IEEE Consumer Electronics Magazine - October 2015 - 103

15 s, and it takes up to 40 s to stabilize vehicle control of driving
from automation [14]. When an autonomous vehicle encounters
an unexpected problem, it would have to assume continued
responsibility for the driving task for another 15-40 s to ensure a
safe handover. With full self-driving automation (i.e., NHTSA
Level 4 [15] or SAE Level 5 [21]), this problem increases. Drivers are not steering the car and, thus, are not foreseen to be in
practice enough, or even capable of reacting faster and better
than their vehicle's technology.
As a consequence, in most circumstances, it may be better
to entirely avoid handovers from the vehicle to a human driver. This also concurs with what Google is championing with
its new driverless car. It was originally released without a
steering wheel and pedals, and the current prototype is continuing this design [8]. If, for whatever reason, a human element is to be required in the loop, one possibility is that
external service providers, comparable to drone operators and
air traffic controllers, could be commissioned to allow for
surveillance and some kind of takeover in critical situations
caused by unreliable technology or, more critically, by manually driven vehicles coexisting in the road network. Precautions would be required to ensure that service providers
would not offload tasks to algorithms but only to highly
trained people, who would fully focus on the vehicles in their
charge. Thus, because of the possible costs, risks, and complications, there is motivation to develop systems that allocate functions with different levels of automation to drivers,
based on context, to keep drivers both in the loop [10] and
safe as well as to protect others on the road from fatalities.

PROGRESSION FROM INDIVIDUAL TO
SEMICONNECTED TO FULLY CONNECTED DRIVING
In the long history of driver-vehicle (interaction) research,
the focus has been mainly on improvements to the individual
experience, e.g., between one driver and his or her car [19].
Numerous driver information and assistance systems have
advanced issues with complexity requiring technical mediation to resolve. Presently, we have traffic that includes some
semiautomated driving in the form of intentional driving
(such as cruise control and adaptive cruise control, antiskid
breaking, etc.) or driving that is unintentional (such as when
people stop driving to do any one of many tasks that distract
them from the road). We are moving toward a future that
includes the broad proliferation of control and communication technologies that, when integrated into the car, will
expand driver knowledge (by increasing the amount of information presented to the driver) and eventually replace that
knowledge-and them. Just recently, cars have been transformed into communications devices [6], with the consequence that drivers are now prone to all of the issues
currently associated with people and communications (i.e.,
divided attention, fragmentation, and distraction), and they
must resolve these while in motion, often at high speeds. Better ways to reconcile the intra- and intervehicle heterogeneity
emerging from this dynamic are required to ensure safety for
everyone, both in and outside of vehicles.

Reconciling multiplexity in this
context is a challenge as the road
of the near, distant, and far
future will be driven by
heterogeneity and diversity
within and between vehicles.
With subscriber identity module cards or other forms of
mobile communication infrastructure integrated and permanently available in vehicles, the next logical step will be to
raise optimization issues to the macrolevel by streaming
local information relating to the driver (e.g., thoughts, intentions, expectations, and physiological state), vehicle (e.g.,
vehicle-specific data from an on-board diagnostics interface
and a controller area network bus), and infrastructure (e.g.,
road conditions, ambient temperature/humidity, and weather
conditions) to a cloud service. In such a setting, information
will be processed and feedback will be provided to the individual driver/vehicle units based on commonalities or similarities-e.g., all cars with the same destination or all drivers
traveling for the same reason (business, shopping, and vacation) or with the same experience level-to optimize global
parameters such as traffic throughput, driving economy, and
road safety. Furthermore, such a setting might be used to
improve quality aspects on the local driver-vehicle interaction level, such as driver convenience, driving experience,
ergonomics, user friendliness, and other factors. Connected
vehicles without any capabilities for (semi-)automated
behavior, either algorithmic or human free will (agency),
pose a severe safety concern. Information is exchanged
among multiple vehicles, and the interpretation of and reaction to this information leads to mutual interdependence
between all the vehicles in a sphere of interest. This could
easily result in information overload for individual drivers or
vehicle-centric automated agents due to different multiple
information sources evaluated concurrently [2] by different
vehicles and local (personal or vehicle-centric) goals competing within a global optimum. In such a situation, it is
even more challenging (and important) to develop new interface concepts for optimized human-system collaboration
[17] (e.g., by sorting out irrelevant information or making
self-directed decisions).
Given that we cannot expect any fully centralized system
to be effective and reliable in all cases, we also cannot
assume that we can associate any given discrete set of vehicles. In traffic, there are no discrete sets that are independent
of all other vehicles (other discrete sets). Instead, a projected
optimum set for each vehicle can be asserted, best executed
by the vehicle itself (or its driver in unautomated contexts).
But the optimum set for each vehicle will rarely be fully congruent with adjacent vehicles. Instead, we have (largely)
october 2015

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IEEE ConsumEr ElECtronICs magazInE

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