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实时路径规划自动驾驶汽车的越野驾驶
Ethery Ramirez-Robles1, Oleg Starostenko1, Vicente Alarcon-Aquino1
1Department of Computing, Electronics, and Mechatronics, Universidad de las Américas-Puebla, Puebla, Mexico.
PeerJ. Computer science
|August 15, 2024
概括
这项研究引入了一种新的方法,用于自主越野驾驶,使用语义细分和实时路径规划. 该系统实现了高精度和更快的计算时间用于导航辅助.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 自动驾驶是一个快速发展的领域,具有重大的社会和经济效益.
- 由于复杂的地形和感知要求,完全自动的越野导航仍然是一个挑战.
- 现有的自动驾驶汽车 (AV) 系统需要复杂的数据处理来进行传感,绘图,定位和路径规划.
研究的目的:
- 通过整合语义细分和实时路线规划,提出自主越野驾驶的原创方法.
- 设计一个模块化框架,用于在越野场景中辅助驾驶,优化用于资源有限的设备.
- 提高场景理解和路径预测能力,用于越野自主导航.
主要方法:
- 一个深度神经网络在红绿蓝 (RGB) 摄像头图像上执行语义细分.
- 一个可穿越性模块将光检测和距离 (LiDAR) 数据与细分结果融合起来,创建一个占用网格地图.
- 快速探索随机树 (RRT) 算法适用于实时路径规划.
主要成果:
- 提出的语义细分方法在弗莱堡森林数据集 (FFD) 上达到85.9%的精度,在RELLIS-3D数据集上达到79.5%.
- 与现有方法相比,该系统在路径规划方面展示了更快的计算时间.
- 模块化框架有效地为自主导航提供场景理解.
结论:
- 开发的方法为自主越野导航提供了一个有前途的解决方案.
- 语义细分和基于RRT的路径规划的集成可以在具有挑战性的环境中提高AV性能.
- 该系统的效率使其适用于资源有限的越野自动驾驶应用.
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