BE ST PRACT IC E SEngineer Perception,Prediction and Planning intoADAS

Hardware, software and humans need to be in the loop
to advance safe autonomous driving technologies.
By Gilles Gallee, Autonomous Vehicle Business Developer, Ansys, La Farlede, France
Advanced driver assistance systems (ADAS) — such as forward collision warning (FCW),automatic emergency braking (AEB), lane departure warning (LDW), lane keeping
assistance (LKA) and blind spot monitoring (BSM) systems — are estimated to have thepotential to prevent more than a third of all passenger-vehicle crashes.1 According to aAAA Foundation for Traffic Safety report2, such a reduction would in turn prevent 37% ofinjuries and 29% of deaths in crashes that involve passenger vehicles. To fulfill the potentialof ADAS, as well as the even greater potential safety and convenience benefits associatedwith fully autonomous driving, simulation is needed to ensure that vehicles can perceive
the world around them, predict what might happen next and plan accordingly.

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There are a lot of literal and figurative miles between the current state of ADAS
and Level 5 fully autonomous vehicles. Simulation is critical to getting there.
An oft-cited report from Rand Corp. makes the case that autonomous vehicles
would have to be driven hundreds of millions of miles and sometimes
hundreds of billions of miles to demonstrate their reliability in terms of fatalities andinjuries. For example, according to Rand3, to prove that autonomous vehicles would get
into fewer serious crashes than human drivers would require a fleet of 100 autonomous
cars traveling at 25 mph non-stop for 125 million miles — the equivalent of six years ofdriving. To provide the same evidence for fatal crashes, that same fleet would have totravel 8.8 billion miles, which would take about 400 years.
Currently, most ADAS functions fall in the Level 1 or 2 range. Even achieving Level 3
autonomy, in which a vehicle can take full control when certain operating conditions aremet, is a challenging technological leap. It requires a combination of physical testing andsimulation that includes hardware, software and humans in the loop. Each aspect of theautonomous vehicle technology stack is critical and requires people with different skills
and knowledge to be involved.

LEVELS OF AUTONOMY

1

DRIVER
ONLY

Driver handles
all functions,steering,braking, lanemonitoring, etc.

2

ASSISTEDDRIVING

Vehiclehandles somefunctions, suchas emergency
braking.

3

PARTIALLYAUTOMATED

Vehicle handles
at least twofunctions,such as cruise
control and ​lane-centering.

4

HIGHLYAUTOMATED

Vehicle handles
all functions, but
driver is required
to be able totake control.

5

FULLY
AUTOMATED

Vehicle handles
all functionsautomatically.
No driverneeded.

TODAY
SAE International, the Society of Automotive Engineers, first published J3016 autonomy
level guidelines in 2014. They have since been adopted by both the U.S. Department ofTransportation and the United Nations.

SOLVE THE PERCEPTION AND PLANNING PROBLEMS WITH SENSORS AND AI
Sensors are the eyes and ears of ADAS. Like our own senses, weather and complicateddriving conditions can confuse and overwhelm them. In the automotive industry,
suppliers have been challenged to develop sensors and sensing systems that function
at a higher level so that they not only perform well on sunny days in light freeway traffic,
but in blizzards, on busy city streets and under a multitude of “edge cases.” Edge cases
encompass those unusual scenarios that don’t happen often, but often lead to accidents— a dog chasing the car in front of you, construction workers rerouting traffic, or a flash
flood making a roadway impassable are just a few of many edge cases.
Ansys SCADE Vision powered by Hologram helps to identify the edge cases to pinpoint
the weakness of AI. Armed with edge case information, SCADE Vision can then trigger
more AI training actions and new testing scenario conditions. See page 15 for more
information about Ansys SCADE Vision.
Software developers are interested
Fast Tracking ADAS Autonomous Vehicle
in generating synthetic data fromDevelopment with Simulation webinar
simulation to more quickly train AI inAnsys.com/fasttrack
various operation design domains (ODD),© 2020 ANSYS, INC.

