Sideslip Angle Fusion Estimation Method of Three-Axis Autonomous Vehicle Based on Composite Model and Adaptive Cubature Kalman Filter

Combined longitudinal and lateral dynamics regulation of autonomous vehicles via induced ℒ2$$ {\mathcal{L}}_2 $$‐gain linear parameter varying control strategies based on parameter‐dependent Lyapunov functions

“…A number of approaches have been proposed to improve both safety and efficiency of CAVs. [7][8][9][10][11][12][13][14][15] In particular, in References 7 and 8 the problem of integrated longitudinal and lateral cooperative control with practical implications for CAVs is addressed while in Reference 9 the coordination control problem of connected autonomous vehicles (CAVs) is presented. In Reference 10, a cooperative LPV/MPC approach is proposed to characterize longitudinal and lateral coupled vehicle control in shared driving scenarios.…”

“…This interconnectedness enables real‐time data exchange, fostering a collaborative and synchronized driving environment. A number of approaches have been proposed to improve both safety and efficiency of CAVs 7–15 . In particular, in References 7 and 8 the problem of integrated longitudinal and lateral cooperative control with practical implications for CAVs is addressed while in Reference 9 the coordination control problem of connected autonomous vehicles (CAVs) is presented.…”

“…The aim is at resolving conflicts among drivers and driving automation systems (DAS). A sideslip angle fusion estimation strategy based on an adaptive cubature Kalman filter is investigated in Reference 11 for a three‐axis vehicle while a tire‐road peak adhesion coefficient estimation method applicable to intelligent vehicles is proposed in Reference 12. Reference 13 presents a framework for tactical decision‐making for autonomous driving that combines planning and learning using Monte Carlo tree search and deep‐reinforcement learning.…”

Method for the Trajectory Tracking Control of Unmanned Ground Vehicles Based on Chaotic Particle Swarm Optimization and Model Predictive Control

“…The slow response and trajectory deviation caused by time delays may prevent vehicles from making timely and correct judgments and executing corresponding actions in emergency situations, thereby increasing the risk of accidents. Therefore, in intelligent vehicle trajectory tracking control, effective handling of time delay issues is an important link for ensuring effectiveness in vehicle trajectory tracking control and driving safety [14][15][16]. By optimizing the processing speed of sensor data, constructing predictive control algorithms with high solving ability, introducing robust control strategies, adjusting control parameters in real time, and establishing accurate models, the impact of time delay factors on control systems can be effectively mitigated, and the accuracy and stability of trajectory tracking can be improved.…”