基于自适应腰带区动态占用率的自动变道

IF 4.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems Pub Date : 2024-06-20 DOI:10.1016/j.robot.2024.104732

Soo Ho Woo , Soon-Geul Lee , JaeHwan Choi , JunKi Hong , Jae-Hong Lee , HaoTian Xie

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引用次数: 0

摘要

本文介绍了自动变道技术的自适应和占位网格图算法，这是自动驾驶汽车的一项核心技术。其目的是通过应驾驶员要求自动变更车道的技术，提高驾驶员的安全性和便利性。该算法以自我车辆为基础构建九个网格，并利用自我车辆和目标车辆的相对速度生成自适应和占用网格图。当驾驶员请求变更车道时，自适应网格图将自我车辆周围可能存在目标车辆的情况从 256 种减少到 32 种，从而减少了计算量。因此，该算法适用于需要实时计算的自动驾驶车辆。根据目标车辆的位置形成占用网格图，并确定是否可以变道。算法通过 CarMaker 生成虚拟仿真环境，并使用 Matlab/Simulink 进行仿真。为了证明算法的有效性，我们在真实驾驶环境中使用真实车辆进行了实验。保留所有权利。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Automatic lane change based on dynamic occupancy of an adaptive gird zone

This paper presents adaptive and occupancy grid map algorithms for automatic lane change technology, a core technology in autonomous vehicles. The objectives are to improve driver safety and convenience with technology that automatically changes lanes at the request of the driver. The algorithms construct nine grids on the basis of ego vehicles and generate adaptive and occupancy grid maps by using the relative speeds of ego and target vehicles. When a driver requests a lane change, the adaptive grid map reduces the number of cases where the target vehicles may exist around the ego vehicle from 256 to 32, thus decreasing the calculation amount. Therefore, the algorithms are suitable for use in autonomous vehicles that require real-time calculations. An occupancy grid map is formed in accordance with the location of the target vehicles, and whether lane changes are possible is determined. The algorithms generate a virtual simulation environment with the CarMaker and are simulated using Matlab/Simulink. An experiment is conducted in a real driving environment with real vehicles to prove the validity of the algorithms.

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来源期刊

Robotics and Autonomous Systems 工程技术-机器人学

CiteScore

9.00

自引率

7.00%

发文量

164

审稿时长

4.5 months

期刊介绍： Robotics and Autonomous Systems will carry articles describing fundamental developments in the field of robotics, with special emphasis on autonomous systems. An important goal of this journal is to extend the state of the art in both symbolic and sensory based robot control and learning in the context of autonomous systems. Robotics and Autonomous Systems will carry articles on the theoretical, computational and experimental aspects of autonomous systems, or modules of such systems.