一个基于视觉感知的终端到终端自动驾驶模型,用于临时道路
Qinghua Su1, Min Xie1, Liyong Wang1
1Key Laboratory of Modern Measurement and Control Technology, Ministry of Education, Beijing Information Science and Technology University, Beijing, China.
PeerJ. Computer science
|September 24, 2025
概括
本研究介绍了一种端到端的深度学习模型,用于在临时道路上自动驾驶,提高移动机器人的安全性和稳定性. 该模型准确地识别临时道路,并实现无人地面车辆跟踪的实时性能.
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:
- 自动驾驶的深度学习在结构化道路上取得了进展,但对临时道路的研究有限.
- 端到端模型提供高度集成,将输入数据直接转化为驾驶操作,提高安全性和稳定性.
- 在临时道路等非结构化环境中的自主导航存在独特的挑战.
研究的目的:
- 为移动机器人设计的专为临时道路设计的全自动驾驶模型.
- 提高在充满挑战的非结构化环境中运行的自动驾驶汽车的安全性和稳定性.
- 让无人驾驶地面车辆能够在临时道路上有效执行跟踪任务.
主要方法:
- 开发了一个新的端到端模型,利用三个道路图像作为输入.
- 使用全球上下文视觉转换器 (GCViT) 网络提取图像特征.
- 使用变压器和门式反复单元 (GRU) 网络进行了局部路径规划,通过控制模型输出转向角度.
主要成果:
- 端到端模型证明了临时道路的准确识别.
- 单轨迹规划时间平均约为100 ms.
- 平均轨迹偏差记录在0.689米,满足实时处理需求.
结论:
- 拟议的端到端模型对在临时道路上自动驾驶有效.
- 该模型的实时性能使低速无人驾驶车辆能够在临时的道路环境中执行跟踪任务.
- 这项研究有助于在非结构化和动态道路条件下推进自主导航能力.
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