Change Management in Environmental Adaptation and Resilience for Autonomous Vehicles: Leveraging Deep Learning for Enhanced Performance
Keywords:
autonomous vehiclesAbstract
In real-world scenarios, the performance of autonomous vehicles (AVs) can be significantly degraded by harsh weather conditions, such as heavy rain, snow, fog, or interference from the sun’s rays. Environmental adaptation is essential to facilitate the adjustment of focal perceptual tasks, while resilience ensures that AVs maintain an acceptable level of performance under challenging conditions. These factors are critical for fully autonomous vehicles, which, in the absence of a human driver, must manage a broader range of driving situations, including those involving inclement or unexpected conditions. Resilience capabilities should ideally operate in the background during normal vehicle operations, providing alerts to system designers for further evaluation and potential improvements. These adaptations fundamentally impact perception, mapping, planning, and control. Therefore, this survey focuses on state-of-the-art techniques in deep learning applied to sensing and perceiving the environments of AVs, as well as the control technologies utilized to adapt to and overcome adverse environmental challenges, all within the framework of effective change management strategies.
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