ROBOCAR CONTEXT

The surging wave of R&D into smarter and smarter road vehicles is proceeding within an official classification of levels of vehicle automation. However, it lacks a comparable categorization of the roads on which they are to operate.

Reducing accident cost and fatalities is
a driving force for robocars.

Asking the right questions is often the key to finding the right answer. How we think about something determines what kind of answers we get. Robocars are still an amorphous concept in a state of flux. Let’s think more about their context -- or more appropriately, their contexts.

Focus on Vehicles

The US Government’s National Highway and Transportation Safety Administration (NHTSA) in 2013 published a policy framework defining five levels of vehicle automation:

            0   None -- driver in full control

                1   Function Specific -- automated braking, cruise control, etc.

                2   Combined Functions -- two or more driving functions work synergistically, e.g. cruise control and lane                                         centering

                3   Limited Self-Driving -- driver cedes controls under certain conditions

                4   Full Self-Driving -- the robocar of our dreams and/or nightmares

This heuristic classification framework has become part of the vocabulary of engineers and commercial entrepreneurs working on the many public and private programs to design, prototype, test and implement robocars. It helps them communicate the technical subtleties that often escape casual participants in professional and public conversations. As such, it facilitates communication. It is a useful intellectual tool that helps a society’s progress.

Enter the Context

Today we hear about “smart cars in smart cities” still not knowing precisely what the second half of the term means. What is a smart city? In 2013 MIT’s Technology Review with electric vehicles in mind offered this:  “In a smart city, every electric vehicle must have access to a charging station within its driving range.”

Since then we have witnessed remarkable progress in robotic sensor and visioning systems. Public officials who deal with the public realm are joining conversations with individuals and corporations focused on individual vehicles. Cutting-edge thinkers are exploring visions of a smart city where electronic markers dot the urbanscape.

Robocars service a metro station need not travel across town,
go out to suburbs nor go cross country.

Vehicles then can do more than watch out for cars, kids and other obstacles on the running surfaces around them.  They inform themselves by using an array of local points that themselves can communicate back. For example, real-time data on congestion can alter trip itineraries.  Stationary data emitters can also inform congestion managers. These can be small-scale operations such as parking and circulation departments at large university and medical complexes. Alternatively they can be large-scale, sophisticated traffic control centers, such as the New York City region's Transcom, 

Two-Dimensional Robocaricity

The performance and safety of robocars in cities depend not just on the hardware and software within vehicles. They are likewise affected by the extent, complexity and intelligence of the paths, lanes, streets, arterials and interstate infrastructure over which they run. Robocars without roads make no sense.

Why haven’t NHTSA or others categorized the contexts in which robocars are to run? It’s one thing to operate robocars within a private campus, where traffic can be tamed or even eliminated on some segments of the campus and where security and maintenance resources are available. College campuses tend to be pretty not because of intellectual debates and high culture.

It’s a whole different undertaking to run driverless vehicles from Albany to Albuquerque, Boston to Boulder, or Syracuse to San Francisco.

A Matrix to Think Better

Trans.21 proposes the following categories of Robocar Context:

          

            0   Secured and managed campuses

                1   Supervised activity centers with public streets and multiple private owners

                2   Urban neighborhoods and residential or mixed use districts with firm boundaries (physical or policy) and                               populations of 5000 to 50,000 or more

                3   An entire city - central or suburban - but within the jurisdiction of a single legal entity - aka City Hall

                4   A metropolitan area with populations of 0.5-15 or so million residents

                5   Inter-regional - long-distance trips such as mentioned above, extending from sea to shining sea.

Combining these two dimensions, we get the following application matrix:

	Context Scale	0	1	2	3	4	5
Vehicle   Smarts
0
1			A
2
3		B
4						C

In the above robocar-context matrix, Cell A involves driven vehicles that automatically brake in a major activity center -- such as a large shopping district or an airport.

Cell B is a much smarter robocar in which the driver opts over to automation and can regain control operating  within a college campus.  Neither A nor B seems too daunting.

However the challenges in Cell C are significantly more complex and challenging. This is large driverless fleets operating throughout a metropolitan region.  The time for overcoming these problems doesn’t need to block serious discussion and implementations in A and B.