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
tracker) and evaluate tracking performance.
trackFuser to fuse tracks from multiple
automotive tracking sources utilizing a track-to-track
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.
trackerGridRFS to track vehicles and targets
using a grid-based occupancy evidence approach.
dynamicEvidentialGridMap to predict and plan
vehicle motion in urban environments.