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It is a well-known fact that perception of road (lane) plays a vital role in assisting drivers by using software that can convey them the possible risks in their surroundings. Lane lines are a significant factor of signaling traffic flow. The strategy of this work is to prevent car accidents on highways mainly due to rash driving that consists of sudden lane departure or not keeping up with the lane in case of curved roads. Hence, we propose a method to reduce these crashes by mounting a calibrated camera on the vehicle which will continuously keep track of the view in front of vehicle and deliver certain relevant warnings to the driver in case of any rash driving or fatal situations. Also, it can be helpful for self-driving cars by providing a wide range of possibilities to learn by software and move vehicle on its own in the lane. We employ image processing techniques to detect and solve some of the most prevalent roadway concerns. The problem statement is broken down into basic building blocks to better understand the comprehensive list of potential techniques within this framework. It comes with a decision model that will provide the driver the required cautions.

The rapid advancements in technology in recent years have led to a massive increase in the exchange of data (images, videos, audio, etc.) between portable devices. This has invoked the necessity for building algorithms which consume low power with no compromise in the performance. In this paper, the above captioned issue is taken into account and accordingly an image compression technique using Repetitive Iteration CORDIC (RICO) architecture has been proposed. The proposed method is power efficient as it uses RICO for Discrete Cosine Transform (DCT) coefficient generation, and performs equally well when compared to Joint Photographic Experts Group (JPEG) standard. Results have been obtained via Matrix Laboratory (MATLAB) and they show that the proposed technique performs equally well and consumes less power in comparison with the other techniques.