부산대학교 도서관

Considering multi-lane and multi-vehicle scenarios common adaptive cruise control (ACC) systems often face the problem of sudden and uncomfortable control actions when surrounding vehicles change the lane leading to a switch in the target vehicle of the ACC. Probabilistic modeling of the lane change behavior of surrounding traffic participants allows to predict such lane changes. This enables anticipatory control actions to avoid hard braking maneuvers and hence increases driving comfort and economy. This paper presents a scenario model predictive control (SCMPC) which estimates the lane change tendency of surrounding drivers by drawing a number of scenarios from a stochastic lane change prediction model. The model itself is identified based on real driving data. Simulation results show the advantages of the proposed control strategy by means of comparison to a common PI controlled ACC system.