Huizong Feng

An extended microscopic traffic flow model based on the spring-mass system theory

This study proposes a new microscopic traffic flow model based on the spring-mass system theory. In particular, considering the similarity between the acceleration or deceleration behavior in traffic flow and the scaling properties of a spring, a car-following (CF) model is proposed based on the fundamental physical law of the spring-mass system. Stability of the proposed model is analyzed using the perturbation method to obtain the stability condition. Numerical experiments are performed through simulation. The results demonstrate the proposed model can capture the characteristic of propagation backwards of disturbance in traffic flow. In addition, the findings of this study provide insights in modeling traffic flow from the mechanical system theory perspective.

DSRC based vehicular platoon control considering the realistic V2V/V2I communications

This study proposes a dedicated short range communications (DSRC) based vehicular platoon control considering the realistic vehicle-to-vehicle/vehicle-to-infrastructure (V2V/V2I) communications. In particular, the information on vehicles of interest, i.e., speed and position, is shared with the neighboring vehicles or roadside units (RSUs) through the on-board units (OBUs) via the DSRC. Then, the leader-follower approach is used to manage the vehicular platoon. The leader of platoon can receive/send the relevant information from/to the followers so as to guarantee the stable platoon formation. Field experiments are performed in two scenarios: platoon forming and merging. Results from the experiments validate the effectiveness of the proposed method in terms of the speed and position trajectories.

TransModeler based implementation of autonomous vehicular platoon control

This study develops a vehicular platoon control system (VPCS) based on the combination of car-following model and leader-follower model under connected environment. In particular, first, a three-level framework including information collection, information processing and information release is proposed to design the control strategy. Then, the car-following model is used to capture the characteristics of vehicular platoon movement; while the leader-follower model is used to describe the vehicle dynamics of vehicle merging into the platoon. Finally, the TransModeler based platform is developed to validate the effectiveness of the proposed method with respect to vehicle speed and position trajectory.

Learnable geometric method on multi-sensor weighted evidence fusion

Focused on the shortcomings of dealing with the evidences conflict in Dempster-Shafter combination rule and other improved algorithms, a new learnable geometric method on weighted data fusion is presented. In this method, a linear space is spanned by basic probability assignment vector over discernment frame, and the historical evidence data of each sensor in a period of time are mapped into the space to form the geometric model of evidence point distribution. Then the credibility of each sensor is evaluated by credible radius of evidence set of the sensor, and the optimal weighted allotment of different evidence sources is acquired. Along with the increasing of new point in evidence set, the credibility of sensor tends to an accurate and steady value. Because the conflict and consistency can be characterized by the Euclidean distance of evidences in the linear space to describe the rejection or support of each focus element, the weighted combination rule can be expressed and calculated conveniently by the method presented. Experiment results show that the method can estimate the credibility of sensor accurately and improve the combination rule effectively.