# Sensor Fusion

> Multi-sensor fusion algorithms for perception in autonomous driving

- Skill: `a5c-ai/sensor-fusion-2` (Agent Skill)
- Install (CLI): `npx skillmds@latest add a5c-ai/sensor-fusion-2`
- Raw SKILL.md: https://api.skillmd.com/api/skills/a5c-ai/sensor-fusion-2/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: a5c-ai (https://skillmd.com/u/a5c-ai)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/a5c-ai/sensor-fusion-2

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# 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