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Part 1: The State of CAV TechnologyJune 2016June 2016June 2016June 2016

Index

I. Autonomous Car

Technology

I. Sensors

I. Lidar

II. GPS

III. Cameras

IV. Radar

V. Other

II. Computer

Systems

III. Software

IV. Levels of

Automation

V. Semi

Autonomous

Features

II. Current Company

Formulas

I. Google

II. SMART

III. Mercedes

IV. Volvo

V. Others Matrix

VI. Conclusions

III. Vehicle &

Infrastructure

Connectivity

I. Applications

II. Results

III. Path to V2X

IV. Bandwidth

Congestion

V. Establishing a

network

VI. Federal

Mandates

VII. Roadside Units

VIII. Federal Program

Opportunities

IV. Near Term

Conclusions

I. Infrastructure

Response

II. Next Step –

Planning/Policy

Autonomous Vehicle TechnologyLIDARProduces a 360 degree 3d model

of the surroundings

Video CameraMonitors frontward, lane

departure and reads traffic signals

RadarMonitors surroundings

Odometry SensorsMonitors vehicle distance

travel and speed

GPSTracks the car location

geospatially

UltrasonicSenses at low speeds

Internal CPU

V2V, V2I CommunicationConnects with other cars and

supporting infrastructure

● Ultrasonic

● Short/Long

Range Radar

● Lidar

● Camera

• Surround View

• Digital Side Mirror

• Surround View

• Park Assistance

• Rear View Mirror

• Rear Collision Warning

• Park Assist

• Blind Spot Warning

• Cross Traffic Warning• Lane Departure

Warning

• Traffic Sign

Recognition

• Cross Traffic Warning

• Emergency Braking

• Pedestrian Detection

• Collision Avoidance

• Adaptive Cruise Control

• Environment Mapping

Car Sensor Suite

1) Traffic-Sign Recognition

2) Obstacle Detection

3) Lane Detection

4) Terrain Mapping

5) Vehicle Detection

6) Oncoming-Vehicle Detection

7) Blind-Spot Monitor

8) Parking-Lot Detection

9) Scene Classification and Tunnel

Detection

10)Pedestrian Detection

Sensor Requirements

Light Detection and Ranging

Autonomous vehicles use LIDAR for obstacle detection and avoidance to navigate safely through

environments. By emitting pulses of ultraviolet, visible or near infrared light (using lasers) and then

recording the amount of time to read a reflection of the pulse, LIDAR can use the speed of light to detect

distance to an object. Through emitting the light in a circular motion and at different angles, the

returned reading can be coded to create a 3 dimensional point map of the sensor’s surroundings.

How It Works

• Lidar is currently effective to a range of 200 M.

• Accuracy is heavily dependent on the number of lasers emitted from the sensor - common Lidar

range is available from 4 laser to 64 laser.

• Costs for Lidar range from $8,000 for a 4 laser unit to $75,000 for a 64 laser unit.

• Works well at night

• Issues in snow/rain

Capabilities/Limitations

64 laser LIDAR

LIDAR$8,000 – $75,000

Costs per Unit

A Closer Look at LIDAR Cost Projections

Accuracy is heavily dependent on the number of lasers emitted from the LIDAR sensor - common commercially

available LIDAR ranges from 4 to 64 laser system. The high level system used by Google costs $80,000. Ford uses 4 32-

laser systems at $30,000 each.

Issue

HDL-64E – $80,000,

(64 channels, 200 m)

• Market leader Velodyne anticipates that when demand reaches 1 Million units a year, the production cost will

go from $75,000 to $500 for a 64 laser unit. They see this drop as soon at 2018.

• Google has begun developing their own LIDAR system in house.

• Tesla believes LIDAR is not necessary for a vehicle, instead relying on radar and cameras.

