Target Tracking via LiDAR-RADAR Sensor Fusion for Autonomous Racing
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909898946641920 |
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| author | Cellina, Marcello Corno, Matteo Savaresi, Sergio Matteo |
| author_facet | Cellina, Marcello Corno, Matteo Savaresi, Sergio Matteo |
| contents | High Speed multi-vehicle Autonomous Racing will increase the safety and performance of road-going Autonomous Vehicles. Precise vehicle detection and dynamics estimation from a moving platform is a key requirement for planning and executing complex autonomous overtaking maneuvers. To address this requirement, we have developed a Latency-Aware EKF-based Multi Target Tracking algorithm fusing LiDAR and RADAR measurements. The algorithm explots the different sensor characteristics by explicitly integrating the Range Rate in the EKF Measurement Function, as well as a-priori knowledge of the racetrack during state prediction. It can handle Out-Of-Sequence Measurements via Reprocessing using a double State and Measurement Buffer, ensuring sensor delay compensation with no information loss. This algorithm has been implemented on Team PoliMOVE's autonomous racecar, and was proved experimentally by completing a number of fully autonomous overtaking maneuvers at speeds up to 275 km/h. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20043 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Target Tracking via LiDAR-RADAR Sensor Fusion for Autonomous Racing Cellina, Marcello Corno, Matteo Savaresi, Sergio Matteo Robotics High Speed multi-vehicle Autonomous Racing will increase the safety and performance of road-going Autonomous Vehicles. Precise vehicle detection and dynamics estimation from a moving platform is a key requirement for planning and executing complex autonomous overtaking maneuvers. To address this requirement, we have developed a Latency-Aware EKF-based Multi Target Tracking algorithm fusing LiDAR and RADAR measurements. The algorithm explots the different sensor characteristics by explicitly integrating the Range Rate in the EKF Measurement Function, as well as a-priori knowledge of the racetrack during state prediction. It can handle Out-Of-Sequence Measurements via Reprocessing using a double State and Measurement Buffer, ensuring sensor delay compensation with no information loss. This algorithm has been implemented on Team PoliMOVE's autonomous racecar, and was proved experimentally by completing a number of fully autonomous overtaking maneuvers at speeds up to 275 km/h. |
| title | Target Tracking via LiDAR-RADAR Sensor Fusion for Autonomous Racing |
| topic | Robotics |
| url | https://arxiv.org/abs/2505.20043 |