Driving systems often rely on high-definition (HD) maps for precise
environmental information, which is crucial for planning and navigation. While
current HD map constructors perform well under ideal conditions, their
resilience to real-world challenges, \eg, adverse weather and sensor failures,
is not well understood, raising safety concerns. This work introduces MapBench,
the first comprehensive benchmark designed to evaluate the robustness of HD map
construction methods against various sensor corruptions. Our benchmark
encompasses a total of 29 types of corruptions that occur from cameras and
LiDAR sensors. Extensive evaluations across 31 HD map constructors reveal
significant performance degradation of existing methods under adverse weather
conditions and sensor failures, underscoring critical safety concerns. We
identify effective strategies for enhancing robustness, including innovative
approaches that leverage multi-modal fusion, advanced data augmentation, and
architectural techniques. These insights provide a pathway for developing more
reliable HD map construction methods, which are essential for the advancement
of autonomous driving technology. The benchmark toolkit and affiliated code and
model checkpoints have been made publicly accessible.

这项工作介绍了 MapBench，它是第一个旨在评估高清地图构建方法对各种传感器损坏的鲁棒性的全面基准。我们的基准包括来自相机和 LiDAR 传感器的共 29 种类型的损坏。对 31 种高清地图构建方法进行的广泛评估揭示了在恶劣天气条件和传感器故障下现有方法的显著性能下降，强调了重要的安全问题。我们确定了提高鲁棒性的有效策略，包括利用多模态融合、先进的数据增强和架构技术的创新方法。这些信息为开发更可靠的高清地图构建方法提供了一个路径，这对于自动驾驶技术的进步至关重要。基准工具包以及相关的代码和模型检查点已经公开可访问。

您的高精地图构建器在传感器异常下是否可靠？

Is Your HD Map Constructor Reliable under Sensor Corruptions?

Tactical decision making is a critical feature for advanced driving systems,
that incorporates several challenges such as complexity of the uncertain
environment and reliability of the autonomous system. In this work, we develop
a multi-modal architecture that includes the environmental modeling of ego
surrounding and train a deep reinforcement learning (DRL) agent that yields
consistent performance in stochastic highway driving scenarios. To this end, we
feed the occupancy grid of the ego surrounding into the DRL agent and obtain
the high-level sequential commands (i.e. lane change) to send them to
lower-level controllers. We will show that dividing the autonomous driving
problem into a multi-layer control architecture enables us to leverage the AI
power to solve each layer separately and achieve an admissible reliability
score. Comparing with end-to-end approaches, this architecture enables us to
end up with a more reliable system which can be implemented in actual
self-driving cars.

本研究开发了一个多模态架构，包括自我周围环境的建模，并训练了一个深度强化学习代理，以在随机高速公路驾驶场景中实现一致的性能，将自主驾驶问题分成多层控制结构使我们能够将 AI 的能力分别用于解决每一层，最终实现更可靠的系统，可在实际的自动驾驶汽车中实现。