• Quanergy has developed solid state LIDAR which, in full production is projected to be $250 or less

Potential Solutions

VLP-16 Puck – $8,000

(16 channels, 100 m)

LIDAR

Global Positioning Systems

The US Department of Defense operates 27 satellites in space that broadcast microwave signals to earth

that contain their coordinates, heading, velocity and timestamp. The orbits of these satellites are

coordinated so that, at any given time, 4 are visible in the sky at any point on earth. With the collective

data from these microwave signals, a GPS unit is able to triangulate its position on earth.

How It Works

• GPS is accurate to 11 feet.

• Accuracy is heavily dependent on the ability to see sky. Operation in tunnels and dense urban

corridors with blocking structures is suspect.

• Works well at night, in the rain and snow


GPS$100 - $2,000

Costs per Unit

GPS position (white box) vs. Google Car

GPS is not accurate enough to precisely locate cars in the correct

lane.

Issue

• Currently, autonomous vehicles combine GPS readings with other sensor readings to locate

themselves on high precision maps.

• Through adding another signal from a known terrestrial position and using additional carrier

waves from the GPS satellites, differential GPS can produce readings accurate to 1 cm. Todd

Humphreys, at the University of Texas, is testing technology that provides this level of

accuracy, even at high speed.

Potential Solutions

Camera systems produce live video output. This output is analyzed by a

computer system to determine obstacles and provide roadway information,

such as relation to lane striping and signal meanings. Currently, most

automakers rely on LIDAR to produce their 3d maps in which to navigate

their vehicles, and use cameras for obstacle detection, lane departure and

signal reading. However some automakers, Tesla in particular, believes

there might be a way to make fully autonomous vehicles using video

cameras as the main sensor.

How It Works

• Significant ability to look far down the road provides ample information,

however “strong AI” is needed to extract depth information from video.

• Video cameras do not need preloaded high resolution maps provided to

the car

• Issues working at night, and in limited visibility, can be overcome by

super sensitive cameras

• Heavily software driven, this is a very cost effective sensor method.


Cameras$100 - $200

Costs per Unit

• Israeli company Mobileye’s camera technology is in 8 major car

manufacturers. This company believes they can create an intelligent enough

AI, based on cameras, that would effectively learn from other vehicles where

landmarks are and appropriate driving behavior.

Potential

Radar sends out radio waves and listens for an echo to determine

distance and location of surrounding objects.

How It Works

• Radar wavelengths can penetrate dust and other visual

obscurants, allowing the car to “see” in poor visibility.

• Radar does not work well in snow and rain.

• Radar works very well along a two dimensional

plane. Higher angular resolution needed for 3d images can

be obtained only with inconveniently large antenna

apertures.

• Radar is a well established commodity and is well developed

in other technologies.


Radar$100 - $200

Costs per Unit

Near range sensors that utilize high frequency sound waves to

detect nearby objects. These sensors are currently used in slow

speed operations, such as backing up. Additionally, they may be

used to track vehicles in adjacent lanes

Ultrasonic Sensors

• A very limited sensor range of 6 m on average

• Very low power and cost effective

• Generally very reliable


Other

$15 - $200

Costs per Unit

Motion sensors that estimate the change of position over time,

sometimes by counting the revolutions of a wheel.

Odometry Sensors

• Low cost

• Often inaccurate with error that builds with

ever rotation.

• Can be calibrated over time and combined

with other sensors for an accurate reading


$15 - $200

Costs per Unit

Computers with specially designed chipsets must take in all the

senor information and produce driving instructions for the vehicle

How It Works

• Relatively available technology already enables the

necessary number of calculations.

• NVIDIA’s current premier system offers 2.3 teraflops

(roughly 20% more than a PlayStation 4) and can handle 12

sensor feeds. Nvidia recently began showcasing its Drive PX

2, with 8 Tflops (equivalent to roughly 150 Macbook pros)

• Mobileye, in conjunction with STMicrolectonics, is

anticipating making available a 12 Teraflop platform by 2020

that will be able to handle 20 sensor feeds.

