arXiv subjects

Qiuyu Xu

Publications and source records attributed to Qiuyu Xu.

3 recordsLinked to original sources

arXiv2024

Applying Extended Object Tracking for Self-Localization of Roadside Radar Sensors

Intelligent Transportation Systems (ITS) can benefit from roadside 4D mmWave radar sensors for large-scale traffic monitoring due to their weatherproof functionality, long sensing range and low manufacturing cost. However, the localization method using external measurement devices has limitations in urban environments. Furthermore, if the sensor mount exhibits changes due to environmental influences, they cannot be corrected when the measurement is performed only during the installation. In this paper, we propose self-localization of roadside radar data using Extended Object Tracking (EOT). The method analyses both the tracked trajectories of the vehicles observed by the sensor and the aerial laser scan of city streets, assigns labels of driving behaviors such as "straight ahead", "left turn", "right turn" to trajectory sections and road segments, and performs Semantic Iterative Closest Points (SICP) algorithm to register the point cloud. The method exploits the result from a down stream task -- object tracking -- for localization. We demonstrate high accuracy in the sub-meter range along with very low orientation error. The method also shows good data efficiency. The evaluation is done in both simulation and real-world tests.

cs.RO↗

arXiv2024

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.

cs.RO↗

arXiv2023

Streamlined Data Fusion: Unleashing the Power of Linear Combination with Minimal Relevance Judgments

Linear combination is a potent data fusion method in information retrieval tasks, thanks to its ability to adjust weights for diverse scenarios. However, achieving optimal weight training has traditionally required manual relevance judgments on a large percentage of documents, a labor-intensive and expensive process. In this study, we investigate the feasibility of obtaining near-optimal weights using a mere 20\%-50\% of relevant documents. Through experiments on four TREC datasets, we find that weights trained with multiple linear regression using this reduced set closely rival those obtained with TREC's official "qrels." Our findings unlock the potential for more efficient and affordable data fusion, empowering researchers and practitioners to reap its full benefits with significantly less effort.

cs.IR↗