## 1 Introduction  
Autonomous driving is considered the next-generation transportation breakthrough due to its multiple benefits. According to the World Health Organization (WHO), approximately 1.3 million people die each year as a result of road traffic accidents [1]. Autonomous vehicles (AVs) have the potential to enhance road safety by applying artificial intelligence (AI) techniques in its different tasks [2]. The Society of Automotive Engineers (SAE) ranks autonomous driving from Level 0 (no automation) to Level 5 (full automation under any operational design domains (ODDs), including adverse weather conditions) [3]. Until now, Level 5 AVs have not been introduced to the market; they should be reliable in all challenging conditions in order to gain the public's confidence that they are worth the investment. Most AV applications are tested in clear weather conditions. However, adverse weather conditions cause sensors (ex: camera, LiDAR, etc.) report inaccurate data and limit their functionality.  
Many survey papers have reviewed different algorithms used for object detection in AVs [4, 5, 6, 7, 8, 9, 10, 11]. On the other hand, the performance of object-detection algorithms is greatly affected by the presence of challenging weather conditions.  
Challenging weather conditions that cause images distortions can be classified into steady and dynamic [12]. Steady weather conditions include fog, mist, and haze. While dynamic weather conditions include rain and snow. Dynamic weather conditions impose greater degradations to the scene's visibility as the droplets are bigger and of a changing pattern. These conditions reduce scene visibility and contrast, which causes a significant degradation in the process of objects detection performance by AVs.  
Rain is the most common dynamic challenging weather condition, especially in the United Kingdom [2]. Even though many researchers have achieved advancements in developing deraining methods, real-time video deraining for AV tasks have been largely understudied.  
Rain can be described as the presence of countless drops with different sizes, complex shapes, and varying speeds. Rain causes two different types of visibility degradations. Rain streaks distort the images by generating specular highlights and distorting background scene elements. On the other hand, rain streaks may accumulate generating a hazy effect, similar to fog, hence, reducing the visibility of the scene [13]. Characteristics of rain streaks and rain streaks accumulation are different; therefore, distortions they impose on images are different. Eventually, researchers address them as two separate problems.  
In this paper we aim to present a lightweight multi-stage network for single image deraining which mitigates the main two types of rain degradations. Our approach utilizes different progressively stages to reliably restore images affected by different rain degradations while maintaining low inference time. Additionally, extensive experiments are conducted to validate the effectiveness of our network on both synthetic and real rainy datasets. Moreover, despite the existing research towards the development of deraining frameworks, the effect of deraining on object detection was not sufficiently studied especially in the context of autonomous driving; therefore, we also apply object detection for comprehensively evaluating the deraining performance.  
The paper is organized as follows. Section 2 we review related deraining work. In Section 3, we introduce and discuss the proposed network. In Section 4, we evaluate the proposed network over different synthetic and real rainy datasets, also we perform sped comparisons between the proposed network and other SOTA networks, experimental results are quantitively and qualitatively presented. In Section 5, we summarized our findings. Moreover, we tabulated used abbreviations in Abbreviations section.