Model Radar Sensor Detections

This example shows how to model and simulate the output of an automotive Radar sensors for different driving scenarios. Generating synthetic radar detections is important for testing and validating tracking and sensor fusion algorithms in corner cases or when sensor hardware is unavailable. This example analyzes the differences between radar measurements and the vehicle ground truth position and velocity for a forward collision warning (FCW) scenario, a passing vehicle scenario, and a scenario with closely spaced targets. It also includes a comparison of signal-to-noise ratio (SNR) values between pedestrian and vehicle targets at various ranges.

In this example, you generate radar detections programmatically. You can also generate detections by using the Driving Scenario Designer app. For an example, see Create Driving Scenario Interactively and Generate Synthetic Sensor Data.

Introduction
Vehicles that contain advanced driver assistance system (ADAS) features or are designed to be fully autonomous typically rely on multiple types of sensors. These sensors include sonar, radar, lidar, and vision. A robust solution includes a sensor fusion algorithm to combine the strengths across the various types of sensors included in the system. For more information about sensor fusion of synthetic detections from a multisensor ADAS system, see Sensor Fusion Using Synthetic Radar and Vision Data.

When using synthetic detections for testing and validating tracking and sensor fusion algorithms, it is important to understand how the generated detections model the sensor’s unique performance characteristics. Each kind of automotive sensor provides a specific set of strengths and weaknesses which contribute to the fused solution. This example presents some important performance characteristics of automotive radars and shows how the radar performance is modeled by using synthetic detections.

Radar Sensor Model
This example uses drivingRadarDataGenerator to generate synthetic radar detections. drivingRadarDataGenerator models the following performance characteristics of automotive radar:

Strengths

Good range and range-rate accuracy over long detection ranges
Long detection range for vehicles
Weaknesses

Poor position and velocity accuracy along the cross-range dimension
Shorter detection range for pedestrians and other nonmetallic objects
Close range detection clusters pose a challenge to tracking algorithms
Inability to resolve closely spaced targets at long ranges

We specialize in manufacturing Radar sensors in China. we have the highest technology and stable quality control, welcome to inquiry it, please contact us by e-mail sales@radar-sensors.com for quotation requests, please provide quantity and any other specific requirements.