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arXiv Machine Learning By Gaofeng Dong, Vamsi Eyunni, Pragya Sharma, Kang Yang, Mani Srivastava

Beyond Direct Sensing: Harnessing Indirect Observations from Third-Party Sensors in Vehicle Tracking

Read the original on arXiv Machine Learning →

The paper introduces GrayTrack, a vehicle‑tracking system that fuses weak, indirect observations from third‑party sensors with sparse direct sensor data using a road‑constrained particle filter. Experiments on a CARLA‑Mininet‑WiFi pipeline show that the system achieves an F1 score of 0.989 for anonymous vehicle passages and reduces trajectory RMSE by 60.1% while cutting catastrophic track loss from 35.8% to 0.3%. These results demonstrate that incorporating indirect third‑party observations can substantially extend tracking capabilities when direct sensor access is limited.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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