The new generation of maps.
Maps are cool. They
are beautiful, functional, informative and artistic. They are many different things to many
different people. While acknowledging
that, my interest here is in maps that can support automated vehicles and
transportation in general. In some ways
this is a limitation over previous maps, they lack the artistic beauty of many
different forms, and certainly the evocative “here be dragons” in the blank
spots. In other ways this is much larger
than most people’s conceptions of maps.
I retain the spatial components, that is everything is linked to a
latitude and longitude, but I do not limit this map to a purely physical
description of the world, rather it is a representation of how vehicles move
through the world and what they sense at each particular location and
pose. It contains a history of the
vehicles that have come before, and knowledge of the dynamic and transient
behaviors of vehicles all within an environment that a machine can understand. A different sort of aesthetic.
Probe based mapping.
Autonomous vehicles require maps based on probe data. This is partially because they include
content that can only be available from probes (i.e. content that cannot be
observed from a mapping van or aerial photograph) and also because probes are
the only way to get sufficient timeliness and accuracy from the mapping
system. Within the past few years there
has arisen a general consensus that map should be augmented with probe data in
order to get traffic information and the like.
In all cases these maps originally built from specialized collection
equipment. I believe this is a mistake
because building a map by this method requires the deployment of that
specialized collection equipment to every spot to be mapped. This is a very severe constraint, especially
if one is trying to map the minor roads in a rural region, which is often the
region were roads are not standard and there are large numbers of fatal
accidents, precisely the areas we want to address with map-based vehicle safety
applications. Furthermore, the single
pass of the collection vehicle is prone to errors. They can be sensor errors, human errors by the
driver of the collection asset or anywhere in the processing chain, or simply
random errors that conspire to introduce faults exceeding any tolerance
threshold.
These problems are overcome by the statistical nature of
probe based mapping. Many probes are
needed, and the data must be collected into a processing engine. These are the sorts of problems that
technology is overcoming in every field, mapping will be no different.
One of the major advantages of probe based mapping is that
the data is statistical in nature and therefore amenable to traditional error
analysis techniques. The error on any
measurement in a probe-based map can be quantified and provided with the data,
allowing the application designer to choose suitable data for their
process. In addition, any deviation from
normal can be quantified in terms of the probability of that deviation allowing
quantitative assessment of changes in the map.
The core of the map.
Existing maps rely on roads is a fundamental element. Lanes and other attributes are then derived
from the road centerline. For automated
driving applications the road is an abstraction, what really matters is the
lane. This should be the core element of
the map. A lane can be defined in terms
of a desired path for a large number of vehicles. Lanes have a centerline, they have
directionality, they may have a width and other attributes. Even far in the future, when road markings
may not be visible to humans, Helene will still be an important concept, that
is an agreed-upon path for vehicles with a common origin and destination. Given the proximity of vehicles and the
potential time to collision relative to any response time of the vehicle I
cannot imagine a situation where paths will not need to be defined in
agreed-upon among vehicles. The lanes may
be very short-lived and possibly quite dynamic, changing direction and width
based on the needs of local users, but without lanes, every vehicle is in a
free-for-all and free, efficient movement will not be possible given the number
of potential adverse interactions in a crowded environment.
In simulations of human movements through crowds lanes form
dynamically and can last a considerable amount of time[1]. With slow-moving, and relatively robust,
entities defining lanes on the fly is an acceptable approach, it is highly
unlikely to work in a vehicle environment.
It certainly may be the case that lanes are defined in some collective
manner and disseminated among the participants.
Even today, lane directionality is often determined by participants, say
on a one lane bridge. In the future,
lanes may exist only virtually, defined as needed for the vehicles in task at
hand, but the lane must be agreed upon by all of the participants.
A lane describes motivated movement with the expectation
that there are no conflicts with other vehicles in that lane. In general, one can make very accurate
predictions that if a vehicle is in a lane now, it will be in that same lane a
few seconds in the future. This is the
fundamental structure of the roadway and must be at the core of any representation
that is useful for automated vehicles.
A road then, is some collection of lanes. In general, cars can change between adjacent
lanes traveling in the same direction.
Lanes traveling in the opposite direction may be used for passing (a
short term change in the direction of travel).
Adjacent lanes are also generally subject to the same speed limit and
traffic controls (stop signs etc.), but there are certainly exceptions. In general I find that the definition of a
road is fairly ambiguous. A road
centerline may or may not correspond to a lane centerline, and may not even
correspond to a drivable path. These
problems are alleviated if the geometric entity is the lane and the road level
attributes merely describes the relationships between lanes, that is the
ability to transition from one lane to the other. The road may also provide a convenient entity
for the recording of certain attributes, such as speed limit, but fundamentally
these belong to a lane.
Another relevant concept is that of the surface. This, as the name implies, is the drivable
surface on which vehicles may move. A
surface may be subdivided into lanes which are the primary interest of
vehicles, and some of the surface may not be associated with a lane. Under normal conditions of vehicle would stay
within a lane however, there are many emergency situations where a vehicle must
move outside of the lanes in knowing the extent of the drivable surface is
useful for any application taking control in this situation.
for Crowd
Simulation”, IEEE Transactions on Visualization
and Computer Graphics (TVCG) 2014
