While driving a car, collision detection plays an important role in autonomous driving and for the driver. This project presents an integrative approach to speed and direction detection which aims to be as simple as possible to enable the real- time computation while being able to adapt to a variety of highway and urban scenarios on different conditions. In this work, we implement a signal-based forward collision warning system and an overtaking vehicle detection system. The forward collision warning system aims to detect other vehicles driving ahead of the host vehicle while overtaking so as to avoid the potential traffic accidents. On the other hand, the overtaking vehicle detection system is to detect other vehicles behind or on the side of the host vehicle so as to give an alert to the driver when the vehicle begins overtaking. Experimental results are presented with real scene. The performance evaluation has demonstrated the effectiveness of the proposed techniques.

Agent-based Forward Collision Warning and Collision Avoidance system using CARLA, Radar Sensor Fusion, and Explainable AI.

Git repo for my senior project implementing a retrofit automotive forward collision warning system.

This repository holds the source code for my thesis project entitled "Comparative Analysis of Perceptual Forward Collision Warning Algorithms"

This project creates a real-time ADAS prototype using Python, OpenCV, and a YOLOv5 AI model. It processes a video feed from a webcam or file to detect lane lines and other vehicles. The system then provides drivers with two critical safety alerts: Lane Departure Warning (LDW) and Forward Collision Warning (FCW).

This project creates a real-time ADAS prototype using Python, OpenCV, and a YOLOv5 AI model. It processes a video feed from a webcam or file to detect lane lines and other vehicles. The system then provides drivers with two critical safety alerts: Lane Departure Warning (LDW) and Forward Collision Warning (FCW).