• These more advance CPUs will need water cooling


Computer Systems $10,000

Costs per Unit

There are a multitude of software architectures that are being explored and advocated by

various studies and research. The most common appear to enable two key aspects:

1) Cm level localization on high accuracy maps though GPS positioning corrected by

sensor data that relates relative position to landmarks allows for the construction of a

virtual grid that the car can use to determine unobstructed areas.

2) Identify surrounding objects and motion vectors (speed and direction) to help

determine relation to other roadway users/objects.

How It Works

• Precise localization (to within a cm accuracy) requires high

accuracy GPS maps. Since there is low penetration of autonomous

vehicles, these maps are currently made through dedicated

service vehicles that must drive the routes before autonomous

vehicles may go through.

• Camera based systems (non-LIDAR) attempt localization through

spatial relationships to landmarks as identified on video output.

• Software looks for a set of rules and often the driving public

ignores these rules. Most major programs incorporate a machine

learning component


Software

High Accuracy Map Data

Localization Object Detection

Mission Planning Object Tracking

ReprojectionMotion Planning

Path Following

Image dataLIDAR data

Current PositionCurrent Position

Global Waypoint

Local Waypoint

Object Positions

Object Vector

Object Position,

Direction, Speed

Velocity and

Angle

SoftwareObject Identification and Vectoring Grid Base Occupancy

“The driver has overall control, and is solely responsible for safe operation, but can choose to cede limited authority

over a primary control (as in adaptive cruise control), the vehicle can automatically assume limited authority over a

primary control (as in electronic stability control), or the automated system can provide added control to aid the driver

in certain normal driving or crash-imminent situations (e.g., dynamic brake support in emergencies).”

Level 1

NHTSA Levels of Automation

“This level involves automation of at least two primary control functions designed to work in unison to relieve the

driver of control of those functions. Vehicles at this level of automation can utilize shared authority when the driver

cedes active primary control in certain limited driving situations. The driver is still responsible for monitoring the

roadway and safe operation and is expected to be available for control at all times and on short notice.“

Level 2

“Vehicles at this level of automation enable the driver to cede full control of all safety-critical functions under certain

traffic or environmental conditions and in those conditions to rely heavily on the vehicle to monitor for changes in

those conditions requiring transition back to driver control.”

Level 3

“The vehicle is designed to perform all safety-critical driving functions and monitor roadway conditions for an entire

trip”

Level 4

Adaptive

Cruise Control

Adaptive Cruise

Control + Lane Assist

Open Road Automated

Vehicle

Automated Valet

Incr

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Sa

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Incr

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two

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Not actually an NHTSA Level, but included in the Society of Automotive Engineers, these cars have no steering

controls and are expected to go from point to point with absolutely no assistance from the driver.

Level 5

Semi-Autonomous Features

The car will identify the vehicle in front of it and match speeds to maintain

a safe following distance (set by the user) while not exceeding a certain

speed (also set by the user)

Adaptive Cruise Control

Automatically adjust speeds in a traffic jam, including braking to a full stop,

and handles the steering. Driver must stay alert, but does not have to

touch the wheel or pedals.

Traffic Jam Assist

Alerts the driver when the system detects that the vehicle is about to leave

its lane and can automatically correct the steering and keep the car on

course

Lane Keep Assist

The car will detect panicked breaking and apply more pressure to the

brakes to stop the car faster.

Emergency Brake Assist

Automatically parallel parks a car, as long as the gap is 1.2 times the size of

the car.

Parking Assist

Automatically applies the brakes for obstacle avoidance.

Auto Braking

Semi-autonomous features are safety based – and their incorporation in

current models will begin to reduce accidents in the next 5 to 10 years.

