Vehicle Safety Management System

Overtaking is a critical maneuver in driving that requires accurate information about the location and distance of other vehicles on the road. This study suggests a real-time overtaking assistance system that uses a combination of the You Only Look Once (YOLO) object detection algorithm and stereo vision techniques to accurately identify and locate vehicles in front of the driver, and estimate their distance. The system then signals the vehicles behind the driver using colored lights to inform them of the safe overtaking distance. The proposed system has been implemented using Stereo vision for distance analysis and You Only Look Once (YOLO) for object identification. The results demonstrate its effectiveness in providing vehicle type and the distance between the camera module and the vehicle accurately with an approximate error of 4.107%. Our system has the potential to reduce the risk of accidents and improve the safety of overtaking maneuvers, especially on busy highways and roads.

DiPD: Disruptive event Prediction Dataset from Twitter

Riots and protests, if gone out of control, can cause havoc in a country. We have seen examples of this, such as the BLM movement, climate strikes, CAA Movement, and many more, which caused disruption to a large extent. Our motive behind creating this dataset was to use it to develop machine learning systems that can give its users insight into the trending events going on and alert them about the events that could lead to disruption in the nation. If any event starts going out of control, it can be handled and mitigated by monitoring it before the matter escalates. This dataset collects tweets of past or ongoing events known to have caused disruption and labels these tweets as 1. We also collect tweets that are considered non-eventful and label them as 0 so that they can also be used to train a classification system. The dataset contains 94855 records of unique events and 168706 records of unique non-events, thus giving the total dataset 263561 records. We extract multiple features from the tweets, such as the user's follower count and the user's location, to understand the impact and reach of the tweets. This dataset might be useful in various event related machine learning problems such as event classification, event recognition, and so on.