Velocity Prediction Based on Vehicle Lateral Risk Assessment and Traffic Flow: A Brief Review and Application Examples

Forecasting future driving conditions such as acceleration, velocity, and driver behaviors can greatly contribute to safety, mobility, and sustainability issues in the development of new energy vehicles (NEVs). In this brief, a review of existing velocity prediction techniques is studied from the perspective of traffic flow and vehicle lateral dynamics for the first time. A classification framework for velocity prediction in NEVs is presented where various state-of-the-art approaches are put forward. Firstly, we investigate road traffic flow models, under which a driving-scenario-based assessment is introduced. Secondly, vehicle speed prediction methods for NEVs are given where an extensive discussion on traffic flow model classification based on traffic big data and artificial intelligence is carried out. Thirdly, the influence of vehicle lateral dynamics and correlation control methods for vehicle speed prediction are reviewed. Suitable applications of each approach are presented according to their characteristics. Future trends and questions in the development of NEVs from different angles are discussed. Finally, different from existing review papers, we introduce application examples, demonstrating the potential applications of the highlighted concepts in next-generation intelligent transportation systems. To sum up, this review not only gives the first comprehensive analysis and review of road traffic network, vehicle handling stability, and velocity prediction strategies, but also indicates possible applications of each method to prospective designers, where researchers and scholars can better choose the right method on velocity prediction in the development of NEVs.

Vehicle lateral motion control via robust delay-dependent Takagi-Sugeno strategy

Presented in this research paper is an integrated direct yaw moment control (DYC) and active front steering (AFS) for an uncertain vehicle lateral dynamics model considering network-induced communication delay, which is a time-varying continuous function with a known upper bound. Firstly, we consider tire cornering stiffness as a non-linear norm-bounded uncertain system that is modeled by fuzzy membership functions, and then vehicle lateral dynamics model is expressed by a set of linear Takagi-Sugeno (T-S) uncertain fuzzy models. Secondly, since the network-induced communication delay in vehicle control system is an inherent reason for stability and performance degradation, we derive a robust delay-dependent [Formula: see text] control methodology via the Lyapunov-Krasovskii functional for stability and performance conditions of the closed-loop system. For the synthesis, the robust control method is employed within the T-S fuzzy-model-based analysis framework and formulations are performed based on the solution of delay-dependent linear matrix inequalities (LMIs). The simulation study is presented using MATLAB/Simulink to show the achieved improvements in tracking variables via the designed robust fuzzy [Formula: see text] state-feedback controller. The proposed fuzzy robust delay-dependent controller is compared with a linear robust delay-dependent controller to clearly show the tracking improvements for different road conditions. Moreover, a performance-based analysis is carried out to demonstrate the advantage of the design with respect to different delay values. It is confirmed from the analysis results that the proposed fuzzy controller can successfully stabilize and possess improved tracking performance for vehicle lateral motion control.