Conclusion

Index

I. Autonomous Car

Technology

I. Sensors

I. Lidar

II. GPS

III. Cameras

IV. Radar

V. Other

II. Computer

Systems

III. Software

IV. Levels of

Automation

V. Semi

Autonomous

Features

II. Current Company

Formulas

I. Google

II. SMART

III. Mercedes

IV. Volvo

V. Others Matrix

VI. Conclusions

III. Vehicle &

Infrastructure

Connectivity

I. Applications

II. Results

III. Path to V2X

IV. Bandwidth

Congestion

V. Establishing a

network

VI. Federal

Mandates

VII. Roadside Units


Opportunities

IV. Near Term

Conclusions

I. Infrastructure

Response

II. Next Step –

Planning/Policy

• Level 4 • Electric Hybrid • 2 people capacity

Sensors

Communications: 5G

Software: Proprietary system based on grid based occupancy

Sensors:

• LIDAR – 64 lasers beams, camera creates a 3D image of objects helping the car see

hazards. Laser can calculate distance and create images for objects in a 200m range

• Two front cameras for lane delineation and traffic sign/landmark identification.

• Bumper Mounted Radar – 4 radars mounted on car’s front and rear bumper

• Rear ultrasonic sensors– helps keep track of the movements of the car and alert the car

about the obstacles in the rear.

• Inside car – Altimeters (instrument for determining altitude attained), Gyroscopes (),

and tachymeters (measuring speed)

• Aerial that reads precise geo-location – car receives information about the precise

location of the car aka GPS satellites. GPS data is compared with sensor map data

previously collected from same location.

Google expects to launch commercially available autonomous vehicles

in Spring 2019. However, they are seeking to license their technology

to other auto companies. They recently signed an agreement with

Chrysler to automate 100 Chrysler minivans. Google see full adoption

in 10 years and they predict a ridesharing model.

• High accuracy GPS maps must be made before the car can go through an area, changes in actual

conditions could have unexpected consequences

• Hasn’t driven in cold weather like snow

• Pedestrians are detected as moving, column-shaped blurs of pixels, so what if a police officer is waving for

traffic to stop – Google is working on being able to identify this as well

Market View

Technology

Limitations

Current Total Cost:

$150,000

Projected cost in 5 years:

$10,000

Cost Projections

● Front Camera

● Radar

● LIDAR

● Ultra Sonic

• Level 4 • Electric • 4 people capacity

Sensors

Communication: V2V enabled

Sensors: SMART relies solely on low cost Lidar and one camer – it is not

dependent on GPS

Mobility on Demand: Mobility-on-Demand (MoD) Transportation

Model, SMART is building a mobile app that will exemplify the

ridesharing model. Users will be able to call for a Taxi and be ferried to

their destination without a human ever touching the controls.

A team of 25 researchers from the National University of

Singapore (NUS) and the Singapore-MIT Alliance for Research

and Technology (SMART), which has now received $16 m in

venture funding, plan to have a fleet of Level 4 Taxis on the

road in Singapore by 2018. This project is backed by the

government of Singapore who wants to have the first fleet of

self-driving vehicles in the world.

• Electric car can only go 62-80 miles on a charge, and a full

charge takes 6-8 hours

• Current autonomous mode maxes out at 18 mph

Market View

Technology

Limitations

Current Total Cost:

$23,500

Projected cost in 5 years:

$7,800

Cost Projections

● Camera

● LIDAR

• Level 3 • Electric Power • 2 people capacity

Communications: V2V

Software: “Highway Pilot”

Sensors: Radar and Camera sensors

Technology:

• LED Lights go from white to blue when the truck’s driving itself and replace the headlights.

• The truck uses platooning and an aerodynamic trailer designed to limit wind resistance and

cut fuel consumption by as much as 5 – 15 percent. When platooning, vehicles are

separated by only 50 feet (as opposed to the 164 normal separation)

• Doesn’t need to check google maps, the truck has a navigation system to independently find

the best route.

• The system does not make decisions simply bases on information from its own sensors.

