Sensor Fusion

Multi-sensor fusion algorithms for perception in autonomous driving

a5c-ai Updated 1.7k repo stars

File contents

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

a5c-ai/babysitter/tree/main/library/specializations/domains/science/automotive-engineering/skills/sensor-fusion commit a23a2990d0

Frequently asked questions

npx skillmds@latest add a5c-ai/sensor-fusion-2