In order to clearly understand the risky riding behaviors of electric bicycles (e-bikes) and analyze the riding characteristics, we review the research results of the e-bike risky riding behavior from three aspects: the characteristics and causes of e-bike accidents, the characteristics of users’ traffic behavior, and the prevention and intervention of traffic accidents. The analysis results show that the existing research methods on risky riding behavior of e-bikes mainly involve questionnaire survey methods, structural equation models, and binary probability models. The illegal occupation of motor vehicle lanes, over-speed cycling, red-light running, and illegal manned and reverse cycling are the main risky riding behaviors seen with e-bikes. Due to the difference in physiological and psychological characteristics such as gender, age, audiovisual ability, responsiveness, patience when waiting for a red light, congregation, etc., there are differences in risky cycling behaviors of different users. Accident prevention measures, such as uniform registration of licenses, the implementation of quasi-drive systems, improvements of the riding environment, enhancements of safety awareness and training, are considered effective measures for preventing e-bike accidents and protecting the traffic safety of users. Finally, in view of the shortcomings of the current research, the authors point out three research directions that can be further explored in the future. The strong association rules between risky riding behavior and traffic accidents should be explored using big data analysis. The relationships between risk awareness, risky cycling, and traffic accidents should be studied using the scales of risk perception, risk attitude, and risk tolerance. In a variety of complex mixed scenes, the risk degree, coupling characteristics, interventions, and the coupling effects of various combination intervention measures of e-bike riding behaviors should be researched using coupling theory in the future.

Considering the complexity of lane changing using automated vehicles and the frequency of turning lanes in city settings, this paper aims to generate an accelerated lane-changing trajectory using vehicle-to-vehicle collaboration (V2VC). Based on the characteristics of accelerated lane changing, we used a polynomial method and cooperative strategies for trajectory planning to establish a lane-changing model under different degrees of collaboration with the following vehicle in the target lane by considering vehicle kinematics and comfort requirements. Furthermore, considering the shortcomings of the traditional elliptical vehicle and round vehicle models, we established a rectangular vehicle model with collision boundary conditions by analysing the relationships between the possible collision points and the outline of the vehicle. Then, we established a simulation model for the accelerated lane-changing process in different environments under different degrees of collaboration. The results show that, by using V2VC, we can achieve safe accelerated lane-changing trajectories and simultaneously satisfy the requirements of vehicle kinematics and comfort control.