The fusion of raw features from multiple sensors on an autonomous vehicle to
create a Bird's Eye View (BEV) representation is crucial for planning and
control systems. There is growing interest in using deep learning models for
BEV semantic segmentation. Anticipating segmentation errors and improving the
explainability of DNNs is essential for autonomous driving, yet it is
under-studied. This paper introduces a benchmark for predictive uncertainty
quantification in BEV segmentation. The benchmark assesses various approaches
across three popular datasets using two representative backbones and focuses on
the effectiveness of predicted uncertainty in identifying misclassified and
out-of-distribution (OOD) pixels, as well as calibration. Empirical findings
highlight the challenges in uncertainty quantification. Our results find that
evidential deep learning based approaches show the most promise by efficiently
quantifying aleatoric and epistemic uncertainty. We propose the
Uncertainty-Focal-Cross-Entropy (UFCE) loss, designed for highly imbalanced
data, which consistently improves the segmentation quality and calibration.
Additionally, we introduce a vacuity-scaled regularization term that enhances
the model's focus on high uncertainty pixels, improving epistemic uncertainty
quantification.

这篇论文介绍了一种用于预测不确定性量化的基准测试方法，通过评估不同方法在三个流行数据集上对于识别被错分和不适当类别像素、以及校准的有效性，发现证据深度学习方法在高效量化不确定性方面表现最有潜力，提出了适用于高度不平衡数据的 Uncertainty-Focal-Cross-Entropy (UFCE) 损失函数，以及增强模型对高不确定性像素关注的缺空缩放规则项能够提高确定性不确定性量化。

鸟瞰视野语义分割的不确定性量化：方法与基准

Uncertainty Quantification for Bird's Eye View Semantic Segmentation:  Methods and Benchmarks

Collaborative trajectory prediction can comprehensively forecast the future
motion of objects through multi-view complementary information. However, it
encounters two main challenges in multi-drone collaboration settings. The
expansive aerial observations make it difficult to generate precise Bird's Eye
View (BEV) representations. Besides, excessive interactions can not meet
real-time prediction requirements within the constrained drone-based
communication bandwidth. To address these problems, we propose a novel
framework named "Drones Help Drones" (DHD). Firstly, we incorporate the ground
priors provided by the drone's inclined observation to estimate the distance
between objects and drones, leading to more precise BEV generation. Secondly,
we design a selective mechanism based on the local feature discrepancy to
prioritize the critical information contributing to prediction tasks during
inter-drone interactions. Additionally, we create the first dataset for
multi-drone collaborative prediction, named "Air-Co-Pred", and conduct
quantitative and qualitative experiments to validate the effectiveness of our
DHD framework.The results demonstrate that compared to state-of-the-art
approaches, DHD reduces position deviation in BEV representations by over 20%
and requires only a quarter of the transmission ratio for interactions while
achieving comparable prediction performance. Moreover, DHD also shows promising
generalization to the collaborative 3D object detection in CoPerception-UAVs.

我们提出了一种名为 “无人机互助” 的新框架，用于解决无人机协同预测中的两个主要挑战，通过地面先验和基于局部特征差异的选择机制来实现更准确的 BEV 生成和满足实时预测需求，相比现有方法，DHD 在 BEV 表示中减少位置偏差超过 20%，仅需要四分之一的传输比率来实现可比较的预测性能，同时还显示出在协同感知 - UAVs 中的协同三维物体检测上有很好的推广能力。

无人机协同框架：多无人机物体轨迹预测与更多应用

Drones Help Drones: A Collaborative Framework for Multi-Drone Object  Trajectory Prediction and Beyond

Sim2Real transfer has gained popularity because it helps transfer from
inexpensive simulators to real world. This paper presents a novel system that
fuses components in a traditional \textit{World Model} into a robust system,
trained entirely within a simulator, that \textit{Zero-Shot} transfers to the
real world. To facilitate transfer, we use an intermediary representation that
are based on \textit{Bird's Eye View (BEV)} images. Thus, our robot learns to
navigate in a simulator by first learning to translate from complex
\textit{First-Person View (FPV)} based RGB images to BEV representations, then
learning to navigate using those representations. Later, when tested in the
real world, the robot uses the perception model that translates FPV-based RGB
images to embeddings that are used by the downstream policy. The incorporation
of state-checking modules using \textit{Anchor images} and \textit{Mixture
Density LSTM} not only interpolates uncertain and missing observations but also
enhances the robustness of the model when exposed to the real-world
environment. We trained the model using data collected using a
\textit{Differential drive} robot in the CARLA simulator. Our methodology's
effectiveness is shown through the deployment of trained models onto a
\textit{Real world Differential drive} robot. Lastly we release a comprehensive
codebase, dataset and models for training and deployment that are available to
the public.

一种使用 Bird's Eye View 图像作为中间表示的系统，在模拟器中训练并能够通过零样本转移到真实世界，在真实世界环境中通过 Anchor 图像和混合密度 LSTM 增强鲁棒性，通过 Differential drive 机器人在 CARLA 模拟器中的训练和部署证明了该方法的有效性，并公开了相关代码、数据集和模型。