Paper ID: 2408.11196 • Published Aug 20, 2024
Robust Long-Range Perception Against Sensor Misalignment in Autonomous Vehicles
Zi-Xiang Xia, Sudeep Fadadu, Yi Shi, Louis Foucard
TL;DR
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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.