Abstract
The most significant part in the design and operation of a self-driving vehicle is the detection of objects in its operating environment. Object detection becomes more challenging during adverse weather conditions like rain, fog, snow etc. The performance of the contemporary methods and technologies for object detection is significantly compromised in adverse weather conditions. This study presents a comprehensive review of the up-to-date technologies of object detection in different weather conditions. A comparison on various sensors and existing sensor-fusion techniques along with image pre-processing systems are looked upon in detail. Moreover, the Deep learning (DL) systems are given utmost stress as it stands close to human intelligence, when it comes to object detection, adaptation and decision making. Ultimately, the upshot of the review is encapsulated and possible research directions are put forward towards the development of a fully functional perception system irrespective of the operating environment.
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