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Tracking for Autonomous Systems

Track extended objects and fuse tracks from multiple tracking sources

These examples present tracking applications for autonomous systems.

  • With lidar detections and a 3-D bounding box detector model, track autonomous vehicles using a JPDA (joint probabilistic data association) tracker and an IMM (interactive multiple model) filter.

  • With radar and vision detections, track autonomous vehicles using different trackers (multiObjectTracker (Automated Driving Toolbox), ggiwphd tracker, and gmphd tracker) and evaluate tracking performance.

  • Use trackFuser to fuse tracks from multiple automotive tracking sources utilizing a track-to-track fusion architecture.

  • Using radar and lidar detections, build a synthetic tracking system with multiple trackers and fuse tracks from extended object trackers and conventional pointer object trackers.

  • Use trackerGridRFS to track vehicles and targets using a grid-based occupancy evidence approach.

  • Use dynamicEvidentialGridMap to predict and plan vehicle motion in urban environments.