SL-1-5 Reflection Based FMCW Lidar Detection Model

This is a parameterizable reflection based FMCW lidar detection model that uses the Open Simulation Interface (OSI) standard. The model receives lidar reflection data calculated in a simulation tool beforehand e.g. with ray tracing and outputs lidar detection data. In contrast to SL 1-2 Reflection Based Lidar Object Model, an additional radial velocity component is processed for each detection in order to model the FMCW principle.

The visualization of the model output was created with the Simspector developed by Persival GmbH.

Modeling Approach

Simulation Architecture

The model in this repository is the signal processing model of a FMCW lidar sensor simulation. The simulation architecture ist depicted in the figure below an consists of the enviroment simulation, the signal propagation model and the signal processing model. Open Simulation Interface (OSI) is used for communication between the components. For the environmental simulation and the signal propagation model, any model can be used if the corresponding OSI interfaces are supported as specified below. The signal processing model presented here is based on the sl-1-0-sensor-model-repository-template.

Architecture of the sensor simulation based on [1].

Signal Processing [1]

The input for the model are lidar reflections (sensor_view.lidar_sensor_view.reflection), which can be calculated using a ray tracer, for example. Each reflection holds the information a, b and c. In addition, a predefined beam pattern determines from which direction, depending on the azimuth and elevation angle, the reflections originate. For the modeling approach, the beam pattern is divided into discrete areas, called pixels, from which a lidar detection can result. The beam pattern is assumed to consist of horizontal scan lines that are stacked on top of each other. Accordingly, the size of the individual pixels is determined by the vertical beam divergence of the laser and the azimuth resolution of the sensor. The pixels are super-sampled by multiple reflections as described in [2]. For each pixel, an up-ramp and a down-ramp Fast Fourier Transform (FFT) is calculated according to the FMCW principle to determine the distance and velocity of the lidar detection. The FFTs are approximated using the Fourier tracing method [3], which was also used in the sl-1-1-reflection-based-radar-object-model. A simple maximum function is used to detect a peak in the up and down-ramp spectrum, from which the distance and velocity of the detection are derived. This procedure is repeated for each pixel, resulting in a point cloud as output.

Parameterization

Beam Pattern

Parameter	Description
no_of_layers	Number of horizontal scan lines
beam_center_elevation_deg	Elevation angle for each horizontal scan line in deg
min_azimuth_rad	Minimum azimuth angle in rad
max_azimuth_rad	Maximum azimuth angle in rad
azimuth_resolution_rad	Azimuth resolution of beam pattern in rad
beam_divergence.vertical_angle	Vertical beam divergence in rad
beam_divergence.horizontal_angle	Horizontal beam divergence in rad
emitted_power_per_beam_mW	Emitted power per beam in mW

Ray Pattern

Parameter	Description
rays_per_beam_horizontal	Number of rays per beam in horizontal direction
rays_per_beam_vertical	Number of rays per beam in vertical direction
distance_noise_std	Standard deviation of distance noise applied to each ray in m
rays_per_beam_vertical	Standard deviation of velocity noise applied to each ray in m/s

Lidar Parameters

Parameter	Description
wavelength_m	Wavelength of emitter in m
min_range	Minimum range in m
max_range	Maximum range in m
ramp_duration	Duration of up/down ramp in triangular frequency modulation pattern in s

Fourier Tracing Parameters

Parameter	Description
window_data_per_bin	Data points per bin in the look-up table
bin_affect_range	Number of bins that are effected by the windowing function
window_function	Window function lookup-table
fft_size	Size of FFT

Note: Due to issues with OpenMCX, the ray configuration has to be set within the ray tracing model as well.

Interface

Input: Required Fields in OSI3::SensorView

OSI Message	Required / Optional
sensor_view.lidar_sensor_view.view_configuration.mounting_position	required
sensor_view.lidar_sensor_view.reflection.signal_strength	required
sensor_view.lidar_sensor_view.reflection.time_of_flight	required
sensor_view.lidar_sensor_view.reflection.doppler_shift	required

Output: Fields in OSI3::SensorData Filled by the Sensor Model

OSI Message	Required / Optional
sensor_data.timestamp	required
sensor_data.feature_data.lidar_sensor.header.mounting_position	required
sensor_data.feature_data.lidar_sensor.header.sensor_id	required
sensor_data.feature_data.lidar_sensor.detection.position	required
sensor_data.feature_data.lidar_sensor.detection.intensity	required
sensor_data.feature_data.lidar_sensor.detection.radial_velocity	required

Build Instructions

Build Model in Ubuntu 18.04 / 20.04

1. Clone this repository with submodules:

   git clone https://github.com/openMSL/sl-1-5-reflection-based-fmcw-lidar-detection-model.git --recurse-submodules

2. Build the model by executing in the extracted project root directory:

   mkdir cmake-build
   cd cmake-build
   # If FMU_INSTALL_DIR is not set, CMAKE_BINARY_DIR is used
   cmake -DCMAKE_BUILD_TYPE=Release -DFMU_INSTALL_DIR:PATH=/tmp ..
   make

3. Take FMU from FMU_INSTALL_DIR

Credits

K. Hofrichter, C. Linnhoff, L. Elster, S. Peters, “FMCW Lidar Simulation with Ray Tracing and Standardized Interfaces,” 2024 Stuttgart International Symposium, Stuttgart, Germany, 02-03 July 2024

This work received funding from the research project "Synthetic FMCW-Lidar" which is financed with funds of LOEWE - Landes-Offensive zur Entwicklung Wissenschaftlich-ökonomischer Exzellenz, Förderlinie 3: KMU-Verbundvorhaben (State Offensive for the Development of Scientific and Economic Excellence).

Synthetic FMCW-Lidar	LOEWE-Förderlinie 3	Hessisches Ministerium für Wissenschaft und Kunst

Thanks to all contributors of the following libraries:

• Open Simulation Interface, a generic interface based on protocol buffers for the environmental perception of automated driving functions in virtual scenarios
• FMI Version 2.0: FMI for Model Exchange and Co-Simulation

References

[1] K. Hofrichter, C. Linnhoff, L. Elster, S. Peters, “FMCW Lidar Simulation with Ray Tracing and Standardized Interfaces,” 2024 Stuttgart International Symposium, Stuttgart, Germany, 02-03 July 2024.

[2] P. Rosenberger, M. F. Holder, N. Cianciaruso, P. Aust, J. F. Tamm-Morschel, C. Linnhoff, and H. Winner, “Sequential lidar sensor system simulation: A modular approach for simulation-based safety validation of automated driving,” Automotive and Engine Technology, vol. 5, no. 3-4, pp. 187–197, Dec. 2020.

[3] M. F. Holder, C. Linnhoff, P. Rosenberger, and H. Winner, “The Fourier Tracing Approach for Modeling Automotive Radar Sensors,” 20th International Radar Symposium (IRS), Ulm, Germany, 26-28 June 2019.