Instead, the truck acquires a significant amount of information by exchanging data with

other vehicles, infrastructures stationary communication network, and by satellite

navigation

Mercedes Benz is test driving Level 3 trucks in European

highways, Daimler, their parent company, is testing autonomous

trucks in Nevada. In early 2016 Mercedes platooned 3 trucks

across Europe, with no drivers in the two following trucks.

Currently, these trucks are being tested on public German

highways.

• Level 3 automation keeps the need for a human driver when not on the

highway

• Level 3 has to give ample warning for the driver to safely resume control.

• Public perception may hinder adoption

Market View

Technology

Limitations

…And then there is Volvohttps://www.youtube.com/watch?v=2q00jIBhkq4

http://www.volvocars.com/intl/about/our-innovation-

brands/intellisafe/intellisafe-autopilot/this-is-autopilot

Market Projections

US Market Share Level 2 Level 3 Level 4

20% 2016 2020

16% 2016 2020

13% 2016 2020

12%

9% 2016 2020

7% 2016 2018 2020

7% 2016 2020 2030

3% 2016 2020

2% 2016 2017 2020

Level 2 Level 3 Level 4

2016 2025

2016 2021

2016 2030

2016 2017 2018

2016 2016 2018

2020

Others:

“Richard Holman, a 30-year automotive veteran running GM’s foresight and trends unit, said Tuesday that three years ago most

industry participants would have estimated 2035 as a reasonable timetable for self-driving cars. Speaking to a conference in

suburban Detroit, Mr. Holman said now most people see that technology being deployed by 2020, if not sooner.” – Wall Street

Journal. May 10, 2016

“Level-four vehicles—[which operate] in a defined area that’s been 3-D mapped—we think that somebody in the industry will

have by the end of the decade. A level-five vehicle, which is, you go into your car, you hit a button, you go to sleep and you wake

up at grandma’s house, that is a long way away—15, 20 years.” – Mark Fields, President and CEO, Ford Motor Co. in the Wall

Street Journal. April 10, 2016

Quotable

Conclusions

• Most auto dealers already provide Level 2 autonomy in high end models

• Major American dealers anticipate a 2020 launch of commercially available autonomous vehicles

• Most Japanese makers (Toyota, Honda, Nissan) anticipate high levels of autonomy by 2020, but not to the same

level as the American Automakers. These companies are generally behind in their efforts to build an autonomous

car, but are making moves to catch up. Toyota, in particular, is spending $1 billion in R&D.

• European automakers offer a mixed bag of expectation, ranging from being ready in the next year or two (Volvo) to

at the end of next decade (Audi)

Autonomous

Index

I. Autonomous Car

Technology

I. Sensors

I. Lidar

II. GPS

III. Cameras

IV. Radar

V. Other

II. Computer

Systems

III. Software

IV. Levels of

Automation

V. Semi

Autonomous

Features

II. Current Company

Formulas

I. Google

II. SMART

III. Mercedes

IV. Volvo

III. Others Matrix

IV. Conclusions

V. Vehicle &

Infrastructure

Connectivity

I. Applications

II. Results

III. Path to V2X

IV. Bandwidth

Congestion

V. Establishing a

network

VI. Federal

Mandates

VII. Roadside Units


Opportunities

VI. Near Term

Conclusions

I. Infrastructure

Response

II. Next Step –

Planning/Policy

Vehicle and Infrastructure Connectivity

Vehicles exchange information to determine location, speed

and heading and provide warnings and driver assistance:

• Forward collision warning

• Emergency electronic brake light

• Blind spot/lane change warning

• Do not pass warning

• Intersection movement assist

• Left turn assist

Vehicle to Vehicle (V2V)

Infrastructure sends situation to vehicles to allow mapping of

intersection, signal phase and signal change timing and

provide various warnings:

• Curve speed warning

• Red light violation warning

• Transit pedestrian detection

• Spot weather impact warning

• Disabled/oversized vehicle warning

Vehicle to Infrastructure (V2I)

V2X encompasses vehicle communication other vehicles and

infrastructure, and with all other things. For instance, vehicles

will be able to communicate with other systems embedded in

other possible roadway users such as mobile phone users or

pet collars or drones.

