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Travel Modeling in an Era of Connected Travel Modeling in an Era of Connected Travel Modeling in an Era of Connected Travel Modeling in an Era of Connected
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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
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
Capabilities/Limitations
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.
Capabilities/Limitations
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.
Capabilities/Limitations
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
Capabilities/Limitations
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
Capabilities/Limitations
$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
Capabilities/Limitations
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
Capabilities/Limitations
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
ea
se in
Ro
ad
way
Sa
fety
Incr
ea
se in
Ne
two
rk E
ffe
cts
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
VIII. Federal Program
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
VIII. Federal Program
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
VIII. Federal Program
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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Part 1: The State of CAV TechnologyJune 2016June 2016June 2016June 2016