pre_lanechange Computer Vision Project

Here are a few use cases for this project:

1. Advanced Driver-Assistance Systems (ADAS): The "pre_lanechange" model can be integrated into ADAS to help in identifying when lane changes are imminent, enhancing the safety features of vehicles by providing real-time warnings to the driver or even taking automated corrective actions.

2. Autonomous Vehicles: This model can play a crucial part in the decision-making process of autonomously driven vehicles, understanding when lane changes are necessary to maintain traffic flow or avoid obstacles.

3. Traffic Control and Management Systems: The model can be used in traffic control centers to monitor and manage traffic flow efficiently by analyzing the frequency and patterns of lane changes, thus taking necessary actions to prevent traffic congestion.

4. Driver Behavior Analysis: By using this model, insurance companies or fleet management services can monitor the driving patterns of individual drivers, detecting frequent or unsafe lane changes which could indicate risky driving behaviors.

5. Road Safety Research: Traffic researchers and road safety experts can utilize the model to gain insights into the impacts of lane changes on overall road safety, contributing to the development of safer road infrastructure designs or identifying targeted driver education needs.