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レーダーおよびカメラによるトラッキング
R2026aこれらの例では、レーダーおよびカメラの検出を使用して拡張オブジェクトをトラックします。
注目の例
Highway Vehicle Tracking Using Multi-Sensor Data Fusion
Track vehicles on a highway with commonly used sensors such as radar, camera, and lidar. In this example, you configure and run a Joint Integrated Probabilistic Data Association (JIPDA) tracker to track vehicles using recorded data from a suburban highway driving scenario.
Extended Object Tracking of Highway Vehicles with Radar and Camera
Track highway vehicles around an ego vehicle as extended objects that span multiple sensor resolution cells.
Asynchronous Sensor Fusion and Tracking with Retrodiction
Construct an asynchronous sensor fusion and tracking model in Simulink®.
Processor-in-the-Loop Verification of JPDA Tracker for Automotive Applications
Generate embedded code for a JPDA tracker and verify it using processor-in-the-loop (PIL) simulations.
Highway Vehicle Tracking with Multipath Radar Reflections
The challenges associated with tracking vehicles on a highway in the presence of multipath radar reflections. It also shows a ghost filtering approach used with an extended object tracker to simultaneously filter ghost detections and track objects.
Extended Object Tracking of Highway Vehicles with Radar and Camera in Simulink
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 MATLAB® example.
Extended Target Tracking with Multipath Radar Reflections in Simulink
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.
Grid-Based Tracking in Urban Environments Using Multiple Radars
Track moving objects with multiple high-resolution radars using a grid-based tracker. A grid-based tracker enables early fusion of data from high-resolution sensors such as radars and lidars to estimate a dynamic occupancy grid map and a global object list. For grid-based tracking with lidar sensors refer to the Grid-Based Tracking in Urban Environments Using Multiple Lidars
