[论文解读] Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey

Yuxiao Zhang, Alexander Carballo+2 more|arXiv (Cornell University)|Dec 16, 2021

Air Quality Monitoring and Forecasting被引用 24

一句话总结

本综述分析恶劣天气如何影响 ADS 传感器，评述感知增强与传感器鲁棒性技术，并讨论数据集、仿真器与未来方向。

ABSTRACT

Automated Driving Systems (ADS) open up a new domain for the automotive industry and offer new possibilities for future transportation with higher efficiency and comfortable experiences. However, autonomous driving under adverse weather conditions has been the problem that keeps autonomous vehicles (AVs) from going to level 4 or higher autonomy for a long time. This paper assesses the influences and challenges that weather brings to ADS sensors in an analytic and statistical way, and surveys the solutions against inclement weather conditions. State-of-the-art techniques on perception enhancement with regard to each kind of weather are thoroughly reported. External auxiliary solutions, weather conditions coverage in currently available datasets, simulators, and experimental facilities with weather chambers are distinctly sorted out. Additionally, potential future ADS sensors candidates and approaches beyond common senses are provided. By looking into all kinds of major weather problems the autonomous driving field is currently facing, and reviewing both hardware and computer science solutions in recent years, this survey points out the main moving trends of adverse weather problems in autonomous driving, i.e., advanced sensor fusions, more sophisticated networks, and V2X & IoT technologies; and also the limitations brought by emerging 1550 nm LiDARs. In general, this work contributes a holistic overview of the obstacles and directions of ADS development in terms of adverse weather driving conditions.

研究动机与目标

• 评估天气现象对 ADS 传感器及感知任务的影响。
• 调研在恶劣天气下用于感知增强的硬件与算法解决方案。
• 编目用于天气相关 ADS 研究的数据集、仿真器和设施。
• 识别在恶劣天气中实现鲁棒自动驾驶的趋势与未来方向。

提出的方法

• 对天气对 ADS 传感器的影响进行统计与分析评估。
• 回顾用于天气鲁棒性的传感器融合与机械解决方案。
• 按天气类型对感知增强方法进行分类并给出实验验证。
• 讨论研究中使用的数据集、仿真器与天气设施。
• 强调在恶劣条件下朝向高级融合、网络与 V2XIoT 的趋势。

实验结果

研究问题

• RQ1 降雨、雾、雪及其他恶劣天气现象如何定量影响 LiDAR、摄像头、雷达及其他传感器？
• RQ2 哪些感知增强与传感器鲁棒性技术可缓解天气引起的性能下降？
• RQ3 现存哪些用于在恶劣天气下研究 ADS 的数据集、仿真器与设施，以及它们的局限性？
• RQ4 在使 ADS 对恶劣天气鲁棒方面的主要趋势与未来方向是什么？

主要发现

• 天气现象对传感器与感知产生可测量的影响，且在各模态间严重程度不同。
• 将先进的传感器融合、更加复杂的网络，以及 V2X/IOT 技术被确定为鲁棒性的重要发展趋势。
• 讨论新兴的 1550 nm LiDARs 的局限性，作为天气鲁棒感知的瓶颈。
• 该综述提供一个将硬件与计算机科学解决方案联系起来的整体性综述，用于恶劣天气。
• 在恶劣天气下的感知增强方法已有实验验证。