Poster 4: Trajectory Planning and Tracking Control for Autonomous Vehicle Lane Change Maneuver Based on Model-Predictive Control
(Room Palms Ballroom)
16 Jul 19
5:30 PM
-
7:00 PM
Tracks:
Vehicle Automation Technology
This research focuses on lane change motion planning with a combination of probabilistic and deterministic prediction for Autonomous Vehicle (AV) under complex driving circumstances. By fusing information from vehicle sensors, a lane changing predictor based on Intelligent Driving Model (IDM) car-following model is applied to predict the trajectories of the preceding vehicles on both current lane and target lane. Model Predictive Control (MPC) is applied to implement trajectory generation and path following. Field experiments are conducted in the vehicle test site. An autonomous vehicle equipped with 3D Light Detection and Ranging (LiDAR) sensor and three human driven vehicles are used in the experiments. During the experiments, the preceding vehicle drives at different speed levels ranging from 20 km/h to 60 km/h. Comparisons between our proposed algorithm and human driven vehicle (HV) show that lane changing trajectories of the AV is much more smoother than HV.