## 2 Literature Review  
Many researchers and authors conducted research and presented their results. For instance, [PERSON] et al., in 2019, [3] conducted research on the personal and societal benefits of ADAS systems such as blind-spot monitoring, lane departure warning, and forward-collision warning in light-duty vehicles sold in the United States in 2015. Their research estimated that the technologies above can collectively prevent up to 1.6 million crashes every year, including 7200 fatal crashes. Thus, ADAS adoption will lead to positive personal and societal benefits. A study [4] evaluated the field effectiveness of ADAS technologiessuch as frontal collision warning with autonomous emergency braking, lane departure warning, and blind-spot detection deployed in BMW cars sold in the United States between 2014 and 2017 in preventing moderate-to-severe crashes. Their research shows that in the 2014 model year, vehicles equipped with ADAS were 13% less likely to crash than their non-ADAS versions. Similarly, in the 2017 model year, vehicles were 34% less likely to crash when all the systems above were deployed together. Enhancing the hazard warning systems available to drivers and automated systems such as AEBS (advanced emergency braking systems), pedestrian warning, and blind-spot warning systems with visual and auditory warning can help improve the driver's behavior on the road. [PERSON] in 2018 [5] evaluated driver behavior in a trial of retrofitting the CAT (collision avoidance technology) ADAS system in government fleet vehicles, which provided LDW and FCW (forward collision warning) in the form of audio and visual warnings to drivers. The analysis from the driver feedback survey and other vehicle parameters from the CAT system indicated that the system effectively improved drivers' behavior on-road and helped reduce crashes. Nevertheless, the drivers felt that such a system was distracting and would not use it themselves but agreed that it would improve overall road safety. The authors suggested that optimism bias might be a reason for such feedback from the drivers and concluded that appropriate training about the CAT system will be useful in increasing the acceptance and perception of such a system among drivers. This was reiterated by [PERSON] et al. [6], that there is a need for proper driver education on ADAS systems to improve driver behavior and effective system use in improving overall road safety and preventing collisions. Lack of standardization and appropriate visual guides leads to an incomplete understanding of the system's behavior and limitations. Camera sensors that provide computer vision-based object detection capability using deep learning-based algorithms are a vital part of ADAS across all levels of automation. From a simple level 0 camera-based parking assist system to a comprehensive level 5 fully autonomous vehicle, the computer vision-based object detection system acts as the eyes in the environment to the ADAS based on which crucial warnings, decisions, and automation tasks are accomplished [7]. A study by [PERSON] et al. [8] compared the performance of two state-of-the-art convolutional neural networks (CNN) algorithms, namely, faster R-CNN (region-based CNN) and YOLOv3, for detecting cars from aerial images. Both models were trained and tested on a large car dataset taken from UAVs. These authors demonstrated in this paper that YOLOv3 outperforms faster R-CNN in terms of sensitivity and processing time, despite being similar in terms of precision. [PERSON] et al. [9] developed an intelligent vehicle tracking system based on video surveillance. In order to track vehicles, this system integrates neural networks with image-based tracking, and You Only Look Once (YOLOv3). Different datasets were used to train this system. [PERSON] et al. [10] presented a comparison of different neural network architectures for object detection and recognition. Pedestrians and vehicles were the subjects of their study.