In this report, we describe the technical details of our submission to the
2024 RoboDrive Challenge Robust Map Segmentation Track. The Robust Map
Segmentation track focuses on the segmentation of complex driving scene
elements in BEV maps under varied driving conditions. Semantic map segmentation
provides abundant and precise static environmental information crucial for
autonomous driving systems' planning and navigation. While current methods
excel in ideal circumstances, e.g., clear daytime conditions and fully
functional sensors, their resilience to real-world challenges like adverse
weather and sensor failures remains unclear, raising concerns about system
safety. In this paper, we explored several methods to improve the robustness of
the map segmentation task. The details are as follows: 1) Robustness analysis
of utilizing temporal information; 2) Robustness analysis of utilizing
different backbones; and 3) Data Augmentation to boost corruption robustness.
Based on the evaluation results, we draw several important findings including
1) The temporal fusion module is effective in improving the robustness of the
map segmentation model; 2) A strong backbone is effective for improving the
corruption robustness; and 3) Some data augmentation methods are effective in
improving the robustness of map segmentation models. These novel findings
allowed us to achieve promising results in the 2024 RoboDrive Challenge-Robust
Map Segmentation Track.

本文描述了我们在 2024 年 RoboDrive Challenge Robust Map Segmentation Track 中提交的技术细节。我们通过探索几种方法来提高地图分割任务的稳健性，包括利用时间信息、使用不同的骨干网络以及数据增强等。通过评估结果，我们得出了一些重要的发现，包括时间融合模块在改善地图分割模型的稳健性方面是有效的，强大的骨干网络可提高其对干扰的稳定性，以及某些数据增强方法对地图分割模型的稳健性有积极影响。这些创新性的发现使我们在 2024 年 RoboDrive Challenge-Robust Map Segmentation Track 取得了令人期待的结果。

三星 - 罗斯福研究所团队：2024 机器人驱动挑战 - 鲁棒地图分割赛道技术报告

Team Samsung-RAL: Technical Report for 2024 RoboDrive Challenge-Robust  Map Segmentation Track

The objective of the collaborative vehicle-to-everything perception task is
to enhance the individual vehicle's perception capability through message
communication among neighboring traffic agents. Previous methods focus on
achieving optimal performance within bandwidth limitations and typically adopt
BEV maps as the basic collaborative message units. However, we demonstrate that
collaboration with dense representations is plagued by object feature
destruction during message packing, inefficient message aggregation for
long-range collaboration, and implicit structure representation communication.
To tackle these issues, we introduce a brand new message unit, namely point
cluster, designed to represent the scene sparsely with a combination of
low-level structure information and high-level semantic information. The point
cluster inherently preserves object information while packing messages, with
weak relevance to the collaboration range, and supports explicit structure
modeling. Building upon this representation, we propose a novel framework
V2X-PC for collaborative perception. This framework includes a Point Cluster
Packing (PCP) module to keep object feature and manage bandwidth through the
manipulation of cluster point numbers. As for effective message aggregation, we
propose a Point Cluster Aggregation (PCA) module to match and merge point
clusters associated with the same object. To further handle time latency and
pose errors encountered in real-world scenarios, we propose parameter-free
solutions that can adapt to different noisy levels without finetuning.
Experiments on two widely recognized collaborative perception benchmarks
showcase the superior performance of our method compared to the previous
state-of-the-art approaches relying on BEV maps.

协作车辆到所有感知任务通过邻近交通代理之间的消息通信提高各个车辆的感知能力。我们提出了一种全新的消息单元，即点簇，并建立了协作感知的新框架 V2X-PC，以解决稠密表示中的目标特征破坏、长距离协作中的信息聚合效率和隐含结构表示通信的问题。通过在两个被广泛认可的协作感知基准上进行实验，我们展示了我们方法相对于依赖于 BEV 地图的先前最先进方法的优越性能。

V2X-PC: 基于点簇的车联网协同感知

V2X-PC: Vehicle-to-everything Collaborative Perception via Point Cluster

The limited sensing resolution of resource-constrained off-road vehicles
poses significant challenges towards reliable off-road autonomy. To overcome
this limitation, we propose a general framework based on fusing the future
information (i.e. future fusion) for self-supervision. Recent approaches
exploit this future information alongside the hand-crafted heuristics to
directly supervise the targeted downstream tasks (e.g. traversability
estimation). However, in this paper, we opt for a more general line of
development - time-efficient completion of the highest resolution (i.e. 2cm per
pixel) BEV map in a self-supervised manner via future fusion, which can be used
for any downstream tasks for better longer range prediction. To this end,
first, we create a high-resolution future-fusion dataset containing pairs of
(RGB / height) raw sparse and noisy inputs and map-based dense labels. Next, to
accommodate the noise and sparsity of the sensory information, especially in
the distal regions, we design an efficient realization of the Bayes filter onto
the vanilla convolutional network via the recurrent mechanism. Equipped with
the ideas from SOTA generative models, our Bayesian structure effectively
predicts high-quality BEV maps in the distal regions. Extensive evaluation on
both the quality of completion and downstream task on our future-fusion dataset
demonstrates the potential of our approach.

通过未来融合的自我监督学习方法，提出解决资源受限型越野车辆感应分辨率有限的问题，以完成高分辨率 BEV 地图的自主预测，为后续任务提供更好的长期预测能力。