Jewoo Park, Nhi Quach, Yonghwi Kim, Ruey-Hwa Cheng, Michal Jenco, Chenxi Yin, Alex K. Lee, Yoonjin Won
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Machine Learning Analysis of Autonomous Vehicle Sensors Under Extreme Conditions in Alaska
Abstract Autonomous vehicles are part of an expanding industry that encompasses various interdisciplinary fields such as dynamic controls, thermal engineering, sensors, data processing, and artificial intelligence. Exposure to extreme environmental conditions, such as changes to temperature and humidity, affects sensor performance. To address potential safety concerns related to sensor perception used in autonomous vehicles in extremely cold real-world situations, specifically Alaska, examination of frosts and water droplets impact on vehicle optical sensors is conducted in both real-world and laboratory-controlled settings. Machine learning models are utilized to determine the vision impediment levels. Potential hardware and software tools are then introduced as solutions for the environmental impacts. Through this research, a better understanding of the potential caveats and algorithm solutions can be suggested to improve autonomous driving, even under challenging weather conditions.
期刊介绍:
The Journal of Electronic Packaging publishes papers that use experimental and theoretical (analytical and computer-aided) methods, approaches, and techniques to address and solve various mechanical, materials, and reliability problems encountered in the analysis, design, manufacturing, testing, and operation of electronic and photonics components, devices, and systems.
Scope: Microsystems packaging; Systems integration; Flexible electronics; Materials with nano structures and in general small scale systems.