使用低成本传感器进行延迟补偿车道坐标车辆状态估计
Minsu Kim1, Weonmo Kang1, Changsun Ahn1
1School of Mechanical Engineering, Pusan National University, Busan 46241, Republic of Korea.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
本研究引入了一种使用低成本传感器精确估计车辆状态的新方法,这对于先进的驾驶辅助系统 (ADAS) 和自动驾驶至关重要. 这种方法有效地弥补信号延迟,提高安全性和可靠性.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 计算机视觉用于自主系统
- 对于车辆动力学的传感器融合.
背景情况:
- 在车道坐标系统中对强大的车辆状态估计对于高级驾驶辅助系统 (ADAS) 和自动驾驶至关重要.
- 像摄像头,IMU和转向角度传感器这样的低成本传感器由于复杂的车辆动态和视觉系统信号延迟而带来了挑战.
- 准确的实时估计横向位置,速度和方向角度是很难实现现有的方法.
研究的目的:
- 开发基于车道坐标的车辆状态估计器,克服低成本传感器和信号延迟的局限性.
- 为了实现对ADAS和自动驾驶应用程序的关键车辆状态的可靠和持续估计.
- 与传统方法相比,提高车辆状态估计的准确性和减少相位滞后.
主要方法:
- 基于车辆动态的自行车模型与扩展卡尔曼波器 (EKF) 的集成.
- 实现信号延迟补偿算法,在摄像头处理过程中预测车辆状态演变.
- 开发一条高效的摄像头处理管道,预先训练有素的网络用于车道细分和状态提取.
主要成果:
- 拟议的估计器提供连续的,高精度的,以及延迟补偿的车道坐标为基础的车辆状态.
- 与仅使用摄像头的方法和没有延迟补偿的估计器相比,估计错误和阶段滞后的显著减少.
- 通过使用真实车辆驾驶数据在直线和曲线道路上成功验证.
结论:
- 开发的估计器有效地解决了使用低成本传感器基于车道的车辆状态估计的挑战.
- 该方法可用于ADAS和自动驾驶,可靠地实时获取关键车辆状态信息.
- 该方法为提高自动驾驶系统的安全性和性能提供了一种实际解决方案.
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