@article{ref1,
title="Lane-exchanging driving strategy for autonomous vehicle via trajectory prediction and model predictive control",
journal="Chinese journal of mechanical engineering (English edition)",
year="2022",
author="Chen, Yimin and Yu, Huilong and Zhang, Jinwei and Cao, Dongpu",
volume="35",
number="1",
pages="e71-e71",
abstract="The cooperation between an autonomous vehicle and a nearby vehicle is critical to ensure driving safety in the lane-exchanging scenario. The nearby vehicle trajectory needs to be predicted, from which the autonomous vehicle is controlled to prevent possible collisions. This paper proposes a lane-exchanging driving strategy for the autonomous vehicle to cooperate with the nearby vehicle by integrating vehicle trajectory prediction and motion control. A trajectory prediction method is developed to anticipate the nearby vehicle trajectory. The Gaussian mixture model (GMM), together with the vehicle kinematic model, are synthesized to predict the nearby vehicle trajectory. A potential-field-based model predictive control (MPC) approach is utilized by the autonomous vehicle to conduct the lane-exchanging maneuver. The potential field of the nearby vehicle is considered in the controller design for collision avoidance. On-road driving data verification shows that the nearby vehicle trajectory can be predicted by the proposed method. CarSim® simulations validate that the autonomous vehicle can perform the lane-exchanging maneuver and avoid the nearby vehicle using the proposed driving strategy. The autonomous vehicle can thus safely perform the lane-exchanging maneuver and avoid the nearby vehicle.<p /> <p>Language: en</p>",
language="en",
issn="1000-9345",
doi="10.1186/s10033-022-00748-7",
url="http://dx.doi.org/10.1186/s10033-022-00748-7"
}