论文标题
在鸟眼中的物体和区域之间校准的感知不确定性
Calibrated Perception Uncertainty Across Objects and Regions in Bird's-Eye-View
论文作者
论文摘要
在驾驶方案较差或可见性或阻塞的情况下，重要的是，自动驾驶汽车在做出驾驶决策时（包括选择安全速度）时要考虑所有不确定性。然后必须伴随良好的不确定性估计值，基于网格的感知输出（例如占用网格和基于对象的输出），例如检测到的对象列表。我们重点介绍了最先进的局限性，并提出了一组更完整的不确定性，尤其是包括未发现的对象概率。我们提出了一种新的方法，以在烈火模型的示例中获取这些概率输出从鸟类视图概率的语义分割。我们证明所获得的概率未经现成的概率，并提出了实现良好校准不确定性的方法。
In driving scenarios with poor visibility or occlusions, it is important that the autonomous vehicle would take into account all the uncertainties when making driving decisions, including choice of a safe speed. The grid-based perception outputs, such as occupancy grids, and object-based outputs, such as lists of detected objects, must then be accompanied by well-calibrated uncertainty estimates. We highlight limitations in the state-of-the-art and propose a more complete set of uncertainties to be reported, particularly including undetected-object-ahead probabilities. We suggest a novel way to get these probabilistic outputs from bird's-eye-view probabilistic semantic segmentation, in the example of the FIERY model. We demonstrate that the obtained probabilities are not calibrated out-of-the-box and propose methods to achieve well-calibrated uncertainties.
