Track vehicles using measurements from a lidar sensor mounted on top of an ego vehicle. Lidar sensors report measurements as a point cloud. The example illustrates the workflow in MATLAB® for processing the point cloud and tracking the objects. For a Simulink® version of the example, refer to Track Vehicles Using Lidar Data in Simulink.The lidar data used in this example is recorded from a highway driving scenario. In this example, you use the recorded data to track vehicles with a joint probabilistic data association (JPDA) tracker and an interacting multiple model (IMM) approach.

Track highway vehicles around an ego vehicle. Vehicles are extended objects, whose dimensions span multiple sensor resolution cells. As a result, the sensors report multiple detections of these objects in a single scan. In this example, you will use different extended object tracking techniques to track highway vehicles and evaluate the results of their tracking performance.

Fuse tracks from two vehicles in order to provide a more comprehensive estimate of the environment that can be seen by each vehicle. The example demonstrates the use of a track-level fuser and the object track data format. In this example, you use the driving scenario and models from Automated Driving Toolbox™ and the tracking and track fusion models from Sensor Fusion and Tracking Toolbox™.

Track moving objects with multiple lidars using a grid-based tracker. A grid-based tracker enables early fusion of data from high-resolution sensors such as radars and lidars to create a global object list.

Detect, classify, and track vehicles by using lidar point cloud data captured by a lidar sensor mounted on an ego vehicle. The lidar data used in this example is recorded from a highway-driving scenario. In this example, the point cloud data is segmented to determine the class of objects using the PointSeg network. A joint probabilistic data association (JPDA) tracker with an interactive multiple model filter is used to track the detected vehicles.

Generate an object-level track list from measurements of a radar and a lidar sensor and further fuse them using a track-level fusion scheme. You process the radar measurements using an extended object tracker and the lidar measurements using a joint probabilistic data association (JPDA) tracker. You further fuse these tracks using a track-level fusion scheme. The schematics of the workflow is shown below.

Track vehicles using measurements from a lidar sensor mounted on top of an ego vehicle. Due to high resolution capabilities of the lidar sensor, each scan from the sensor contains a large number of points, commonly known as a point cloud. The example illustrates the workflow in Simulink for processing the point cloud and tracking the objects. The lidar data used in this example is recorded from a highway driving scenario. You use the recorded data to track vehicles with a joint probabilistic data association (JPDA) tracker and an interacting multiple model (IMM) approach. The example closely follows the Track Vehicles Using Lidar: From Point Cloud to Track List MATLAB® example.

Perform track-to-track fusion in Simulink® with Sensor Fusion and Tracking Toolbox™. In the context of autonomous driving, the example illustrates how to build a decentralized tracking architecture using a track fuser block. In the example, each vehicle perform tracking independently as well as fuse tracking information received from other vehicles. This example closely follows the Track-to-Track Fusion for Automotive Safety Applications MATLAB® example.