Connected and Automated Vehicles: the Long and Winding Road Glenn N

• Historical Perspective • Yes, It’s Really Happening • What are the Impacts? • Where We are Headed

4 © 2016 Iteris, Inc. All rights reserved. Timeline to Automation • 1939 – NY World’s Fair Demo by GM • 1945 – First Cruise Control • 1950-60s – RCA and GM test automated highways (guide by wire) • 1960s-80s – Assorted tests and vehicle sensor tests, led by Japan / Europe

5 © 2016 Iteris, Inc. All rights reserved. March 6, 2018 Automated Highway System Demo (1997) • ISTEA (1991) authorized National Automated Highway System Consortium • Focus on safety, efficiency, throughput • 7.6 mile corridor in San Diego (I-15) • On-vehicle sensors tracked lane guidance markers, supported adaptive But…… cruise control, collision avoidance Clear outcome was not defined! • Limited testing of automated bus

• Main USDOT focus “Connected Vehicles” (vehicle-to- infrastructure, vehicle-to-vehicle communications) using 5.9 GHz DSRC • Research initiatives addressed AV’s for military and urban use (not using V2V or V2I)

o Carnegie-Mellon Navlab (1995)

o DARPA Challenge (2004, 2005, 2007) KAT-5 (2005) o Team behind 2005 winner developed Google (now Waymo) Gray Insurance (Metairie, LA) & Car Tulane University • High-definition mapping, real-time updates a core element of AV’s (and a link to concept of CV’s)

Connectivity

Autonomous driving Shared mobility

Electrification

How will these trends impact transportation operations?

• Mix of human-driven and driverless vehicles in foreseeable future, so traffic control devices will be needed • AV operations can increase lane capacity (less headway, narrower lanes possible) • Still-evolving liability considerations

Expected results – Increased person/vehicle capacity, reduction of crashes due to inattentive driving / DUI over time

© 2016 Iteris, Inc. All rights reserved. Impacts of connectivity • Advance intelligence on traffic control / road conditions • Richer vehicle data and probe info for operational analytics and decision support • Warning messages to supplement sensor data

Expected results – fewer crashes, more efficient traffic flow, more data for planning and operations

• Developing charging infrastructure • Assuring adequate charging spaces • Addressing stalled (discharged) vehicles • Re-use of gas/petrol stations (long-term) • Impacts on traffic flow dynamics

Expected results – Reduced fuel consumption and emissions, changes in

• Less personal vehicle ownership may reduce parking requirements • May either complement public transport (“last mile”) or compete • Potentially more circulating traffic • Increased shared bicycle use may drive future accessibility needs Expected results – less use of personal vehicles for shorter trips, changes to travel patterns, PT impacts © 2016 Iteris, Inc. All rights reserved. How Might CAVs Impact Louisiana?

14 © 2016 Iteris, Inc. All rights reserved. Potential Impacts of CAVs • Road infrastructure • Transportation systems management and operations (TSMO) • Transportation performance (safety, congestion, etc.) • Travel options (shared use vehicles, transit) • Land use • Legal / regulatory inforcement • Big data • What timeframe?

• Shared mobility will impact shorter/urban trips, less likely to impact family trips or rural travel • Potential freight efficiencies In short, • Other options (dynamic pricing, probably “no” transit) may continue to expand

• Use vehicle data to evaluate trends, adjust traffic, pricing, parking and demand management strategies • Future TMC roles may include monitoring / oversight of Level 4 and 5 driverless vehicles (NHTSA Automated Driving Systems 2.0) • Manage / coordinate vehicle, bike and pedestrian operations

0 1 2 3 4 5

Driver Partial Conditional High Full No Automation Assistance Automation Automation Automation Automation

• Driver has • Auto systems • Auto systems • Auto driving • Auto driving • Full-time complete of either of both system of all system of all performance control steering or steering & aspects with aspects even by an acceleration/ acceleration/ the expectation without the automated deceleration deceleration that the human human driver to driving system using driver will intervene of all aspects information perform when that can be form requested to managed by a surroundings intervene human driver

Human driver monitors the driving Automated driving system

Both driven and driverless vehicles will share the road for years to come

• Size of Potential Market - $83B by 2025 per Bloomberg (6/4/17) • Focus of automakers on getting products out first • Too many regulations are thought to introduce uncertainties/development path risk (Betamax anyone?)

© 2016 Iteris, Inc. All rights reserved. Influence on Federal Legislation • Private market seeks 100,000 car exemption from current NHTSA safety rules • House Bill (reflecting industry preferences) proposes:  NHTSA as lead regulatory body (states would not be allowed to make stricter rules)  Crash data becomes “Confidential Business Data”  Simplified review/approval process as opposed to FAA style • Most Important Concern (common among all parties): Maintaining Road Network in a State of Good Repair

© 2016 Iteris, Inc. All rights reserved. Current AV Initiatives and Deployments • “Market-Based” Examples:  Tesla Autopilot (Level 3+ operation)  Most automakers offer some combinations of cameras, sensors, adaptive cruise control, lane departure warning, automatic braking, blind-spot warning, parking assist, etc.  Real-time navigation using live traffic and dynamic rerouting logic

• Major Partnership Initiatives:  Shared autonomous vehicles

 PA, CA, AZ  Smart shuttles / fixed-route buses

 Las Vegas, Dubai, Minnesota  Vehicle testing

 Mcity (MI), Waymo, GM, Ford, others

• Automated Vehicles permit less public investment in additional technology infrastructure (other than electric vehicle charging stations) • Connected Vehicles provide additional intelligence not available with on-vehicle sensors (network and regional intelligence) • A “true” CAV is more responsive to travel conditions, including dynamic rerouting, collision avoidance and signal optimization