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Abstract
Changing lane must not only ensure the safety of the vehicle itself, but also ensure the patency of the traffic flow of the original lane and the target lane. Therefore, successful lane-changing is a key technology for autonomous vehicle control. In order to avoid collisions and ensure the smooth flow of traffic, in this paper a vehicle dynamics state model with time variable is established as plant, and the lateral force of the steering wheel is further optimized through Model Predictive Control(MPC), and then the steering wheel angle is obtained to complete the lane-changing operation. The longitudinal and lateral logic controllers designed through soft constraints can better achieve the results of successful lane-changing and unsuccessful return to the original lane, and the lane-changing characteristics within the safety corridor are analyzed in several ways. The simulation analysis of lane-changing strategy at different vehicle velocities provides helpful guidance for the design of autonomous vehicle controllers.
Highlights
Since the use of video cameras and Light Detection and Rangings (LIDARs) can better perceive the surrounding traffic conditions and navigate the road ahead through a planned map, autonomous vehicles are gradually entering test roads from the laboratories
The two lane-changing modes respectively with the maximum longitudinal acceleration priority strategy and the minimum longitudinal acceleration priority strategy are compared in typical vehicle velocities, and the causes of lag of the lane changing are analyzed from the perspective that the tire longitudinal and lateral forces are constrained by the tire friction circle
When changing lanes according to the minimum acceleration axmin strategy, both high-velocity lane change and low-velocity lane change can be completed within 5∼13 seconds, but the high-velocity lane changing presents a large overshoot control characteristic in the prediction horizon and the oscillation is obvious
Summary
Since the use of video cameras and Light Detection and Rangings (LIDARs) can better perceive the surrounding traffic conditions and navigate the road ahead through a planned map, autonomous vehicles are gradually entering test roads from the laboratories. When the vehicle VB is traveling in its own lane with the first phase of lane change, longitudinal control is required to adjust the velocity and displacement of the vehicle VB in order to adapt the traffic flow composed of VB, V1 and V3, and to determine the prediction horizon to start the lane changing.
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