Sensor Fusion Skill

Purpose

Enable multi-sensor fusion algorithm development for autonomous driving perception including object detection, tracking, and environmental modeling.

Capabilities

• Camera, radar, lidar data preprocessing
• Object detection fusion algorithms
• Tracking filter implementation (Kalman, EKF, UKF)
• Association algorithms (Hungarian, GNN, JPDA)
• Occupancy grid fusion
• Confidence estimation and sensor weighting
• Time synchronization handling
• Ground truth comparison and metrics

Usage Guidelines

• Preprocess sensor data for consistent coordinate frames
• Select appropriate tracking filters based on object dynamics
• Implement robust association for multi-target scenarios
• Fuse sensor confidence for reliable perception
• Handle time delays and synchronization issues
• Validate fusion against ground truth data

Dependencies

• ROS/ROS2
• TensorFlow
• PyTorch
• NVIDIA DriveWorks

Process Integration

• ADA-001: Perception System Development
• ADA-002: Path Planning and Motion Control
• ADA-003: ADAS Feature Development
• ADA-004: Simulation and Virtual Validation