Transcript Autonomous Vehicles - Mid-America Transportation Center (MATC)

Towards Autonomous Vehicles
Chris Schwarz
National Advanced Driving Simulator
Acknowledgements
• Mid-America Transportation Center
– 1 year project to survey literature and report on state
of the art in autonomous vehicles
– Co-PI: Prof. Geb Thomas
– Undergraduate students
•
•
•
•
Kory Nelson
Michael McCrary
Mathew Powell
Nicholas Schlarmann
– http://matc.unl.edu/research/research_projects.php?researchID=405
– https://www.zotero.org/groups/autonomous_vehicles/items
Why Autonomous Vehicles?
• Safety
– 32,000 people killed each year, 93% due to driver error,
billions in property damage
– Autonomous vision is ‘crashless’
• Mobility
– Safely increase traffic density (x2)-(x3)
– Greater access for elderly, disabled, etc.
• Sustainability
– Fuel savings due to platooning (20%), eliminating traffic
jams, reducing trip times, reducing ownership, reducing
parking spaces
Cycles of Innovation
Vehicle Automation Partner Matrix
Academic
Government
Private
Military
A
G
P
M
An early experiment on automatic highways was conducted by RCA and the
state of Nebraska on a 400 foot strip of public highway just outside Lincoln
(“Electronic Highway of the Future - Science Digest (Apr, 1958)” 2013)
A
G
P
M
CMU NAVLAB
• RALPH, ALVINN, YARF
• In 1995, RALPH drove NAVLAB 5 over 3000
miles from Pittsburgh to Washington, DC.
– Steered autonomously 96% of the way from
Pittsburgh, PA to Washington DC
Pomerleau, 1995, RALPH: Rapidly Adapting Lateral Position Handler,
IEEE Symposium on Intelligent Vehicles, September, 1995
A
G
P
M
National Automated Highway System
1994-1997
A demonstration of the automated highway
system in San Diego (1997). University of
California PATH Program
A
G
P
M
Intelligent Vehicle Initiative
1997-2005
• Prevent driver distraction
• Facilitate accelerated
deployment of crash
avoidance systems
Radar
Lane-change/Merge
(LCM)
Vision
Lateral Drift
Warning (LDW)
– Normal conditions
• IVIS
– Degraded condition
• Visibility, drowsiness
Curve
speed
Warning
(CSW)
Forward Crash
Warning (FCW)
– Imminent crash
• Rear end, lane depart,
intersection, ESC
Multiple ADAS system. Image from IVBSS
materials, courtesy of UMTRI
A
G
P
M
DARPA Grand Challenge
Grand Challenge:
2004 – no winner
2005 – Stanley (Stanford)
Urban Grand Challenge
2007 – Boss (CMU)
A
G
P
M
Connected Vehicles
2004-present
• DSRC (5.9 GHz)
– Allocated in 2004
• Goals
– Safety
VII -> IntelliDrive -> Connected Vehicles
Regulatory decision from NHTSA
recently announced. V2V will
eventually be required in new cars.
• Forward collision, intersection
movement assist, lane change,
blind spot, do not pass, control
loss warning, emergency brake
light warning
– Mobility
– Sustainability
• AERIS
A
G
P
M
Google Self-Driving Car
2010
A
G
P
M
NHTSA Automation Program
• Licensing
• Testing
• Regulations
2012-present
• Cybersecurity
• Currently recommends
states only allow testing
NHTSA Levels of Automation
Level
Example
Transition Time to Manual
(Heuristic)
0 – No Automation
Warning only
--
1 – Function-specific Automation
ADAS
< 1 second
2 – Combined Function Automation
Super cruise
< 1 minute
3 – Limited Self-Driving Automation
Google car
< 10 minutes
4 – Full Self-Driving Automation
PRT
--
A
G
P
M
Future Societal Impacts
Light Cars: A Virtuous Cycle
Autonomous Car Sharing
Reduce
mass
Smaller
fuel
supply
Downsize
engine
Drivetrain
brakes
tires
MIT’s Stackable City Car
A Bottom-up approach
Advanced Driver Assistance Systems
ACC
Audi
BMW
Chrysler
Ford
GM
Honda
Kia
Jaguar
Lexus
Mercedes
Nissan
Saab
Toyota
Volkswagen
Volvo
Sensor
Year
Laser
Radar
Radar
2006
2009
2004
Radar
Laser
Radar
1999
2001
2001
Radar
Laser
2002
1998
Radar
2002
Pre-Crash
Sensor
Year
Radar/Video
2011
LDWS
Sensor
Camera
Camera
Year
2007
2007
Camera
Camera
Camera
Camera
2010
2008
2003
2010
Radar
2009
Radar
2003
Radar
2002
Camera
Camera
2009
2001
Radar
Radar/Video
Radar/Video
2003
2011
2007
Camera
2002
A 2011 review of commercial ADAS systems compares
manufacturers, model year, and sensor type for three
types of systems (Shaout, Colella, and Awad 2011)
ADAS Automation
Abb.
ESC
FCW
ACC
LDW
LKA
LCA
RCTA
BSD
EBA
AEBS
ESA
System
Electronic Stability Control
Forward Collision Warning
Adaptive Cruise Control
Lane Departure Warning
Lane Keeping Assist
Lane Change Assist
Rear Cross Traffic Alert
Blind Spot Detection
Emergency Brake Assist
Advanced Emergency Braking System
Emergency Steer Assist
Abb.
