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Road accidents are one of the major causes of deaths in our country today. Every year, around 1.5 lakh people die on Indian roads, which means, on an average, 1130 accidents and 422 deaths every day or 47 accidents and 18 deaths occur every hour. One of the main reasons for accidents is frontal collisions due to distracted drivers. This issue can be tackled in a number of different ways. They all, however, fit into one of two categories-driver behaviour analytics, which includes using mobile, drowsiness, etc., or obstacle detection and collision avoidance. We decided to develop an obstacle detection and collision avoidance system that takes in camera input and alerts the driver with a buzzer when the vehicle becomes too close to the obstacle. These obstacles can include debris, fallen objects, animals, or even other vehicles. After the development of the system, we have performed real-time testing on roads to evaluate the performance metrics (Agrawal and Varade in Collision detection and avoidance system for vehicle, pp 476–477, 2017 [1]; Kovačić et al. in Computer vision systems in road vehicles: a review, 2013 [2]).

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Smart Collision Detection and Mitigation: Harnessing Machine Learning for Safety

  • 会议论文

  • 发表日期：

    2025年

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  • Sai Ananya Tungaturthi,
  • Nandiwada Prajna Gayathri,
  • Dhanwanth Rao Varala Balaji,
  • V. Krishna Mohan

摘要

Road accidents are one of the major causes of deaths in our country today. Every year, around 1.5 lakh people die on Indian roads, which means, on an average, 1130 accidents and 422 deaths every day or 47 accidents and 18 deaths occur every hour. One of the main reasons for accidents is frontal collisions due to distracted drivers. This issue can be tackled in a number of different ways. They all, however, fit into one of two categories-driver behaviour analytics, which includes using mobile, drowsiness, etc., or obstacle detection and collision avoidance. We decided to develop an obstacle detection and collision avoidance system that takes in camera input and alerts the driver with a buzzer when the vehicle becomes too close to the obstacle. These obstacles can include debris, fallen objects, animals, or even other vehicles. After the development of the system, we have performed real-time testing on roads to evaluate the performance metrics (Agrawal and Varade in Collision detection and avoidance system for vehicle, pp 476–477, 2017 [1]; Kovačić et al. in Computer vision systems in road vehicles: a review, 2013 [2]).