Bird's Eye View (BEV) map prediction is essential for downstream autonomous driving tasks like trajectory prediction. In the past, this was accomplished through the use of a sophisticated sensor configuration that captured a surround view from multiple cameras. However, in large-scale production, cost efficiency is an optimization goal, so that using fewer cameras becomes more relevant. But the consequence of fewer input images correlates with a performance drop. This raises the problem of developing a BEV perception model that provides a sufficient performance on a low-cost sensor setup. Although, primarily relevant for inference time on production cars, this cost restriction is less problematic on a test vehicle during training. Therefore, the objective of our approach is to reduce the aforementioned performance drop as much as possible using a modern multi-camera surround view model reduced for single-camera inference. The approach includes three features, a modern masking technique, a cyclic Learning Rate (LR) schedule, and a feature reconstruction loss for supervising the transition from six-camera inputs to one-camera input during training. Our method outperforms versions trained strictly with one camera or strictly with six-camera surround view for single-camera inference resulting in reduced hallucination and better quality of the BEV map.

本研究解决了在低成本传感器设置下，鸟瞰图（BEV）感知模型性能下降的问题。提出了一种新颖的方法，结合现代遮罩技术、循环学习率调度和特征重构损失，优化了单摄像头推断中的性能表现。研究表明，该方法在单摄像头推断中效果优于仅使用单摄像头或六摄像头训练的模型，减少了幻觉现象，并提升了BEV地图的质量。

通过多摄像头训练改善单摄像头的鸟瞰图感知