Deep Learning for Autonomous Vehicle Environmental Adaptation and Resilience

Abstract

In real-world scenarios, the AV performance is significantly degraded in harsh weather conditions, e.g., heavy rain, snow, fog, or interference with the sun's rays. Environmental adaptation can address the requirements to facilitate the switching of their focal perceptual tasks, and resilience can support in maintaining an acceptable level of performance under such consideration. These will be very critical for fully autonomous vehicles, where the absence of a human driver requires these systems to manage a much broader range of driving situations, including those in which they face extremely inclement or other unexpected conditions. The resilience capabilities should likely be operating in the background through normal vehicle operations, possibly only with warnings or alerts sent to the system designers for further system evaluation and potentially improved operational results. These are fundamentally deeply affecting perception, mapping, planning, and control. Thus, the primary focus of this survey is to capture all these state-of-the-art techniques applied to sensing and perceiving the surroundings of AVs as well as control technologies used to adapt to and thus overcome adverse environmental conditions.