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Babysitter sensor-fusion

Multi-sensor fusion algorithms for perception in autonomous driving

install
source · Clone the upstream repo
git clone https://github.com/a5c-ai/babysitter
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/domains/science/automotive-engineering/skills/sensor-fusion" ~/.claude/skills/a5c-ai-babysitter-sensor-fusion && rm -rf "$T"
manifest: library/specializations/domains/science/automotive-engineering/skills/sensor-fusion/SKILL.md
tags
#sensor-fusion#autonomous-driving#perception#adas#object-tracking#fusion-algorithms
source content

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