基于标准化车牌字体的单目车辆距离估计
Physics-Grounded Monocular Vehicle Distance Estimation Using Standardized License Plate Typography
AI总结：
本文提出了一种利用美国标准化车牌字体作为被动标记进行车辆距离估计的方法，通过显式的几何先验知识解决尺度模糊问题，无需训练数据或主动照明，实现了鲁棒的距离、相对速度和碰撞预警。
摘要：
准确的车辆间距离估计是高级驾驶辅助系统（ADAS）和自动驾驶的核心。尽管LiDAR和雷达提供高精度，但其高成本限制了在大众市场车辆中的广泛应用。基于单目相机的估计提供了低成本的替代方案，但存在根本性的尺度模糊问题。最近的单目深度学习方法取得了显著成果，但需要昂贵的监督训练，存在领域偏移，并且生成的预测难以在安全关键部署中认证。本文提出了一种框架，利用美国标准化车牌字体作为被动标记进行度量测距，通过显式的几何先验知识解决尺度模糊问题，无需任何训练数据或主动照明。首先，一个四方法并行车牌检测器在全汽车照明范围内实现了稳健的车牌阅读。其次，一个三阶段状态识别引擎融合光学字符识别文本匹配、多设计颜色评分和轻量级神经网络分类器，在所有环境条件下提供稳健的识别。第三，混合深度融合与逆方差加权和在线尺度对齐，结合一维常速卡尔曼滤波器，提供平滑的距离、相对速度和时间到碰撞用于碰撞预警。在受控静态数据集上的基线验证重现了字符高度测量的2.3%系数变异和与先前工作中的车牌宽度方法相比距离估计方差减少了36%。
英文摘要：
Accurate inter-vehicle distance estimation is a cornerstone of Advanced Driver Assistance Systems (ADAS) and autonomous driving. While LiDAR and radar provide high precision, their high cost prohibits widespread adoption in mass-market vehicles. Monocular camera-based estimation offers a low-cost alternative but suffers from fundamental scale ambiguity. Recent deep learning methods for monocular depth achieve impressive results yet require expensive supervised training, suffer from domain shift, and produce predictions that are difficult to certify for safety-critical deployment. This paper presents a framework that exploits the standardized typography of United States license plates as passive fiducial markers for metric ranging, resolving scale ambiguity through explicit geometric priors without any training data or active illumination. First, a four-method parallel plate detector achieves robust plate reading across the full automotive lighting range. Second, a three-stage state identification engine fusing optical character recognition text matching, multi-design color scoring, and a lightweight neural network classifier provides robust identification across all ambient conditions. Third, hybrid depth fusion with inverse-variance weighting and online scale alignment, combined with a one-dimensional constant-velocity Kalman filter, delivers smoothed distance, relative velocity, and time-to-collision for collision warning. Baseline validation on a controlled static dataset reproduces a 2.3% coefficient of variation in character height measurements and a 36% reduction in distance-estimate variance compared with plate-width methods from prior work.
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