Mike Dempsey Managing Director - Microsoft · –For example: Ibeo Scala B2, Velodyne VLP-16...
Transcript of Mike Dempsey Managing Director - Microsoft · –For example: Ibeo Scala B2, Velodyne VLP-16...
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Virtual development and testing
of autonomous vehicles
Mike Dempsey
Managing Director
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Who are Claytex?
• Model-based engineering analysis consultancy
– Innovators in CAE process
– Leading the way on zero-prototype development
– Specialists in high-fidelity real-time simulation
– Users of Dymola and Modelica since 1999
• Provider of software solutions for systems engineering
– Dymola distributors since 2003
– Dassault Systemes partner since 2008
– rFpro system integrator and distributor since 2009
• Modelica library and FMI tool developers
• Dassault Systemes Certified Education Partner
• Offices in the UK, USA and South Africa
• Major customers include Automotive OEM’s, suppliers and
Motorsport teams (Formula 1, NASCAR, Indycar, Formula E)
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Autonomous Vehicles Are Coming!
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How do we prove the AI is safe?
• Research has been carried out to
determine the amount of testing
required to prove autonomous
vehicles are safer than human
drivers
• Each software/hardware release
will have to be validated
• However, this is not physically
possible
Source: Rand Corporation
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Testing autonomous vehicles
Simulation
• Huge number of scenarios can be considered
• Full control of virtual environment: traffic, pedestrians, weather, etc.
Proving Grounds
• Recreate critical scenarios
• Limited control of the environment
• Robot controlled targets
• Pedestrian targets
• No control of weather and light
Field Tests
• Investigation of real driving situations
• No control of the environment
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What must our simulation tool do to support this?
• Provide accurate models of real world locations
• Support interaction with traffic, pedestrians, animals
• Have a flexible approach to defining test scenarios
– Weather, time of day, location, etc.
• Allow human controlled vehicles to interact in the environment
• Allow multiple CAV to operate in the same environment
• Run in real-time to support HiL integration of the AI processor
• Provide sensor models that replicate the real sensors
– radar, LiDAR, ultrasound, camera, GPS, etc.
• Support the integration of your vehicle physics model
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Real world locations
• 100km’s of real world locations are available
• Proving grounds: Idiada, Millbrook, Mcity, SMLL
• Race tracks: Nordschleife, full Formula 1 and NASCAR
calendars, partial Formula E calendar and many other
tracks worldwide
• Public roads including locations in:
– France, Germany, USA, Canada, Italy, China
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Traffic
• rFpro connects to wide range of traffic
modelling tools
– SUMO, IPG Traffic, PTV Vissim, Vires VTD,
Forum 8
• The traffic pack of vehicles covers road cars,
HGV and pedestrians
• Allows control of traffic lights, etc.
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Control weather conditions
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Control the lighting conditions
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Wet roads
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Vehicle Physics
• Wraps around your vehicle model
– Supports Dymola, CarMaker, CarSim,
SIMPACK, Dynaware, AVL_VSM, VI-
Grade, dSPACE_ASM, Simulink and C++
• Includes the rFpro TERRAIN_SERVER
– Provides high frequency road surface
information to the vehicle model
• Supports integration with HIL
– Off-the-shelf implementations for
Concurrent Realtime, dSPACE,
Speedgoat
• Claytex uses Dymola with multi-domain
vehicle models
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Vehicle Systems Modelling and Analysis
• Suite of Modelica libraries for Vehicle
Systems Modelling and Analysis
• Core platform enables performance,
fuel economy and energy analysis
– Drive cycle simulation
• Application specific extensions
provide detailed models across many
areas
– Engines, powertrain, vehicle
dynamics, driver-in-the-loop
• Used for modelling conventional,
hybrid and electric vehicles
• Real-time capable
• Integrates with rFpro
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Testing entire toolchain
rFpro
Sensor model
Algorithms
Validate vs Ground truth
Vehicle physics
Control systems
HUMAN TEST
DRIVER
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Sensor feeds
• Apply effects to replicate what the
sensors see
– Lens distortion effects
– Dirt
• Normal and Fisheye camera views
• Depth map and object lists
• Open API to access all this data
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Sensor modelling
• Building a library of models that represent real
sensors
– For example: Ibeo Scala B2, Velodyne VLP-16 “Puck”,
Delphi ESR 2.5, Continental ARS430
– Produce the same output message format
– Capture the dynamics of the sensor e.g. rotation of
sensor with time
• Developing control panel to manage and configure
scenarios, sensor setup, etc.
• Active R&D projects to increase fidelity of sensor
models under 2 Innovate UK projects
– Sensor models will take into account weather effects and
other significant sources of noise
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dRISK – AV risk assessment
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Summary
• Simulation will be an essential part of the
development and testing of autonomous
vehicles
• Your tools need to provide:
– detailed models of the environment
– support for HIL to include the real controller
– representative sensor models
– vehicle physics model
• rFpro offers all these capabilities