Building a mathematics model for lane‐change technology of autonomous vehicles

Abstract

In the process of autonomous vehicle motion planning and to create comfort for vehicle occupants, factors that must be considered are the vehicle's safety features and the road's slipperiness and smoothness. In this paper, we build a mathematical model based on the combination of a genetic algorithm and a neural network to offer lane‐change solutions of autonomous vehicles, focusing on human vehicle control skills. Traditional moving planning methods often use vehicle kinematic and dynamic constraints when creating lane‐change trajectories for autonomous vehicles. When comparing this generated trajectory with a man‐generated moving trajectory, however, there is in fact a significant difference. Therefore, to draw the optimal factors from the actual driver's lane‐change operations, the solution in this paper builds the training data set for the moving planning process with lane change operation by humans with optimal elements. The simulation results are performed in a MATLAB simulation environment to demonstrate that the proposed solution operates effectively with optimal points such as operator maneuvers and improved comfort for passengers as well as creating a smooth and slippery lane‐change trajectory.

Background memory‐assisted zero‐shot video object segmentation for unmanned aerial and ground vehicles

“…P Gðr,cÞ and P Sðr,cÞ denote the pixel values of the ground truth and predicted moving object map, respectively. The training process aims to minimize the overall loss L in (9). During the testing process, the fusion output was selected as the final estimation map.…”

“…The increasing prominence of unmanned aerial vehicles (UAV) and ground vehicles (UGV) has led to the development of diverse applications that require the identification of moving objects. In applications such as video surveillance [1][2][3], aerial monitoring [4][5][6], and autonomous navigation [7][8][9][10][11], the detection of moving objects, such as pedestrians, animals, or small objects, is crucial for intelligent systems. Thus, these expert systems must differentiate between moving and static objects by analyzing continuous video information.…”