Track-to-Track Fusion for Automotive Safety Applications in Simulink
This example shows how to 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-To-Track Fuser block. In the example, each vehicle performs tracking independently as well as fuses tracking information received from other vehicles. This example closely follows the Track-to-Track Fusion for Automotive Safety Applications MATLAB® example.
Automotive safety applications largely rely on the situational awareness of the vehicle. A better situational awareness provides the basis to a successful decision-making for different situations. To achieve this, vehicles can benefit from intervehicle data fusion. This example illustrates the workflow in Simulink for fusing data from two vehicles to enhance situational awareness of the vehicle.
Setup and Overview of the Model
Prior to running this example, the
drivingScenario object was used to create the same scenario defined in Track-to-Track Fusion for Automotive Safety Applications. The roads and actors from this scenario were then saved to the scenario object file
Tracking and Fusion
In the Tracking and Fusion section of the model there are two subsystems that implement the target tracking and fusion capabilities of Vehicle 1 and Vehicle 2 in this scenario.
Vehicle 1 Subsystem
This subsystem includes the Scenario Reader (Automated Driving Toolbox) block that reads the actor pose data from the saved file. The block converts the actor poses from the world coordinates of the scenario into ego vehicle coordinates. The actor poses are streamed on a bus generated by the block. The actor poses are used by the Sensor Simulation subsystem, which generates radar and vision detections. These detections are then passed to the JPDA Tracker V1 block, which processes the detections to generate a list of tracks. The tracks are then passed into a Track Concatenation1 block, which concatenates these input tracks. The first input to the Track Concatenation1 block is the local tracks from the JPDA tracker and the second input is the tracks received from the track fuser of the other vehicle. To transform local tracks to central tracks, the track fuser needs the parameter information about the local tracks. However, this information is not available from the direct outputs of the JPDA tracker. Therefore, a helper Update Pose block is used to supply this information by reading the data from the v1Pose.mat file. The updated tracks are then broadcasted to T2TF Tracker V1 block as an input. Finally, the Track-To-Track Fuser T2TF Tracker V1 block fuse the local vehicle tracks with the tracks received from the track fuser of the other vehicle. After each update, the track fuser on each vehicle broadcasts its fused tracks to be fed into the update of the track fuser of the other vehicle in the next time stamp.
Vehicle 2 Subsystem
Vehicle 2 subsystem follows a similar setup as the Vehicle 1 subsystem.
The Visualization block is implemented using the MATLAB System block and is defined using the HelperTrackDisplay block. The block uses RunTimeObject parameters Out, Confirmed Tracks, Tracks and Confirmed Tracks of Detection Clustering, JPDA Tracker V1, Update Pose V1, T2TF Tracker V1 blocks respectively for vehicle 1 and RunTimeObject parameters Out, Confirmed Tracks, Tracks and Confirmed Tracks of Detection Clustering, JPDA Tracker V2, Update Pose V2, T2TF Tracker V2 blocks respectively for vehicle 2 to display their outputs. See Access Block Data During Simulation (Simulink) for further information on how to access block outputs during simulation.
After running the model, you can visualize the results. This animation shows the results for this simulation.
The visualization includes two panels. The left panel shows the detections, local tracks, and fused tracks that vehicle 1 generated during the simulation and represents the situational awareness of vehicle 1. The right panel shows the situational awareness of vehicle 2.
The recorded detections are represented by black circles. The local and fused tracks from vehicle 1 are represented by a square and a diamond, respectively. The local and fused tracks from vehicle 2 represented by a solid black square and a diamond. At the start of simulation, vehicle 1 detects vehicles parked on the right side of the street, and tracks associated with the parked vehicles are confirmed. Currently, vehicle 2 only detects vehicle 1 which is immediately in front of it. As the simulation continues, the confirmed tracks from vehicle 1 are broadcast to the fuser on vehicle 2. After fusing the tracks, vehicle 2 becomes aware of the objects prior to detecting these objects on its own. Similarly, vehicle 2 tracks are broadcast to vehicle 1. Vehicle 1 fuses these tracks and becomes aware of the objects prior to detecting them on its own.
In particular, you observe that the pedestrian standing between the blue and purple cars on the right side of the street is detected and tracked by vehicle 1. Vehicle 2 first becomes aware of the pedestrian by fusing the track from Vehicle 1 at around 0.8 seconds. It takes vehicle 2 roughly 3 seconds before it starts detecting the pedestrian using its own sensor. The ability to track a pedestrian based on inputs from vehicle 1 allows vehicle 2 to extend its situational awareness and to mitigate the risk of accident.
This example showed how to perform track-to-track fusion in Simulink. You learned how to perform tracking using a decentralized tracking architecture, where each vehicle is responsible for maintaining its own local tracks, fuse tracks from other vehicles, and communicate the tracks to the other vehicle. You also use a JPDA tracker block to generate the local tracks.