Authors - M Nanda Kumar, Harsh Sharma, Rajan Kakkar, Tushar Naha, Atul, Rishabh Yadav, Naveen Abstract - The demand for autonomous vehicles (AVs) has grown rapidly due to their potential to revolutionize transportation by enhancing safety, efficiency, and convenience while reducing human error, a leading cause of road accidents. AVs leverage advanced technologies like machine learning, LIDAR, GPS, cameras, RADAR, and ultrasonic sensors for precise navigation, obstacle detection, and real-time decision-making. However, their reliability and safety in di-verse environmental conditions remain a significant challenge. Extreme weather events such as heavy rain, snow, fog, ice, hail, and dust storms can impair sensor performance, reducing visibility, traction, and the ability to detect road markings, obstacles, and other vehicles. These conditions degrade the accuracy of critical systems like LIDAR, RADAR, and cameras, raising concerns about AVs’ reliability, particularly in emergencies or unpredictable scenarios. This review paper explores the effects of adverse weather on AVs’ performance, analyzing the limitations of key sensors and assessing various mitigation strategies to enhance their resilience. By identifying technological gaps and emphasizing the need for weather-resilient solutions, the paper aims to guide future research and innovation to improve AVs’ safety and reliability in challenging real-world conditions, ensuring their readiness for broader deployment.
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