Advanced AI-powered detection with real-time risk assessment to make autonomous driving safer for everyone.
Pedestrians in immediate proximity to the vehicle
Pedestrians within cautionary distance
Pedestrians at a safe distance from the vehicle
Our system processes video feeds with advanced neural networks to ensure immediate detection of pedestrians in any lighting condition.
Beyond simple bounding boxes, our system uses pixel-level segmentation to accurately determine pedestrian positions relative to the vehicle.
Automatically categorizes pedestrians into risk levels based on distance, movement trajectory, and environmental factors.
Advanced algorithms maintain high detection accuracy in rain, snow, fog, and low-light conditions where traditional systems fail.
All detection sessions are recorded with metadata for later review, allowing for continuous improvement of the system.
Export detection data in multiple formats for integration with other systems or for compliance reporting.
Our project focuses on enhancing pedestrian safety in autonomous systems using a robust AI-powered framework. Leveraging semantic segmentation and object detection, our model identifies pedestrians with high accuracy in real-time.
We utilize a pre-trained YOLOv8 segmentation model to classify each pixel and assess pedestrian risk levels based on proximity and movement. Individuals closer to the vehicle are marked as high risk, while those farther away are categorized as medium or low risk.
This real-time risk classification helps improve decision-making in autonomous systems, enabling safer navigation in dynamic environments like urban streets and intersections.