V2X

Potential Connected Vehicle Applications

Vehicle to Infrastructure

• Intersection applications

• Speed applications

• Vulnerable road users

• Transit safety

Mobility

• Enable advanced traveler information systems (ATIS)

• Integrate network flow optimization (INFLO)

• Freight advanced traveler information systems

(FRATIS)

• Multimodal intelligent traffic signal systems (M-ISIG)

• Response, emergency staging and communications,

uniform management, and evacuation (RESCUME)

• Integrated dynamic transit operations (IDTO)

• Next generation integrated corridor management

(ICM)

• Information for maintenance and fleet management

systems

• Information and routing support for emergency

responders

• Smart roadside

AERIS

• Eco-signal operations

• Dynamic eco-lanes

• Dynamic low emissions zones

• Support for alternative fuel vehicle operations

• Eco-traveler information

• Eco-integrated corridor management decision

support system

• Road weather

• International border crossings

• Fee payments

• Agency data applications

• Performance measures

• CV-enabled traffic studies

• Probe data applications

Connected Vehicles Results

• Crashes are the leading cause of death for Americans

between the ages of 5 and 44 years

• The application of connected vehicle technologies is

expected to offer some of the most promising opportunities

for crash reductions

Deployment of connected vehicle systems and the combined

use of V2V and V2I applications have the potential to affect up

to 81 percent of unimpaired crash types involving cars or heavy

vehicles

• Congestion in 498 urban areas during 2011 accounted for 5.5

billion hours of extra time and 2.9 billion gallons of wasted

fuel at a cost of $121 billion annually

• The cost to the average commuter was $818

Certain connected vehicle applications should significantly

reduce travel delays, at the very least they will open up big data

opportunities for system optimizations

Level 3 and above automated vehicles may be able to take advantage of

platooning or synchronized movements (including merging).

Vehicle Emissions

• Emissions can be greatly reduced through connected vehicle technology,

through a reduction of fuel consumption, idling and vehicle miles

traveled

System Wide Congestion

• Shorter inter-vehicle distance

• Higher roadway capacity

• Shorter travel times without additional roadway infrastructure

Highway Safety

Traffic Congestion

Connected & Autonomous

Autonomous Intersection Management (AIM)

• Intersection Manager replaces traditional

traffic signal and serves as an RSU

• AIM relies on communication between

vehicles and Intersection Manager

• Vehicle can communicate wirelessly with

Intersection Manager and vice versa

How it WorksAIM

• Relies on ‘call-ahead’ concept:

• Every car must send a reservation message to

the Intersection Manager, and it will check the

availability of the requested space

• If the requested message is not in conflict with

the intersection policy, a car is allowed to pass

through the intersection

• Otherwise, the car has to generate and send a

new request message until it gets the

permission from the intersection

• Or, in the worst case, stop before

entering the intersection

Allocated in 1999, theFCC must protect the 5850-5925

MHz Dedicated Short Range Communication (DSRC)

spectrum for intelligent transportation systems. Right

now it is under attack from wifi signal providers.

Companies must develop technology to

handle Road Side Units (RSU)

connectivity with On Board Units(OBU)

and OBU to OBU communication

The Path to V2X

In order to have a working V2V system at the most

basic level, vehicles need a medium (dedicated

channels) in which they can communicate, and the

technology to do so.

