Navigating autonomous vehicles in adverse weather conditions such as extreme rain, fog, and clouds poses significant challenges in efficiently identifying road elements and performing path planning. These difficulties are further compounded by the frequent transitions between diverse weather conditions, such as shifting from cloudy to rainy or rainy to sunny. Existing research for autonomous vehicles has the least focus on performing object detection in transitional weather conditions. Also, autonomous driving data is vulnerable to minor perturbations or noises, leading to unpredictable outcomes, particularly in transitional weather conditions. In addition, current adversarial attacks demand significant computational resources and have limited real-world applicability due to the large number of queries and computational resources. To address these limitations, we propose a novel minimalistic method called explainable black-box adversarial detection attack in transitional weather conditions for autonomous driving (TransWardX). Our attention-guided attack is minimalist, targeting limited image regions to deceive the model effectively. We assess our attack using a continuous weather-driving dataset called AIWD6. Later, we also evaluate our attack with other datasets like BDD100K and GTSRB. Our results demonstrate the effectiveness of TransWardX, achieving a high success rate with minimal perturbations, fewer iterations, and a drastic reduction of 50% in computational requirements while maintaining low average precision.