Problem

Autonomous vehicles have been gaining popularity in recent years, but one of the biggest challenges they face is recognizing and tracking other vehicles on the road. Traditional computer vision techniques have limitations in accurately identifying and tracking vehicles, especially in complex driving scenarios.

We faced this problem when developing an autonomous vehicle platform for a client. The platform needed to be able to accurately recognize and track other vehicles on the road in real-time, even in challenging conditions such as low light or bad weather.

Click here to see a video about how we solved this problem.

Solution

To solve this problem, we used Convolutional Neural Networks (CNN) - a type of deep learning algorithm that is particularly effective at image recognition tasks. We trained our CNN model using a large dataset of vehicle images captured from various angles and lighting conditions.

The trained model was then integrated into our autonomous vehicle platform, allowing it to accurately identify and track other vehicles on the road in real-time. We also implemented additional features such as object classification and distance estimation to further enhance the platform's capabilities.

Impact

Improved vehicle recognition accuracy by X%
Reduced false positives by X%

"Quote of how incredible of an impact this had."

Team

Team Member #1 - Machine Learning Engineer
- Developed and trained the CNN model
- Optimized the model for real-time performance on the platform
Team Member #2 - Software Engineer
- Integrated the CNN model into the autonomous vehicle platform
- Implemented additional features such as object classification and distance estimation

CNN Improves Autonomous Vehicle Tracking