In present scenario, daily commuters are facing issues while driving which may result in road rage and eventually accidents. To overcome this problem, we propose a product which uses image processing algorithms to detect nearby vehicles and their relative speed to find out an optimum speed the driver can maintain.. This delivers a sense of safety to the commuters.
In order to overcome the problems as discussed, we develop a prototype to suggest the commuters an optimum speed based on the current traffic status and neighboring vehicle movements so as to prevent accidents.
- Python
- C
- OpenCV
- Yolo v3
In this phase, we are roughly detecting the distance between the vehicles and predicting their relative velocity observing their movements and changes in their spatial orientation. The image processing model has been built to detect the vehicles in the surrounding in realtime footage captured by the camera installed in the vehicle. The model observes the change in bounding box area that is covered by the vehicle and predicts the relative velocity based on the change in its dimension.
- Installation of an array of sensors like accelerometer and inertial sensors in order to study the driving pattern of the vehicle in realtime.
- Interconnecting all the nearby vehicles using blockchain and analysing all data from the vehicles to implement a safe traffic environment.
- Intimate the emergency contacts in case of an impact or accident in order to reduce the time taken by the victim to receive health.
