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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.11196 |
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| _version_ | 1866916642740502528 |
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| author | Xia, Zi-Xiang Fadadu, Sudeep Shi, Yi Foucard, Louis |
| author_facet | Xia, Zi-Xiang Fadadu, Sudeep Shi, Yi Foucard, Louis |
| contents | Advances in machine learning algorithms for sensor fusion have significantly improved the detection and prediction of other road users, thereby enhancing safety. However, even a small angular displacement in the sensor's placement can cause significant degradation in output, especially at long range. In this paper, we demonstrate a simple yet generic and efficient multi-task learning approach that not only detects misalignment between different sensor modalities but is also robust against them for long-range perception. Along with the amount of misalignment, our method also predicts calibrated uncertainty, which can be useful for filtering and fusing predicted misalignment values over time. In addition, we show that the predicted misalignment parameters can be used for self-correcting input sensor data, further improving the perception performance under sensor misalignment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_11196 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Robust Long-Range Perception Against Sensor Misalignment in Autonomous Vehicles Xia, Zi-Xiang Fadadu, Sudeep Shi, Yi Foucard, Louis Computer Vision and Pattern Recognition Advances in machine learning algorithms for sensor fusion have significantly improved the detection and prediction of other road users, thereby enhancing safety. However, even a small angular displacement in the sensor's placement can cause significant degradation in output, especially at long range. In this paper, we demonstrate a simple yet generic and efficient multi-task learning approach that not only detects misalignment between different sensor modalities but is also robust against them for long-range perception. Along with the amount of misalignment, our method also predicts calibrated uncertainty, which can be useful for filtering and fusing predicted misalignment values over time. In addition, we show that the predicted misalignment parameters can be used for self-correcting input sensor data, further improving the perception performance under sensor misalignment. |
| title | Robust Long-Range Perception Against Sensor Misalignment in Autonomous Vehicles |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.11196 |