Scalable Radar-based Roadside Perception: Self-localization and Occupancy Heat Map for Traffic Analysis
<|reference_start|>Scalable Radar-based Roadside Perception: Self-localization and Occupancy Heat Map for Traffic Analysis: 4D mmWave radar sensors are suitable for roadside perception in city-scale Intelligent Transportation Systems (ITS) due to their long sensing range, weatherproof functionality, simple mechanical design, and low manufacturing cost. In this work, we investigate radar-based ITS for scalable traffic analysis. Localization of these radar sensors at city scale is a fundamental task in ITS. For flexible sensor setups, it requires even more effort. To address this task, we propose a self-localization approach that matches two descriptions of the "road": the one from the geometry of the motion trajectories of cumulatively observed vehicles, and the other one from the aerial laser scan. An Iterative Closest Point (ICP) algorithm is used to register the motion trajectory in the road section of the laser scan. The resulting estimate of the transformation matrix represents the sensor pose in a global reference frame. We evaluate the results and show that it outperforms other map-based radar localization methods, especially for the orientation estimation. Beyond the localization result, we project radar sensor data onto a city-scale laser scan and generate a scalable occupancy heat map as a traffic analysis tool. This is demonstrated using two radar sensors monitoring an urban area in the real world.<|reference_end|>
@article{han2024scalable,
    title={Scalable Radar-based Roadside Perception: Self-localization and
  Occupancy Heat Map for Traffic Analysis},
    author={Longfei Han, Qiuyu Xu, Klaus Kefferp"utz, Ying Lu, Gordon Elger and
  J"urgen Beyerer},
    journal={arXiv preprint arXiv:2404.01166},
    year={2024},
    doi={10.1109/IV55156.2024.10588397},
    archivePrefix={arXiv},
    eprint={2404.01166},
    primaryClass={cs.RO}
}