首个面向内河自动驾驶的4D雷达相机跟踪数据集，助力水上交通智能化
USVTrack: USV-Based 4D Radar-Camera Tracking Dataset for Autonomous Driving in Inland Waterways
- 基于无人船搭载4D雷达与单目相机采集多模态数据
- 涵盖多种水道、时段与天气，共30+小时真实场景
- 提出易集成的雷达相机匹配方法，提升跟踪精度
内河水域目标跟踪在水上运输、观光旅游、环境监测和水面救援等安全高效应用中至关重要。本研究采用配备4D雷达、单目相机、GPS与惯导（IMU）的无人船（USV），在复杂水域环境中实现鲁棒目标跟踪。通过该平台采集了丰富多样的物体跟踪数据，构建了首个专为新一代水路自动驾驶系统设计的4D雷达-相机跟踪数据集——USVTrack。数据集涵盖多种水道类型、不同时段及多变气象与光照条件，具有30+小时真实采集时长。此外，我们提出一种简单有效的雷达-相机匹配方法（RCM），可无缝接入主流两阶段关联追踪器。实验表明，使用RCM显著提升了水路自动驾驶场景下的目标跟踪准确率与可靠性。数据集已公开于https://usvtrack.github.io。
原文摘要 · Abstract (English)
Object tracking in inland waterways plays a crucial role in safe and cost-effective applications, including waterborne transportation, sightseeing tours, environmental monitoring and surface rescue. Our Unmanned Surface Vehicle (USV), equipped with a 4D radar, a monocular camera, a GPS, and an IMU, delivers robust tracking capabilities in complex waterborne environments. By leveraging these sensors, our USV collected comprehensive object tracking data, which we present as USVTrack, the first 4D radar-camera tracking dataset tailored for autonomous driving in new generation waterborne transportation systems. Our USVTrack dataset presents rich scenarios, featuring diverse various waterways, varying times of day, and multiple weather and lighting conditions. Moreover, we present a simple but effective radar-camera matching method, termed RCM, which can be plugged into popular two-stage association trackers. Experimental results utilizing RCM demonstrate the effectiveness of the radar-camera matching in improving object tracking accuracy and reliability for autonomous driving in waterborne environments. The USVTrack dataset is public on https://usvtrack.github.io.
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