Road Lane Departure, Turn and Collision Warning System [Ref: TT10022]

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Advance your career in AI-assisted driving with the vehicle lane departure, turn curvature, and collision warning system project.  Apply deep neural nets and computer vision to detect lane departures, turn curvatures, and collision early warning systems designed to provide drivers with early information to enable active safety on the road

Problem Scenario :

Safe driving requires actively monitoring the driving trajectory to avoid unintended and unplanned lane departures. It also requires active tracking of the distance to and from other vehicles, and the curvature of roads to keep a safe distance and a safe driving velocity. Equipping vehicle operators with artificial intelligence models that can handle these critical tasks has become important to assure safe driving on all roads

Aim :

This project aims to strengthen mastery in candidates to utilize the power of deep learning, Convolutional Neural Networks (CNN ), computer vision, and related libraries to build composite and integrated models that show great skill at the triple task of lane departure detection, road curvature Vs velocity alerting and dynamic object distance tracking. This is a critical component of self-driving and Ai-assisted cars

Participants in this project will work in core teams to collect data and learn to implement data augmentation to improve model performance under adverse environmental conditions

What you will learn :

Computer Vision Project Organization

Data Requirements Design and data collection

Image data pre-processing and data augmentation techniques

Convolutional Neural Networks Design and Implementation

Model inferencing architecture

Requirements

This project features JIT-based training in Python and image pre-processing and as such, candidates are not required to possess any skills in Python. It is recommended to complete the Road Signs Object Detection project before taking this project