Xiaoyuan Wang

Bio: Xiaoyuan Wang is an academic researcher from Shandong University of Technology. The author has contributed to research in topics: Active safety & Process (engineering). The author has an hindex of 4, co-authored 5 publications receiving 44 citations. Previous affiliations of Xiaoyuan Wang include Rensselaer Polytechnic Institute.

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.

Vehicle Group Relationship Transformation Mechanism under Dynamic and Complex Three-Lane Conditions

Abstract: A vehicle group is a dynamic spatial layout comprising a target vehicle and surrounding vehicles in the process of driving. It partly affects driving behavior states and guides driver propensity. Its formation and transformation mechanism are affected by driving behaviors all the time. It plays a significant role in research on active driving and autodriving systems by revealing the exact vehicle group transformation mechanism in a complex environment. Taking the three-lane condition as an example, a vehicle group is analyzed with the target vehicle running in different lanes. In addition, its state is recognized and a change rule is researched to reveal the transformation mechanism of a vehicle group in a time-varying environment. The current study establishes the basis for subsequent studies on the driver propensity transformation mechanism under complex, evolving environmental conditions.

Prediction Method of Driver’s Propensity Adapted to Driver’s Dynamic Feature Extraction of Affection

Abstract: Driver’s propensity is a dynamic measurement of driver’s characteristics, such as affection and preference. In the vehicle driver-assistance system, especially its collision warning subsystem, it is also an important parameter of computing driver's intention. The prediction of driver’s propensity from relative static and macroscopic perspective is an essential precondition for further researching and extracting dynamic characteristics. Physiology and psychology tests are designed to measure driver’s character and calculate physiological rhythm. Changing data of driver’s psychology and emotion during driving are obtained by real vehicle test. Then driver’s propensity values of different types are calculated by weighting method according to the contribution rate of standard features. Results show that this method is better than the traditional psychology test, and it provides a basis for further studying dynamic characteristics of driver’s affection.

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

Feature extraction and dynamic identification of drivers’ emotions

Abstract: 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.

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

Abstract: Driving emotion is considered as driver's psychological reaction to a change in traffic environment, which affects driver's cognitive, judgement and behaviour. In anxiety, drivers are more likely to get engaged in distracted driving, increasing the likelihood of vehicle crash. Therefore, it is essential to identify driver's anxiety during driving, to provide a basis for driving safety. This study used multiple-electrocardiogram (ECG) feature fusion to recognise driver's emotion, based on back-propagation network and Dempster-Shafer evidence method. The three features of ECG signals, the time-frequency domain, waveform and non-linear characteristics were selected as the parameters for emotion recognition. An emotion recognition model was proposed to identify drivers' calm and anxiety during driving. The results show after ECG evidence fusion, the proposed model can recognise drivers' emotion, with an accuracy rate of 91.34% for calm and 92.89% for anxiety. The authors' findings of this study can be used to develop the personalised driving warning system and intelligent human-machine interaction in vehicles. This study would be of great theoretical significance and application value for improving road traffic safety.