ANSYS ADVANTAGE I 5

BE ST PRACT IC E S

the term used to describe subsets of driving conditions with particular environmental,
geographical, time-of-day, traffic and/or roadway characteristics. Defining andidentifying ODDs are challenges for developers because they affect testing, complianceand real-world Level 3 autonomous driving. For a car to take over from a human driverunder certain conditions, the sensors must perceive those conditions and the softwaremust interpret those perceptions to determine whether ODD requirements have been
met. Simulation helps developers explore those ODD edge cases.
Original equipment manufacturers (OEMs) rely on their supplier tiers to provide
sensor sets. However, OEMs are ultimately responsible for the safety of the cars they
produce, so they want to be sure suppliers have fully vetted those technologies. Suppliers
are using simulation at the component and packaging levels to better understand thestrengths and weaknesses of various sensing technologies, such as Ansys SPEOS for lidar
and cameras and Ansys HFSS for radar (see pages 20 and 24). The goals are to improve
individual sensing technologies and ensure the various technologies can be used together
to help create a robust sensor array that can handle whatever edge cases come up.
The Ansys VRXPERIENCE Driving Simulator powered by SCANeR forms the basis of
an ADAS development cycle. It provides ADAS development teams with the capability
to recreate driving scenarios and enables testing against a variety of objectives andperformance requirements. By replicating roads generated from high-definition maps
and asset libraries, traffic situations, weather conditions, vehicle dynamics and more,
ADAS development teams can validate sensor and AI modules, sensor systems and vehicle
models, as well as human–machine interfaces (HMIs).
SIMULATE ADAS FUNCTIONSADAS functions are driven by software development. Custom vehicle models can beconnected to Ansys VRXPERIENCE through FMI, C/C++, Ansys Twin Builder or MathWorks
Simulink. Engineers can put vehicles in an environment with certain conditions —for example, on a highway arriving at a traffic jam at a certain speed — and quickly
modify them for the scenarios and validation they’d like to perform. Based on that, they
can simulate the scenario with different levels and types of sensors to assess sensor
perception, sensor fusion and systems operations.
VRXPERIENCE can speed edge case exploration and sensor simulation. Take, forexample, headlamp development. There are a lot of missed detection edge cases at night,
so Ansys VRXPERIENCE has specific modules to simulate the physics of light. Intelligent
lighting to automate when highbeams should turn on and off or automatically adjust tominimize glare may seem like simple conveniences, but lighting is an important pieceof ADAS because the car’s camera sensors react to it. Cameras that identify signs, road

AUTOMATED PREVENTIONADAS Systems

Crashes

Injuries

DeathsForward Collision
Warning/AutomaticEmergency Braking

1,994,000

884,000

4,738

Lane Departure Warning/
Lane Keeping Assist

519,000

187,000

4,654

Blind Spot Warning

318,000

89,000274

2,748,000

1,128,000

9,496

Total Potentially Preventable
by All Systems

AAA Foundation research evaluated the potential that popular advanced driver assistancetechnologies have in helping to reduce or prevent crashes. The findings, which used U.S.
data, show that if installed on all vehicles, ADAS technologies can potentially prevent morethan 2.7 million crashes, 1.1 million injuries and nearly 9,500 deaths each year.
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lanes and oncoming vehicles are sensitive to headlamp design changes, for example.
VRXPERIENCE reduces the time and cost of development by enabling a repeatableprocess for modified sensor inputs, such as lighting changes.
Another example is an emergency braking function that is part of an ADAS. To develop
it, the function is first described as a model, often in MathWorks Simulink or Ansys SCADE
Suite. It is tested to meet objectives and then designed as a more detailed model. The
coding of the emergency braking function can then be tested vs. scenarios with softwarein-the-loop and hardware-in-the-loop.

The Ansys VRXPERIENCE Driving Simulator powered by SCANeR is the basis for ADAS andthe continuum all along the ADAS development cycle.

With Ansys VRXPERIENCE Driving Simulator powered by SCANeR, customers havea seamless process to test the model, connect it with SCANeR and then keep thesame vehicle test environment to connect software and hardware as they simulatedifferent ODD edge cases. The streamlined workflow saves time and makes it easier for
geographically dispersed teams and experts from different disciplines to collaborate.
PLANNING FOR HUMANS IN THE LOOP
In addition to predicting what other motorists, cyclists and pedestrians will do, ADAS also
needs to account for how people will behave inside their cars. According to Universityof Iowa research4, people’s behavior can change based on ADAS features. About 25% of
the drivers surveyed who used blind spot monitoring or rear cross traffic alert systems
reported feeling comfortable relying solely on the systems and not performing visualchecks or looking over their shoulder for oncoming traffic or pedestrians. About 25% ofvehicle owners using forward collision warning or lane departure warning systems also
reported feeling comfortable engaging in other tasks while driving.5
Early adopters of ADAS technology proved that false positives or annoying alert sounds
could cause them to ignore or disable safety features. As ADAS continues to take on amore prominent safety role, human–machine interaction becomes increasingly important.
Here again, simulation can help automakers and suppliers implement ADAS by planningfor how safety features will be used or misused.
Ansys VRXPERIENCE Driving Simulator powered by SCANeR integrates with driver
hardware simulator interfaces to create an immersive driving experience with virtualreality. The Ansys VRXPERIENCE HMI module can be used to test and validate the full
cockpit design for HMIs, including virtual displays and actuators, through visual simulation,eye and finger tracking, and haptic feedback. The virtual test driver can interact directlywith the virtual interfaces, from touchscreens to switches, thanks to a fine finger tracking
system. As the system records the behavior of the driver and displays driving andinfotainment information, it identifies and interprets the actions of the driver and triggers
the adapted HMI reaction automatically.
© 2020 ANSYS, INC.