Vehicle to Vehicle (V2V)

Technology

Policy

Dealing with Bandwidth Congestion

• Security overhead should be as low as

possible, this way more efficient data

packets can be used

• Low delay, especially for safety messages

• The data should be correct

• Non-repudiation: if an attack occurs, it

should always be possible to retrace the

attacker

• It is easy to make vehicles communicate, but

hard when hundreds of vehicles try to

disseminate information at the same time

and the same place

• The data aggregation should be highly

scalable, so that the communication

performance is not severely compromised

• The aggregation data solution should be

accurate enough when considering its

purpose (i.e. high accuracy for safety

applications)

• The proposed data aggregation solution

must be able to compare aggregated

data to each other to further increase

the efficiency of the solution

Safety and Security Scalability Accuracy and Efficiency

• There’s a limit to how much bandwidth can be used in the

air for a specific frequency

• Thousands of cars will require massive amounts of broadcast

data

• With just short messages (10 per second) of location and

vector, the current bandwidth is sufficient

• More advanced information sharing (point clouds) will

require new technology or mm length frequency space.

Problem

• Data aggregation is combining the same type of data

from difference sources and their packets into one

single packet, and then broadcasting it again. The

complicated part of this process is seeking out and

combining similar data into one succinct packet

• 5G Communication Protocols

• Full Duplex Communication is being worked on at

UT, where information is passed back and forth in

the same channel.

Solutions

Requirements

Establishing a Network

Wireless Access in Vehicular Environments (WAVE)

needs:

• Roadside Units (RSU)

• Designed to be installed on traffic

lights, signals, and other road

elements

• Onboard Units (OBU)

• Designed to be mounted on the

vehicles to guarantee connectivity

• Service Channels (SCH)

• Allow bidirectional vehicle-to-vehicle

(V2V) or vehicle-to-infrastructure

(V2I) connectivity

WAVE System

VANET: Vehicular Ad-Hoc

Network• Car networks that exchange high rate

multimedia information

• Serve as the foundation for connected vehicle

communication

Federal Mandates

National Highway Traffic Safety Administration (NHTSA) proposes a

rule in 2014 that will mandate V2V communication module on cars

built in a future year (expected mandate to begin phasing in by

2020 with full compliance on new vehicles by 2025. Additionally,

they will seek to implement V2I improvements along the same

time frame.

“NHTSA will…work to develop a

regulatory proposal that would require

V2V devices in new vehicles in a future

year, consistent with applicable legal

requirements, Executive Orders, and

guidance”

V2I Intersection

Deployment

Objective Number of

Sites

Year (projected)

20% High volume intersections, corresponding up to

50% of intersection crashes 62,200 2020

50% Deploy to cover 80% of intersection crashes 155,500

80% Blanket deployment where warranted 248,800 2040

The Roadside Unit: A Closer Look at CostsCurrent Implementations include DSCR communication, independently tied to signals and areas and various

supporting installation services:

Future implementations may include: • connections back to a Traffic Management Center

• LIDAR Installations for 360 degree point cloud mapping• GPS positioning correctional messages

• Further advanced communications

Deployment Cost

• The average direct DRSC RSU equipment and installation

cost per site estimated to be $17,600

• The cost to upgrade backhaul to a DSRC RSU is estimated

to vary between $3,000 and $40,000 depending on an

agency’s existing investments, at an estimated national

average of $30,800

• The typical cost of signal controller upgrades for

interfacing with a DSRC RSU is estimated to be $3,200

• The annual operations and maintenance cost for a DSRC

RSU site is estimated to be $3,050

Federal Pilot Program Cost Calculator Available at:

https://co-pilot.noblis.org/CVP_CET/index.html

Federal Program OpportunitiesUnited States Department of Transportation, Intelligent Transportation Systems Joint Program Office

Connected Vehicles (CV) Pilot Deployment Program – 2015/2016

Next Grant Opportunity: Expected 2017

Wyoming DOT, I-80, a heavy freight corridor:Applications to be deployed include Road Weather Advisories and Warnings for

Motorists and Freight Carriers, Weather-Responsive Variable Speed Limit System, Freight-Specific Dynamic Travel Planning, Spot Weather Impact Warning, Situational

Awareness, and others as determined by the user needs of truck drivers, fleet managers in the corridor

New York City, a heavy pedestrian area:Applications to be deployed include Red Light Violation Warning, Pedestrian in Signalized

Crosswalk Warning, Vehicle Turning Right in Front, Mobile Accessible Pedestrian Signal System

(PED-SIG), and Freight-Specific Dynamic Travel Demand and Performance, to help reduce

congestion and control speeds, enhance intersection and pedestrian safety, and optimize truck

freight operations.

