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© TESIS DYNAware GmbH, www.tesis-dynaware.com 1
GPU-supported real-time sensor modelling
for ADAS development in Simulink
F. Chucholowski, C. Gnandt, C. Hepperle
Stuttgart
2
ADAS and AD
Challenge
Uncounted numbers of different traffic situations
Rising complexity of the systems
Rising complexity of road test setup
Solution
Virtual driving tests MiL / SiL / HiL
as a complement of real road tests
Benefits
Reproducibility
Safety
Frontloading
3
Virtual test drives for ADAS and AD
Situation
Object detection and classification
Information from sensor data fusion
Realistic enough simulation especially
regarding environmental model
Real-time capability
Requirements
Complete complex and configurable virtual world
Closed-loop of
virtual world
vehicle under test
controller functions
4
Sensors
Sensors are crucial for environment perception
of ADAS and AD algorithms
Virtual world must be recognized by all sensor types
For different applications different sensor modelling approaches are
suitable (e.g. stochastic, deterministic, ideal, measurement based)
Consider real-time capability for sensor modelling
Environment Sensors
electromagnetic optic acoustic
Car-2-X Radar Video Lidar Laser Infrared Ultrasonic
5
Main Idea - DYNAanimation
Use solutions from computer gaming industry
Use gaming graphics engine for visualization of a virtual 3D world
Use high-performance GPU computation for sensor modeling and other
suitable calculations
Use open gaming development environment as an asset for specific
application customization
Connect virtual world to overall simulation system
(e.g. MATLAB / Simulink)
Solution: Unity
Established within gaming industry
State-of-the-art computer graphics
Complete SDK + unity ecosystem, open system
Support of standard interfaces and technologies
6
Typical elements of the virtual 3D world
TerrainRoad
Weather Conditions
LightTraffic Signs
Traffic members
Buildings
VuT
Controller Information
7
Intelligent Objects
Powerful possibility to add content
Objects with own control / physics
Full scripting access
Full configuration
GPU-based calculation
8
Ambient conditions
Light
Fog
Rain
Snow
9
Display Objects
Powerful possibility to add content
Information display
Custom product visualization
10
Sensor modelling with unity
Full Unity support
Full scripting access, e.g.
Custom processing
11
Camera based sensors
Adjustable field of view (sensor size and focal length)
Adjustable resolution
Pixel shader to modifiy image
HDR RGB images
Color filter
Lens distortion
12
Time-of-Flight based sensors
LIDAR, RADAR
Configurable source (transmitter)
Propagation characteristics via light cookies
Damping
Reflection behaviour via materials
Intensity information
Depth information inherently available
Occlusion taken into account
Object IDs
13
Simulink s-functions
Bidirectional
Distributed computing and visualization
Real-time capability
Simulink Connection
Simulation Control, Motion Signals, Sensor Control
Time Stamp, Sensor Signals, Objects Interaction
Controller Development
Plant model
Animation of virtual world
Sensor simulation
14
LKAS with image processing (1)
Motivation
Lane Keeping Assist System (LKAS) test bench
with control unit and camera
Limitations regarding
e.g. optical effects
Solution
Closed loop SiL
controller tests
with direct image processing
15
LKAS with image processing (2)
TMW LKAS demo
DYNAanimation
Camera Sensor
Transmit Image to Simulink
Simulink
DYNA4 vehicle dynamics
Camera image receiver
Lane detection algorithm
Controller demands fed back
to vehicle dynamics model
Varying weather conditions
with and without rain
16
Parallel Parking with Ultrasonic Sensors (1)
Application: Parking Assist
Control algorithm and plant model
simulated in Simulink
Ultrasonic sensors simulated in 3D
animation
0.2 0.4 0.6
0.8 1
30
210
60
90270
300
150
330
180
0
horizontal
vertikal
horizontal
vertical
normalized
intensity
Sensor
48 kHz sound, 20 Hz sampling
Field of view from 0.2 to 2 m,
width +/- 60°, height +/- 30°
Dynamic membrane stimulus
and attenuation
0.25 m over ground
17
Parallel Parking with Ultrasonic Sensors (2)
Light sourceIntensity
image capture
Depthimage capture
Intensity-depth-
histogram
18
X [m]
Y [m]
Normalized
measured
intensity
Contour
of parked
vehicle
Curb stone
Simulation accuracy (Front protection scenario)
19
Conclusion
Simulink very suitable for vehicle dynamics and controller algorithms
Complex 3D environment set up in 3D animation
Source for user’s display
Powerful and flexible sensor modelling
Effects, such as reflection intensity or occlusion, handled automatically
Perfect complement to Simulink