1. Planning for Vehicle Automation
Jeremy Raw, P.E.
Office of Planning
Federal Highway Administration
October 27, 2016
AMPO Annual Meeting, Fort Worth, TX
October 27, 2016
Planning for Vehicle Automation

2. Planning for Vehicle Automation
Overview
Automation is a Disruptive Technology
Performance-Based Planning to the Rescue
New Roles for Scenarios and Models
A “big deal”, but not in the way you might immediately think
“Disruptive” of the planning process, that is
A good process beats bad assumptions every time
It would be mean to send you home without some practical suggestions
October 27, 2016
Planning for Vehicle Automation

3. Disruptive Technology
“Disruptive” = threat to “business as usual”
Automation could upend how transportation works
Most disruptive elements:
How or when will it happen?
What will the net effects be?
What to do now (or next)?
“Disruptive” means the end of “business as usual”. But what business are we in if it’s not usual? – we’ll return to that.
October 27, 2016
Planning for Vehicle Automation

4. What makes Automation Disruptive?
Behavior
Drive farther, buy fewer cars, use more taxis
Infrastructure
Better/different markings, less new road capacity, fewer parking spaces, “internet of cars”
Agency roles
Transit vs. shared mobility, “hands on” system management & operations
A short litany of things that AVs are anticipated to affect.
October 27, 2016
Planning for Vehicle Automation

5. What Else is Disruptive?
Self-driving cars are not the only thing
Shared Mobility (Uber/Lyft)
Freight Management (self-driving trucks, drones, 3D printing)
Bicycle and pedestrian travel (“Return to the City”)
Generational shifts (“Millenials hate driving”)
Externalities (new energy sources, climate change)
Common point: we don’t know whether any of these will occur or continue, or what the effects will be if they do.
October 27, 2016
Planning for Vehicle Automation

6. Deployment Status: Don’t Panic
Widespread AV deployment may be 30 years away
Automated Vehicles defined by hype and hope. We’re still all the way down at the left of the technology deployment curve.
There is no objective basis (yet) for determining how these technologies will perform in the real world, or what people will choose to do with them
Deployment curve example: Insurance Institute for Highway Safety; ESC has been required by NHTSA in 100% of new cars since 2012 (55% in 2009, 75% in 2010, 95% of 2011)
October 27, 2016
Planning for Vehicle Automation

7. Planning for Vehicle Automation
The Future…
… is Wide Open
More opportunity than ever to improve
… is Radically Uncertain
Know less than ever about what will happen
… calls for New Planning Strategies
Extrapolate less, experiment more, get more data
The rest of my talk is about the Planning Strategies
October 27, 2016
Planning for Vehicle Automation

8. Performance-Based Planning to the Rescue
October 27, 2016
Planning for Vehicle Automation

9. Handling the Unknown with Performance-Based Planning
Scenario Development and Visioning
Performance Measure Development
Performance Targets
Data Collection
Try Things and Evaluate Them
Revisit Assumptions Regularly
October 27, 2016
Planning for Vehicle Automation

10. Planning for Vehicle Automation
Where do we want to go?
Exit the era of “Manifest Destiny”
Certainty about interventions and outcomes
Consensus vision of what matters
Enter the era of “New Horizons”
Uncertainty about interventions and outcomes
Diverse visions of what might be possible
From “Determinism” to “Free Will”
October 27, 2016
Planning for Vehicle Automation

11. Planning for Vehicle Automation
Data Gathering More Helpful
Models More Helpful
This diagram is about how to respond to the things we don’t know.
Blue line is the “sweet spot” where we’re making the best type of response to varying amounts of available information.
October 27, 2016
Planning for Vehicle Automation

12. Planning for Vehicle Automation
Develop New Knowledge
Data Gathering More Helpful
Models More Helpful
Explore New Phenomena
The gray ovals indicate how we can effectively use models when we are “off the line”.
In these cases, models support the development of our understanding, not prediction of the future.
Side note if time: “how much the future is like the past” is conditioned by three things: (1) the nature of the phenomenon itself (ontology); (2) the state of our knowledge of the phenomenon (epistemology); and (3) how clear we are about the outcomes we want (teleology).
October 27, 2016
Planning for Vehicle Automation

13. Rethinking the Role of Scenarios
Old: Scenarios help decide what will happen
Identify factors everyone can agree on
Compute outcomes based on known relationships
New: Scenarios help decide what we want
Identify outcomes we hope for (or fear)
Identify indicators of where we’re headed
We often think of scenarios as helping us develop the right foundation for predicting the future.
Ithe
October 27, 2016
Planning for Vehicle Automation

14. Rethinking the Role of Models
Old: Models are predictive
Visualize outcome of well-understood interventions
New: Models are exploratory
Explore possibilities of hypothetical scenarios
Obsolete: Cannot use models to evaluate outcomes
October 27, 2016
Planning for Vehicle Automation

15. Imagine the Possibilities
Use planning cycles (TIP / LRP) to review
Scenario Planning, early and often
Focus on “where are we do we want to go?”
Use models sparingly to explore alternatives
Focus on Data Collection and Interpretation
What are the performance measures & targets?
Don’t neglect Visioning
Now more than ever, the future is ours to define
“Support the good, impede the bad”
October 27, 2016
Planning for Vehicle Automation