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BE ST PRACT IC E S

Ansys VRXPERIENCE Driving Simulator powered by SCANeR automates scenario variability
creation for massive simulation.

Advanced driver assistance systems have thepotential to prevent more than a third of allpassenger-vehicle crashes. Such a reduction
would prevent 37% of injuries and 29% of deaths
in crashes that involve passenger vehicles.
ADAS developers can easily evaluate the relevance of the displayed information, in realtime, for a safer drive. Ansys VRXPERIENCE reduces the time and cost of design because
the evaluation of the design is mostly performed on virtual prototypes, reducing thenumber of expensive physical mock-ups necessary to create the product.
CREATE A DISTINCT EXPERIENCE
Safety is paramount, but HMIs are also a way for OEMs to differentiate themselves in themarket. Much like the sound of an engine or the feel of a car door closing, the way peopleand automation interact has become a means to build a brand.
Ansys VRXPERIENCE allows OEMs and suppliers to evaluate different driver andpassenger experiences as part of the same overall development process. Beyond ADAS,
Ansys VRXPERIENCE also allows users to visualize the impact of assembly and shapedeviations on the perceived quality of a product, considering manufacturing variations.
Engineers can see and present the influence on perceived quality based on design andmanufacturing data such as materials, fasteners and tolerances. They can simulate
complex deformation effects such as arching, bending and distorting to identify the root
cause of problem areas.
The Ansys VRXPERIENCE SOUND module provides an intuitive graphic display ofsounds and a one-click magnification control feature to help create a sound signature.
Users can also set up psychoacoustic tests based on a listener panel to obtain statistics
about the real perception of sounds. Sound perception can be evaluated using tools basedon time–frequency representations.

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COLLABORATE TO COMBINE TECHNOLOGICAL AND CULTURAL SHIFTSAs with any innovation that has the potential to disrupt the status quo, technology is onlyTAKOT Estory with ADAS. How that technology is used ultimately determines its successpartEofNtheor failure in the market. All aspects of autonomous systems — the sensors that perceive
the environment, the AI that interprets the sensor data and plans for how humans willinteract with automation — must be considered.
The Ansys VRXPERIENCE Driving Simulator powered by SCANeR brings it all together
in a workflow that encourages the collaboration necessary to vet new technology and how
drivers and passengers will react to that technology.
The development of autonomous vehicles is one of the greatest engineering challengesof our day. It’s a long road ahead, with significant milestones along the way. To meet thechallenge, startups and established players are using simulation to get from here to there
safely and efficiently.
Sources
1. “A Long Road to Safety,” RAND Review, July-August, 2016.
2. “Potential Reduction in Crashes, Injuries and Deaths from Large-Scale Deployment ofAdvanced Driver Assistance Systems” A. Benson, B. C. Teft, A.M. Svancara and W. J. Horrey.
AAA Foundation for Traffic Safety, 2018.
3. “Driving to Safety. How Many Miles of Driving Would It Take to Demonstrate 		Autonomous Vehicle Reliability?” Nidhi Kalra and Susan M. Paddock. Rand Corp., 2016.

4. “Vehicle Owners’ Experiences with and Reactions to Advanced Driver Assistance 		
Systems” A. McDonald, C. Carney and D. V. McGehee. University of Iowa, 2018.
5. “Understanding the Impact of Technology: Do Advanced Driver Assistance andSemi-Automated Vehicle Systems Lead to Improper Driving Behavior?”
N. Dunn, T. A. Dingus and S. Soccolich. AAA Foundation for Traffic Safety, 2019.

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