DD
AL
PM
TSR
TJA
CZA
PA
PP
HC
HP
System
Drowsiness Detection
Adaptive Lighting
Pedal Misapplication
Traffic Sign Recognition
Traffic Jam Assistant
Construction Zone Assist
Parking Assistant
Parking Pilot
Highway Chauffeur
Highway Pilot
A Top-down Approach
Personal Rapid Transit (PRT)
• Fully autonomous
• No operator, no
controls
• Low speed
• May use a guideway
• Morgantown PRT
entered operation in
1975 in West Virginia
PRTs (cont.)
• Morgantown, WV
• Masdar City (on hold)
• London Heathrow
Airport
• City Mobil 2
• Suncheon, South Korea
• Punjab, India
• Early criticisms of PRTs
on guideways concern
the scalability of the
system
• But new concepts are
leaving guideways
behind, alleviating
some of these concerns
Elements of Automation
Automation Sensors
High grade LIDAR
Inconspicuous LIDAR
GPS / IMU
Cameras
RADAR
DSRC
Digital Maps
Localization & Object Detection
Probabilistic Methods
• The world is messy with uneven edges, bad
lighting, poorly marked roads, and
unpredictable people
• Applications of probabilistic reasoning
– Histogram filters (lane line tracking)
– Particle filters, Kalman filters (object tracking)
– Bayesian Networks (decision making)
– Hidden Markov Models (state estimation)
Some Online Courses
• Udacity online courses
Digital Maps & Mapping
• Digital maps negate the need to dynamically
map the environment
• Simultaneous Localization & Mapping (SLAM)
used to create environments in unmapped
areas
• Many modern path planning algorithms are
based on A* algorithm
• Must find the proper correspondence
between the digital map and other sensor
inputs
Challenges of Automation
Weather Challenges
Bob Donaldson / Post-Gazette
Testing & Certification
Path Planning
Decision Making
Digital Maps
All speeds
Parking Lots
Many more tests
Histogram Filters
Particle Filters
Data Fusion
More data (images & video)
More test cases
Logic
Sensor Failures
Kalman Filters
False Positives
Transfer of Control
Example:
Transfer of Control to a Platoon
Level
Example
Transition Time to Manual
0 – No Automation
Warning only
--
1 – Function-specific Automation
ADAS
< 1 second
2 – Combined Function Automation
Super cruise
< 1 minute
3 – Limited Self-Driving Automation
Google car
< 10 minutes
4 – Full Self-Driving Automation
PRT
--
Legality
• “Automated vehicles are probably legal in the
United States” – Bryant Walker Smith
• 1949 Geneva Convention on Road Traffic
requires that the driver of a vehicle shall be at
all times able to control it
• Who is liable: the driver or the manufacturer?
• California, Nevada, and Florida have paved the
way with state laws for automated vehicles
Hacking Entry Points
Entry point
Telematics
MP3 malware
Infotainment apps
Bluetooth
OBD-II
Door Locks
Tire Pressure
Monitoring System
Key Fob
Weakness
The benefit of such systems is that the car can be remotely disabled if stolen, or
unlocked if the keys are inside. The weakness is that a hacker could potentially
do the same.
Just like software apps, MP3 files can also carry malware, especially if
downloaded from unauthorized sites. These files can introduce the malware
into a vehicles network if not walled off from safety-critical systems.
Car apps are like smartphone apps…they can carry viruses and malware. If the
apps are not carefully screened, or if the car’s infotainment software is not
securely walled off from other systems, then an attack can start with a simple
app update.
The system that connects your smartphone to your car can be used as another
entry point into the in-vehicle network.
This port provides direct access to the CAN bus, and potentially every system of
the car. If the CAN bus traffic is not encrypted, it is an obvious entry point to
control a vehicle.
Locks are interlinked with other vehicle data, such as speed and acceleration. If
the network allows two-way communication, then a hacker could control the
vehicle through the power locks.
Wireless TPMS systems could be hacked from adjacent vehicles, identify and
track a vehicle through its unique sensor ID, and corrupt the sensor readings.
It’s possible to extend the range of the key fob by an additional 30’ so that it
could unlock a car door before the owner is close enough to prevent an
unwanted entry.
Vehicle Networks to Secure
Network
LIN
CAN
FlexRay
MOST
Bluetooth
Weakness
Vulnerable at a single point of attack. Can put LIN slaves to sleep or make
network inoperable
Can jam the network with bogus high priority messages or disconnect controllers
with bogus error messages
Can send bogus error messages and sleep commands to disconnect or deactivate
controllers
Vulnerable to jamming attacks
Wireless networks are generally much more vulnerable to attack than wired
networks. Messages can be intercepted and modified, even introducing worms
and viruses
Privacy
• Electronic Data Recorders (Black Box)
• Identified network traffic
• De-identified data
– The myth of anonymity
• “Google’s self-driving car gathers almost 1 Gb
per second” – Bill Gross, Idealab
Privacy By Design
•
•
•
•
•
•
•
Proactive not reactive
Privacy by default
Privacy embedded into the design
Full functionality (positive sum, not zero sum)
End-to-end security (full lifecycle protection)
Visibility and transparency
Respect for user privacy
Discussion
Case Study: Autonomous Intersections
and Time to Collision Perception
• Time to Collision (TTC)
– range / range rate
• Autonomous Intersection
Management
– U Texas at Austin
– Reservation system
Autonomous Intersection (Top down)
Autonomous Intersection (Driver's View)
Van der Horst, 1991
The Trouble With Levels
• Levels are not a roadmap
• Levels are not design guidelines
• Levels discourage potentially helpful ideas like
adaptive automation strategies
The evolution of vehicle automation and associated challenges
5 – 30 years until autonomous vehicles hit the road