Research on the Performance of Vehicle Lateral Control Algorithm Based on Vehicle Speed Variation

Analyzing the performance characteristics and applicable environments of intelligent vehicle lateral control algorithms, this paper establishes the Model Predictive Control (MPC), Pure Pursuit (PP), and Linear Quadratic Regulator (LQR) algorithms and uses 2022b MATLAB software to simulate the lateral error, heading error, and algorithm execution time at speeds of 3 m/s, 7 m/s, and 10 m/s. Urban low-speed scenarios (3 m/s) require high-precision control (such as obstacle avoidance), while high-speed scenarios (10 m/s) require strong stability. Existing research mostly focuses on a single speed and lacks a quantitative comparison across multiple operating conditions. Although MPC has high accuracy, its time consumption fluctuates greatly. LQR has strong real-time performance but a wide range of heading errors. PP has poor low-speed performance but controllable high-speed time consumption growth. It is necessary to define the applicable scenarios of each algorithm through quantitative data. In response to the lack of multi-speed domain quantitative comparison in existing research, this paper conducts multi-condition simulations using MPC, PP, and LQR algorithms and finds that at a low speed of 3 m/s, the peak lateral error of PP (0.45 m) is 55% and 156% higher than MPC (0.29 m) and LQR (0.176 m), respectively. At a speed of 10 m/s, the lateral error standard deviation of MPC (0.08 m) is reduced by 68% compared to PP (0.25 m). In terms of algorithm time consumption, LQR maintains full-speed domain stability (0.11–0.44 ms), while PP time increases by 95% with speed from 3 m/s to 10 m/s. The results show that in terms of lateral error, the MPC and LQR algorithms perform more stably, while the PP algorithm has a larger error at low speeds. Regarding heading error, all algorithms have a relatively large error range, but the MPC and LQR algorithms perform slightly better than the PP algorithm at high speeds. In terms of algorithm execution time, the LQR algorithm has the shortest and most stable execution time, the MPC algorithm has a relatively longer execution time, and the PP algorithm’s execution time varies at different speeds. Through this simulation, if high control accuracy and stability are pursued, the MPC or LQR algorithm can be considered; if real-time performance and computational efficiency are more important, the PP algorithm can be considered.