Towards Autonomous Vehicles - PowerPoint Presentation

Slide1

Towards Autonomous Vehicles

Chris SchwarzNational Advanced Driving SimulatorSlide2

Acknowledgements

Mid-America Transportation Center1 year project to survey literature and report on state of the art in autonomous vehiclesCo-PI: Prof.

Geb

Thomas

Undergraduate studentsKory NelsonMichael McCraryMathew PowellNicholas Schlarmannhttp://matc.unl.edu/research/research_projects.php?researchID=405https://www.zotero.org/groups/autonomous_vehicles/itemsSlide3
Slide4

Why Autonomous Vehicles?

Safety32,000 people killed each year, 93% due to driver error, billions in property damage

Autonomous vision is ‘

crashless

’MobilitySafely increase traffic density (x2)-(x3)Greater access for elderly, disabled, etc.SustainabilityFuel savings due to platooning (20%), eliminating traffic jams, reducing trip times, reducing ownership, reducing parking spacesSlide5

Cycles of InnovationSlide6

Vehicle Automation Partner MatrixSlide7

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)Slide8

CMU NAVLAB

RALPH, ALVINN, YARFIn 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, 1995Slide9

National Automated Highway System

A demonstration of the automated highway system in San Diego (1997

). University of California PATH Program

1994-1997Slide10

Intelligent Vehicle Initiative

Prevent driver distractionFacilitate accelerated deployment of crash avoidance systemsNormal conditions

IVIS

Degraded condition

Visibility, drowsinessImminent crashRear end, lane depart, intersection, ESC1997-2005

Multiple ADAS system. Image from IVBSS materials, courtesy of UMTRISlide11

DARPA Grand Challenge

Grand Challenge:

2004 – no winner

2005 – Stanley (Stanford)

Urban Grand Challenge

2007 – Boss (CMU)Slide12

Connected Vehicles

DSRC (5.9 GHz)

Allocated in 2004

Goals

SafetyForward collision, intersection movement assist, lane change, blind spot, do not pass, control loss warning, emergency brake light warningMobilitySustainabilityAERIS

2004-present

VII ->

IntelliDrive

-> Connected Vehicles

Regulatory decision from NHTSA recently announced. V2V will eventually be required in new cars.Slide13

Google Self-Driving Car

2010Slide14

NHTSA Automation Program

LicensingTestingRegulations

Cybersecurity

Currently recommends states

only allow testing2012-present

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

--

NHTSA Levels of AutomationSlide15

Future Societal Impacts

Light Cars: A Virtuous Cycle

Autonomous Car Sharing

MIT’s Stackable City CarSlide16

A Bottom-up approachSlide17

Advanced Driver Assistance Systems

ACC

Pre-Crash

LDWS

Sensor

Year

Sensor

Year

Sensor

Year

Audi

Radar/Video

2011

Camera

2007

BMW

Camera

2007

Chrysler

Laser

2006

Ford

Radar

2009

Radar

2009

Camera

2010

GM

Radar

2004

Camera

2008

Honda

Radar

2003

Camera

2003

Kia

Camera

2010

Jaguar

Radar

1999

Lexus

Laser

2001

Mercedes

Radar

2001

Radar

2002

Camera

2009

Nissan

 Camera2001SaabRadar2002    ToyotaLaser1998Radar2003Camera2002Volkswagen  Radar/Video2011  VolvoRadar2002Radar/Video2007  

A 2011 review of commercial ADAS systems compares manufacturers, model year, and sensor type for three types of systems (

Shaout

,

Colella

, and

Awad

2011)Slide18

ADAS Automation

Abb.

System

Abb.

System

ESC

Electronic Stability Control

DD

Drowsiness Detection

FCW

Forward Collision Warning

AL

Adaptive Lighting

ACC

Adaptive Cruise Control

PM

Pedal Misapplication

LDW

Lane Departure Warning

TSR

Traffic Sign Recognition

LKA

Lane Keeping Assist

TJA

Traffic Jam Assistant

LCA

Lane Change Assist

CZA

Construction Zone Assist

RCTA

Rear Cross Traffic Alert

PA

Parking Assistant

BSD

Blind Spot Detection

PP

Parking Pilot

EBA

Emergency Brake Assist

HC

Highway Chauffeur

AEBS

Advanced Emergency Braking System

HP

Highway Pilot

ESA

Emergency Steer Assist

 Slide19

A Top-down ApproachSlide20

Personal Rapid Transit (PRT)

Fully autonomousNo operator, no controlsLow speedMay use a guideway

Morgantown PRT entered operation in 1975 in West VirginiaSlide21

PRTs (cont.)

