PilotD offers a variety of models for testing, calibration and high-fidelity simulation of environment perception sensors for automated driving systems, in order to let our customers test sensors, find out specific sensor performance and take sensor performance into account through simulation-based validation when automated driving systems are developed.
Test & Calibration: In test and calibration, PilotD has a comprehensive test & calibration system and process for environmental perception sensors. Through the test process, PilotD provides our customers with various technical indicators for different sensor types, as well as the evaluation of the performance, advantages and disadvantages of sensor characteristics in various essential specific driving situations. Especially for active sensors (millimeter wave radar/lidar), PilotD can even provide technical support and services in environment and target measurement and reproduction (e.g. RCS) through this process. Simulation and Modeling: In simulation and modeling, this type of model is divided into two categories: active environment perception sensor models (for example, millimeter-wave radar models, lidar models, or ultrasonic sensor models) and passive environment perception sensor models (for example, camera models, or far infrared sensor model). In addition, through PilotD's Simulation-based Validation Environment for Automated Driving Systems - GaiA, and its flexible internal modular structure, the integration of the above described two types of models will also be possible. The integrated model can even be used to simulate some of the new compound sensors that are still in development (e.g. 3D-TOF sensors, etc.) Physical Simulation of Passive Environment Perception Sensors (Camera/Far Infrared Sensors): 1.For passive environment perception sensors, image processing-based recognition algorithms work primarily through signal processing of images taken by the camera.
2.The recognition process is reproducible for signal processing, such as the A/D converter used by the camera, the nonlinear sensitivity of the sensor, the dynamic response characteristics, the filter mask etc.
3.Physically, the effects of external conditions (e.g. lighting conditions, weather, etc.) on the world detected by passive environmental perception sensors are also simulated. Physical Simulation of Active Environment Perception Sensors (Camera/Far Infrared Sensors): 1.Compared to the real world visible to humans, the world seen by active environment perception sensors is completely different.
2.In the simulation validation, the simulated vehicle needs to be equipped with high-fidelity virtual environment- perception sensor models to make the validation meaningful.
3.We simulate detailed physical phenomena such as multipath reflection of electromagnetic waves, or dynamic sensor performance such as detection dropout rate, target resolution, measurement inaccuracy, and “ghost” objects to achieve the High fidelity required by sensor models.
4.As sensor outputs, in addition to the standard object list, the user can even obtain the field strength distribution of the electromagnetic wave received by the sensor, which is also well known as the raw measurement data. This type of data will enable an effective combination of accurate sensor-based validation and efficient system development. References 1)Cao, Peng; Wachenfeld, Walther; Winner, Hermann: Perception Sensor Modeling for Virtual Validation of Automated Driving, in: it - Information Technology (4), Issues 57, 2015
2)Cao, Peng: Modeling Active Perception Sensors for Real-Time Virtual Validation of Automated Driving Systems. Technische Universität, Darmstadt,[Dissertation], (2018)