Road Abnormality Detection Using Machine Learning

This research project focuses on developing a machine learning-based solution to detect humps and bumps in a road using Unity simulation. The solution involves creating a training dataset by collecting data on vibrations and suspensions of a car model driven on roads with known coordinates of bad and good conditions. The trained model is then used to classify unknown roads and recommend adjustments to car speed and suspension. Our aim is to improve the accuracy of road condition detection and enhance map services such as Google maps in conditions such as mud, snow, rubbish roads, off-roads, dirt roads, old worn-off roads, and under construction roads. The project will involve identifying and exploring different approaches and technologies, developing and assembling a prototype, testing it, identifying and fixing issues, and deploying the solution in a production environment. The solution in a production environment, ensuring that all issues have been fixed and the final product is ready for deployment. To achieve our goal, we will begin by selecting the best approach suitable for our problem and identifying the tools and technologies required to build the solution. Once we have identified the best approach, we will develop and refine it to solve the identified problem of detecting road humps and bumps. We will assemble the developed solution as a prototype and test it to ensure that it is fit for purpose. Any issues or problems found during testing will be identified and addressed to improve the solution. We will also look for additional features to add to the solution and explore possible environments for deploying the final product. Overall, this research project has the potential to revolutionize the way road conditions are detected and map services are enhanced. By providing a more accurate and reliable solution, it can help to reduce accidents and make driving safer and more efficient.