Morgantown, WVMasdar City (on hold)London Heathrow AirportCity Mobil 2

Suncheon

, South Korea

Punjab, IndiaEarly criticisms of PRTs on guideways concern the scalability of the systemBut new concepts are leaving guideways behind, alleviating some of these concernsSlide22

Elements of AutomationSlide23

Automation Sensors

High grade LIDAR

Inconspicuous LIDAR

GPS / IMU

RADAR

Cameras

Digital Maps

DSRCSlide24

Localization & Object DetectionSlide25

Probabilistic Methods

The world is messy with uneven edges, bad lighting, poorly marked roads, and unpredictable peopleApplications of probabilistic reasoningHistogram filters (lane line tracking)

Particle filters,

Kalman

filters (object tracking)Bayesian Networks (decision making)Hidden Markov Models (state estimation)Slide26

Some Online Courses

Udacity online coursesSlide27

Digital Maps & Mapping

Digital maps negate the need to dynamically map the environmentSimultaneous 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 inputsSlide28

Challenges of AutomationSlide29

Weather Challenges

Bob Donaldson / Post-GazetteSlide30

Testing & Certification

Logic

Sensor Failures

Kalman

FiltersFalse Positives

Histogram Filters

Particle Filters

Data Fusion

More data (images & video)

More test cases

Path Planning

Decision Making

Digital Maps

All speeds

Parking Lots

Many more testsSlide31

Transfer of Control

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

--

Example:Slide32

Legality

“Automated vehicles are probably legal in the United States” – Bryant Walker Smith1949 Geneva Convention on Road Traffic requires that the driver of a vehicle shall be at all times able to control itWho is liable: the driver or the manufacturer?

California, Nevada, and Florida have paved the way with state laws for automated vehiclesSlide33

Hacking Entry Points

Entry point

Weakness

Telematics

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.

MP3 malware

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.

Infotainment apps

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.

Bluetooth

The system that connects your smartphone to your car can be used as another entry point into the in-vehicle network.

OBD-II

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.

Door Locks

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.

Tire Pressure Monitoring System

Wireless TPMS systems could be hacked from adjacent vehicles, identify and track a vehicle through its unique sensor ID, and corrupt the sensor readings.

Key Fob

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.Slide34

Vehicle Networks to Secure

Network

Weakness

LIN

Vulnerable at a single point of attack. Can put LIN slaves to sleep or make network inoperable

CAN

Can jam the network with bogus high priority messages or disconnect controllers with bogus error messages

FlexRay

Can send bogus error messages and sleep commands to disconnect or deactivate controllers

MOST

Vulnerable to jamming attacks

Bluetooth

Wireless networks are generally much more vulnerable to attack than wired networks. Messages can be intercepted and modified, even introducing worms and virusesSlide35

Privacy

Electronic Data Recorders (Black Box)Identified network trafficDe-identified dataThe myth of anonymity

“Google’s self-driving car gathers almost 1 Gb per second” – Bill Gross,

IdealabSlide36

Privacy By Design

Proactive not reactivePrivacy by defaultPrivacy embedded into the designFull functionality (positive sum, not zero sum)

End-to-end security (full lifecycle protection)

Visibility and transparency

Respect for user privacySlide37

DiscussionSlide38

Case Study: Autonomous Intersections

and Time to Collision PerceptionTime to Collision (TTC)

r

ange / range rate

Autonomous Intersection ManagementU Texas at AustinReservation system

Van der Horst, 1991

Autonomous Intersection (Top down)

Autonomous Intersection (Driver's View)Slide39

The Trouble With Levels

The evolution of vehicle automation and associated challenges

Levels are not a roadmap

Levels are not design guidelines

Levels discourage

potentially helpful ideas like adaptive automation strategiesSlide40

5 – 30 years until autonomous vehicles hit the road