How to Cite
Abstract
When a vehicle perceives its surroundings under different driving scenarios (e.g., poor weather and occlusion) with multiple sensors, it obtains diverse observation data. Different sensors focus on different objects and situations. For example, LiDAR works well in both day and night conditions, but its performance degrades for bad weather. In contrast, radar maintains stable performance under adverse weather and light conditions and remains a reliable choice for vehicles. Camera perception has been very much challenged under various circumstances, such as severe weather conditions, or light and shadow interference. Sensory homogeneity provides little help here, because it will also be impacted by inadaptable adverse impact factors [1]. Independently perceiving the environment or only considering the multi-sensor difference in AV coordination creates a new “central perception” approach, in which decision-making and action output are based solely on local observations. This approach preventively circumvents multi-sensor redundancy observation issues and still produces safety decision making when individual sensors fail under certain adversities.
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