Parallel Distributed Compensation/H∞Control of Lane‑keeping System Based on the Takagi‑Sugeno Fuzzy Model

Current research on lane-keeping systems ignores the effect of the driver and external resistance on the accuracy of tracking the lane centerline.To reduce the lateral deviation of the vehicle,a lane-keeping control method based on the fuzzy Takagi-Sugeno(T-S)model is proposed.The method adopts a driver model based on near and far visual angles,and a driver-road-vehicle closed-loop model based on longitudinal nonlinear velocity variation,obtaining the expected assist torque with a robust H∞controller which is designed based on parallel distributed compensation and linear matrix inequality.Considering the external influences of tire adhesion and aligning torque when the vehicle is steering,a feedforward compensation control is designed.The electric power steering system is adopted as the actuator for lane-keeping,and active steering redressing is realized by a control motor.Simulation results based on Carsim/Simulink and real vehicle test results demonstrate that the method helps to maintain the vehicle in the lane centerline and ensures driving safety.

Intelligent Vehicle Driving Controller Design and Implementation Evaluation

An intelligent vehicle control system is designed and embedded in a Digital Signal Processing （DSP） platform （eZdspTM F2812）. A golf cart is used as an installation platform for the overall system, including steering wheel Alternating Current （AC） serve motor, brake actuator, throttle driving circuit and sensors. Digital image processing technology is also used to enable the autonomous driving system to achieve multi-mode lane-keeping, lane-change and obstacle-avoidance. The overall system is tested and evaluated on a university campus.

Spectrum quantification-based safety efficiency evaluation of autonomous vehicle under random cut-in scenarios

Continuous-scale trusted safety efficiency evaluation is crucial for the agile development and robust validation of autonomous vehicle intelligence.While the UN R157 Regulation evaluates automated lane-keeping system(ALKS)performance baselines through safe collision plots(SCPs)in various scenario clusters,quantifying the specific ALKS safety efficiency remains challenging.We propose a spectrum quantification approach to evaluate the safety efficiency of autonomous vehicles in cut-in scenarios.First,we collected speed-distance data under different cut-in scenarios and extracted essential spectral features to indicate the vehicle motion parameters during the cut-in process.Second,by utilizing Fourier analysis,a spectral analysis model was built to quantify and analyze the vehicle motion characteristics,providing insights into scenario safety.Finally,we created approximate analytical equations for the normalized disturbance frequencies in the nonlinear response scenarios of autonomous driving systems by combining the SCP with a frequency spectrum analysis model.The results showed that the normalized disturbance frequency in the cut-in scenario was approximately 0.2.When the relative longitudinal distance and speed of the vehicle are the same,if the cut-in speed of the cut-in vehicle is larger,the normalized disturbance frequency is higher,indicating that the cut-in process of the autonomous vehicle is more dangerous and may trigger a collision.