Crash Recovery and Accident Prediction Using a IoT Based Blackbox System

Vehicle collisions are one of the most dangerous problems in the world. According to the World Health Organization, transportation-related accidents kill over one million people each year. Despite the fact that various vehicle manufacturers have discovered a way to improve vehicle safety, mishap rates continue to be high. Death rates are high as a result of clinical guidelines being delayed, resulting in monetary and social costs for those involved. When vehicles are involved in critical situations, many people are killed. Perhaps a few people could have been saved at that time, but it may not have been possible due to a lack of information, time, and location. This study proposes an optimal solution to this disadvantage. In this study, a Blackbox setup is used to continuously record and save vehicle acceleration, speed, area, and interference values, as well as the vehicle's operating history. The vehicle's driving condition and accident details can be checked using this Blackbox. To collect samples from the sensors and convert them into an advanced incentive to care for in the ESP (Espressif) microcontroller, an analogue to digital converter (ADC) is used. The primary goal of this arrangement is to gradually reduce mishap levels all over the world, and if mishaps do occur, to recover quickly by providing immediate assistance to those who have been injured.