Tampa, urban driving:Some of the applications to be deployed include Curve Speed Warning, Intelligent Traffic Signal

System, Intersection Movement Assist, Mobile Accessible Pedestrian Signal, and Transit Signal

Priority.

Index

I. Autonomous Car

Technology

I. Sensors

I. Lidar

II. GPS

III. Cameras

IV. Radar

V. Other

II. Computer

Systems

III. Software

IV. Levels of

Automation

V. Semi

Autonomous

Features

II. Current Company

Formulas

I. Google

II. SMART

III. Mercedes

IV. Volvo

III. Others Matrix

IV. Conclusions

V. Vehicle &

Infrastructure

Connectivity

I. Applications

II. Results

III. Path to V2X

IV. Bandwidth

Congestion

V. Establishing a

network

VI. Federal

Mandates

VII. Roadside Units


Opportunities

VI. Near Term

Conclusions

I. Infrastructure

Response

II. Next Step –

Planning/Policy

Near-Term Conclusions

Autonomous technology, at a Level 4, will likely be commercially available in the United States by 2020, however, full market saturation will take 10-15 years (based on 17 million cars sold annually).Until then, level 2 and level 3 features will become more common and will start to drastically reduce roadway incidents.

Autonomous

Connected vehicle will also be rolled out along a similar timeline as the level 2 & 3 autonomous features – providing further safety assistance. NHTSA mandate on DSRC in new light vehicles is expected to start around 2020 as a phase-in plan, with completion around 2025.

Connected

Roadway incidents are going to begin declining, with an 80% reduction by 2040. Plans should incorporate costs of V2I implementation and the benefits from a reduction in crashes.

Conclusion

Infrastructure Response

Phase 1

Phase 2

Phase 3

• Maintain lights/landmarks visibility

• Install GPS Terrestrial Station Technology

• Install Federally suggested V2I technology, apply

to Federal Pilot Programs

• Install Lidar at intersections and dangerous areas

to produce high accuracy maps

• Install communications to TMC and update V2I

for transmission of high accuracy maps

• Upgrade V2I communication for individual

vehicle communications

• Data center/high power computing

• Autonomous Intersection Management

Next Steps – Task 2 NCTCOG Policy/Planning

Research

http://ntl.bts.gov/lib/55000/55700/55711/FHWA-JPO-16-246.pdf

• Investigate modifications to NCTCOG ITS strategic plan – “ITS strategic

plans serve as roadmaps for implementing ITS projects system-wide

over a period of time. An ITS strategic plan, developed by a State

transportation agency or an MPO, should give consideration to CV

infrastructure to address mobility needs. The ITS strategic plan can then

be used to initiate C/AV infrastructure deployments for a broad cross-

section of organizations.”

• Investigate policy and legal issues – “DOTs and other agencies

recognize liability concerns in managing transportation operations;

thus, they can use their expertise to help guide the process of officially

determining who or what entity owns the data transmitted between

vehicles by V2V technologies. In the event of a crash, officially

recognized practices make it easier to determine liability.”

• Develop NCTCOG Area Adoption Timelines - Agencies must think about

autonomous vehicles and their impact on operations. The concept of

operations for a network of fully automated vehicles will be significantly

different than that for routine operations, will likely be more complex,

and will impact planning for all modes that use roads. DOTs should note

that all rollouts of CV will be incremental due to resource scarcity at the

State level. In particular, CV/ITS projects require significant operations

and maintenance expenditures.

.

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and Automated Transportation Systems: and Automated Transportation Systems: and Automated Transportation Systems: and Automated Transportation Systems:

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Part 1: The State of CAV TechnologyJune 2016June 2016June 2016June 2016

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