iCar

India has the second highest number of deaths due to road accidents in the world, and 72% of these accidents can be prevented as they are the drivers’ fault. To solve this issue, along with a 3 member team, I collaboratively built an inexpensive and intelligent driver assistance system in my undergraduate capstone project. iCar was my undergraduate capstone project and it was built to assist drivers in various aspects of driving through lane detection, pedestrian & car detection, driver drowsiness detection, and robust rear view parking assistance.

Project specifications

Role:             Developed the lane detection module, Led the team in Quest Ingenium (a national level project competition)
Tools:           Visual studio, Processing IDE
Languages:  OpenCV, Python

To address this issue, we developed an intelligent and interactive driver assistance system using Python and OpenCV. After researching using multiple sources, we concluded that following are the main faults of drivers:

  1. Lane deviation due to drowsiness or distraction
  2. Unexpected car or pedestrian interference in the driver’s path
  3. Blindspots while reversing the car

Brainstorming:

On further exploration, we figured that this we can help drivers using 4 modules.

  1. Pedestrian and Car detection
  2. Lane deviation detection
  3. Driver drowsiness detection
  4. Rear view blindspot assistance

We divided these modules amongst ourselves and I was responsible for the lane extraction and deviation module.



 

Results

This project was selected as one of the “Top 10 projects in India” by Quest Ingenium 2013. It won the “Best Project – Quests Employees Choice” award in the same event. The project was also funded by Karnataka State Council for Science & Technology. We published our research in the Science, Technology, and Arts Research Journal.

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