Tracking for Autonomous Systems
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),
gmphdtracker) and evaluate tracking performance.
trackFuserto 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.
trackerGridRFSto track vehicles and targets using a grid-based occupancy evidence approach.
dynamicEvidentialGridMapto predict and plan vehicle motion in urban environments.