Computer Vision System for Autonomous Navigation
End-to-end computer vision framework for autonomous driving: object detection, lane detection, collision prediction, and sensor fusion. Live demos require connecting to a backend service.
Connect to your backend to run live demos
YOLO-based detection with configurable confidence thresholds and class filters. Requires backend.
Live DemoEdge-based and CNN segmentation approaches. Results shown when connected.
InteractiveTrajectory, time-to-collision, and risk assessment. Requires backend.
Virtual Lab3D visualization of camera/LiDAR/radar fusion. Live data when connected.
3DCNN-based sign classification and compliance checks. Requires backend.
DLDetection with optional pose estimation and movement prediction. Requires backend.
SafetyOverview of supported architectures
Real-time object detection models with multi-scale features and anchor-free heads.
DetectionSemantic segmentation network for robust lane boundary estimation.
SegmentationSequence models for multi-step motion forecasting with uncertainty.
ForecastingExtended Kalman and particle filters for multi-sensor state estimation.
FusionGuides and references
Shown when datasets and results are connected
Published Research & Preprints
This paper presents a comprehensive computer vision framework for autonomous vehicle navigation, integrating YOLOv8-based object detection, CNN-based lane segmentation, and Kalman filtering for multi-sensor fusion. Our system achieves real-time performance (>30 FPS) while maintaining high accuracy in object detection (mAP@0.5: 94.2%) and lane detection (97.8% success rate). We introduce a novel collision prediction algorithm that combines trajectory forecasting with uncertainty quantification, demonstrating 99.1% collision avoidance success in simulated urban environments. The framework processes camera, LiDAR, and radar data through a unified pipeline, addressing the critical challenge of sensor fusion in dynamic traffic scenarios.