在曲线中的车道检测算法基于多传感器融合
Qiang Zhang1, Jianze Liu1, Xuedong Jiang1
1School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
这项研究通过整合方向盘角度传感器和双眼相机来改善自动驾驶的车道检测. 改进的算法准确地跟踪高曲率道路上的车道标记,提高车辆安全.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 汽车工程 汽车工程
背景情况:
- 车道检测对于先进的驾驶辅助系统 (ADAS) 和自动驾驶至关重要.
- 传统的滑动窗算法在高曲率的路段上遇到了困难.
- 大曲率曲线是当前车道检测系统的常见和具有挑战性的场景.
研究的目的:
- 为了提高大曲率曲线中滑动窗口车道检测算法的性能.
- 为辅助和自动驾驶系统开发更强大的车道检测方法.
- 为了改善在具有挑战性的道路几何形状中跟踪和识别车道标记.
主要方法:
- 提出了改进的滑窗车道检测算法.
- 整合方向盘角度传感器和双眼相机.
- 使用前一的方向盘角度来预测滑动窗的搜索中心.
- 使用双眼镜摄像头来协助初始的滑动窗口定位.
主要成果:
- 改进的算法在识别和跟踪高曲率曲线上的车道线路方面表现出卓越的性能.
- 模拟和实验结果验证了拟议方法的有效性.
- 与传统方法相比,传感器数据的整合提高了车道检测的准确性.
结论:
- 拟议的滑窗车道检测方法有效地解决了大型曲率曲线中的传统算法的局限性.
- 整合方向盘角度和双眼相机数据显著提高了车道跟踪的稳定性.
- 这一进步有助于实现更安全,更可靠的自动驾驶系统.
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