用单芯片毫米波雷达实现低延迟高精度车辆自速估计
RadarTrack: Enhancing Ego-Vehicle Speed Estimation with Single-chip mmWave Radar
- 基于信号相位变化而非多普勒效应估算车速
- 实测在真实场景中表现稳定，支持嵌入式实时运行
- 适合对速度精度和响应速度要求高的自动驾驶应用
本文提出RadarTrack，一种基于单芯片毫米波雷达的新型自速估计框架，可在移动平台上实现鲁棒的速度感知。与依赖跨模态学习和复杂深度神经网络的方法不同，RadarTrack采用全新的基于相位的速度估计方法，有效克服了传统方法依赖多普勒测量和静态环境的局限性。该框架专为嵌入式平台设计，具备低延迟特性，适用于对速度与效率要求极高的实时应用场景。主要贡献包括提出仅依赖信号处理的新型相位式速度估计技术，并实现了经大量真实世界测试验证的实时原型系统。RadarTrack提供了一种可靠、轻量化的自速估计方案，在微型机器人、增强现实和自动驾驶等领域具有广泛应用前景。
原文摘要 · Abstract (English)
In this work, we introduce RadarTrack, an innovative ego-speed estimation framework utilizing a single-chip millimeter-wave (mmWave) radar to deliver robust speed estimation for mobile platforms. Unlike previous methods that depend on cross-modal learning and computationally intensive Deep Neural Networks (DNNs), RadarTrack utilizes a novel phase-based speed estimation approach. This method effectively overcomes the limitations of conventional ego-speed estimation approaches which rely on doppler measurements and static surrondings. RadarTrack is designed for low-latency operation on embedded platforms, making it suitable for real-time applications where speed and efficiency are critical. Our key contributions include the introduction of a novel phase-based speed estimation technique solely based on signal processing and the implementation of a real-time prototype validated through extensive real-world evaluations. By providing a reliable and lightweight solution for ego-speed estimation, RadarTrack holds significant potential for a wide range of applications, including micro-robotics, augmented reality, and autonomous navigation.
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