自动驾驶车辆中针对传感器未对准的鲁棒远距离感知
Robust Long-Range Perception Against Sensor Misalignment in Autonomous Vehicles
AI总结：
本文提出一种简单高效的多任务学习方法，能检测自动驾驶传感器模态间的未对准并保持远距离感知鲁棒性，同时预测校准不确定性用于时序过滤融合，并能自校正输入数据以提升性能。
摘要：
机器学习算法在传感器融合方面的进展显著提升了对其他道路使用者的检测和预测能力，从而增强了安全性。然而，传感器安装中即使是微小的角位移也可能导致输出显著退化，尤其是在远距离时。在本文中，我们展示了一种简单、通用且高效的多任务学习方法，它不仅能检测不同传感器模态之间的未对准，还能在远距离感知中对其保持鲁棒性。除了未对准量之外，我们的方法还预测校准后的不确定性，这对于随时间过滤和融合预测的未对准值很有用。此外，我们表明预测的未对准参数可用于自校正输入传感器数据，进一步提升了传感器未对准下的感知性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
