A model predictive-based approach for longitudinal control in autonomous driving with lateral interruptions

Abstract

The longitudinal control of an autonomous vehicle usually suffers from lateral interruptions, such as the cutting in/out of the lead vehicle, deteriorating its performance and even endangering driving safety. To address this problem, we present a model predictive-based approach for longitudinal control in autonomous driving by taking the lateral interruptions into account. First, a virtual lead vehicle scheme is introduced to predict the future behavior of the actual lead vehicle. By following the virtual lead vehicle rather than the actual lead vehicle, the control of the host vehicle is simplified to keep a proper following gap problem. Then, a strategic car-following gap (CFG) model, generated from highway naturalistic driving data, is employed to describe the safety hazard and the probability of cut-ins by other vehicles. A model predictive controller, incorporating the strategic CFG model as well as the acceleration and jerk limitations in the objective function, is designed for the longitudinal control of the host vehicle. Solving the optimal control problem can not only smooth the oscillation and overshoots caused by the lateral interruptions but also reduce the probability of cut-ins from the adjacent lanes. The proposed approach is simulated and validated through some predefined test scenarios in CarSim software.