstract: In recent years, unmanned aerial vehicle (UAV) has become an increasingly popular tool for traffic monitoring and data collection on highways due to its advantage of low cost, high resolution, good flexibility, and wide spatial coverage. Extracting high-resolution vehicle trajectory data from aerial videos taken by a UAV flying over target highway segment becomes a critical research task for traffic flow modeling and analysis. This study aims at proposing a novel methodological framework for automatic and accurate vehicle trajectory extraction from aerial videos. The method starts by developing an ensemble detector to detect vehicles in the target region. Then, the kernelized correlation filter is applied to track vehicles fast and accurately. After that, a mapping algorithm is proposed to transform vehicle positions from the Cartesian coordinates in image to the Frenet coordinates to extract raw vehicle trajectories along the roadway curves. The data denoising is then performed using a wavelet transform to eliminate the biased vehicle trajectory positions. Our method is tested on two aerial videos taken on different urban expressway segments in both peak and non-peak hours on weekdays. The extracted vehicle trajectories are compared with manual calibrated data to testify the framework performance. The experimental results show that the proposed method successfully extracts vehicle trajectories with a high accuracy: the measurement error of Mean Squared Deviation is 2.301 m, the Root-mean-square deviation is 0.175 m, and the Pearson correlation coefficient is 0.999. The video and trajectory data in this study are publicly accessible for serving as benchmark at https://seutraffic.com .

Happy-anger emotions classifications from electrocardiogram signal for automobile driving safety and awareness

Abstract: Developing a system to monitor the physical and psychological states of a driver and alert the driver is essential for accident prevention. Inspired by the advances in wireless communication systems and automatic emotional expression analysis using biological signals, an experimental protocol and computational model have been developed to study the patterns of emotions. The goal is to determine the most efficient display stimuli to evoke emotions and classify emotions of individuals using electrocardiogram (ECG) signals. A total of 69 subjects (36 males, 33 females) participated in the experiment and completed the survey. Physiological changes in ECG during the stimulus process were recorded using a wireless device. Recorded signals underwent a filtering process and feature extraction to determine meaningful features, define the model based on data assumption, and finally select algorithms used in the classification stage. Two extracted ECG features, namely root mean square successive difference and heart rate variability, were found to be significant for emotions evoked using the display stimuli. Support vector machine classification results successfully classify the happy-anger emotions with 83.33% accuracy using an audio-visual stimulus. The accuracy for happy recovery is 90.91%, and an excellent accuracy was also acquired for anger recovery. Findings of this work show that ECG can be used as an alternative to automatic self-reflective test procedures or additional source with which to validate the emotional state of a driver while in an automobile.

Model predictive control–based cooperative lane change strategy for improving traffic flow

Abstract: Lane change has attracted more and more attention in recent years for its negative impact on traffic safety and efficiency. However, few researches addressed the multi-vehicle cooperation during lane change process. In this article, feasibility criteria of lane change are designed, which considers the acceptable acceleration/deceleration of neighboring vehicles; meanwhile, a cooperative lane change strategy based on model predictive control is proposed in order to attenuate the adverse impacts of lane change on traffic flow. The proposed strategy implements the centralized decision making and active cooperation among the subject vehicle performing lane change in the subject lane and the preceding vehicle and the following vehicle in the target lane during lane change. Using model predictive control, safety, comfort, and traffic efficiency are integrated as the objectives, and lane change process is optimized. Numerical simulation results of the cooperative lane change strategy suggest that the deceleration of following vehicle can be weakened and further the shock wave propagated in traffic flow can be alleviated to some degree compared with traditional lane change. Language: en