追跡およびセンサー フュージョン

Track objects whose dimensions span multiple sensor resolution cells.

Track highway vehicles around an ego vehicle in Simulink. In this example, you use multiple extended object tracking techniques to track highway vehicles and evaluate their tracking performance. This example closely follows the Extended Object Tracking of Highway Vehicles with Radar and Camera (Sensor Fusion and Tracking Toolbox) MATLAB® example.

Track moving objects by using multiple lidar sensors and a grid-based tracker.

Track moving objects with multiple lidars using a grid-based tracker in Simulink. You use the Grid-Based Multi Object Tracker (Sensor Fusion and Tracking Toolbox) Simulink block to define the grid-based tracker. This Grid-based tracker uses dynamic occupancy grid map as an intermediate representation of the environment. This example closely follows the Grid-Based Tracking in Urban Environments Using Multiple Lidars (Sensor Fusion and Tracking Toolbox) MATLAB® example.

Fuse tracks from two vehicles to provide a more comprehensive estimate of the environment than seen by each vehicle.

Fuse tracks from multiple vehicles in the Simulink environment.

Track vehicles from lidar data using a joint probabilistic data association (JPDA) tracker and an interacting multiple model (IMM) approach.

Track vehicles from lidar data using a JPDA tracker and IMM approach in the Simulink environment.

Design and test a multiple-lane tracking algorithm.

Fuse tracks obtained by radar and lidar sensor measurements.

Fuse tracks obtained by radar and lidar sensor measurements in Simulink.

Dynamically replan the motion of an autonomous vehicle based on the estimate of the surrounding environment. You use a Frenet reference path and a joint probabilistic data association (JPDA) tracker to estimate and predict the motion of other vehicles on the highway. Compared to the Highway Trajectory Planning Using Frenet Reference Path (Navigation Toolbox) example, you use these estimated trajectories from the multi-object tracker in this example instead of ground truth for motion planning.

Simulate sensor fusion and tracking in a 3D simulation environment for automated driving applications.

Automate the testing of a forward vehicle sensor fusion component and generated code.

Deploy and test forward vehicle sensor fusion component of highway lane following system in real-time.

Design sensor fusion and tracking component to detect vehicles using multiple vision and radar sensors, and generate fused tracks for surround view analysis.

Construct an event-based sensor fusion and tracking model in Simulink.

Assess and mitigate the impact of multipath radar reflections when tracking highway vehicles using an extended object tracker.

Model and mitigate multipath radar reflections in a highway driving scenario in Simulink®. It closely follows the Highway Vehicle Tracking with Multipath Radar Reflections (Radar Toolbox) MATLAB® example.

Generate embedded code for a trackerJPDA (Sensor Fusion and Tracking Toolbox) (JPDA) tracker and verify it using processor-in-the-loop (PIL) simulations on a STM32 Nucleo board with 1 MB RAM and 2 MB flash memory. In this example, you configure the JPDA tracker to process detections from a camera and a radar sensor mounted in front of an ego vehicle in highway scenarios. For PIL simulations, you use simulated detections to verify the tracking and computational performance of the generated code.