Optimisation of curve following behaviour in adaptive cruise control platoon using model predictive control

This study addresses the challenges of Adaptive Cruise Control (ACC) in curved road scenarios by proposing a novel enhanced control framework leveraging Model Predictive Control (MPC), which is chosen for its ability to handle multi-constraint optimisation and anticipate future vehicle states. The framework jointly considers longitudinal and lateral dynamics to enhance vehicle stability and safety. A new variable control mode is developed, dividing the scenarios into mild, moderate and extreme adjustments based on road curvature and lateral acceleration. The study introduces a predictive control algorithm with adaptive weight adjustments and lateral constraints to enhance curve-following accuracy. Simulations using CarSim software validate the algorithm under scenarios from the OpenACC data set, demonstrating improved stability and reduced overshoots. Comparative analysis with standard ACC platoons highlights significant improvements in longitudinal tracking and platoon stability. This work contributes to the optimisation of ACC systems for safer and more efficient autonomous driving.