![](https://csdnimg.cn/release/download_crawler_static/39612311/bg4.jpg)
4
(a) Case I: P L < AL along with examples of nominal, misleading,
and hazardous misleading information.
(b) Case II: AL < P L resulting in no availability.
Fig. 5. Relationship between protection level, alert limit, availability, and
different operations.
be developed in more detail throughout the text.
Accuracy is described by the typical performance of the
system, usually measured at the 95th percentile. This is a
manifestation of the desired statistical distribution of local-
ization errors, where alert limits usually define the maximum
allowable error for operation to the desired integrity level
which define the tails of the statistical distribution. Hence,
accuracy is a measure of nominal performance and integrity a
measure of the limits. This relationship is shown in Figure 4.
At present, there is no one localization technology that can
meet the requirements presented here for safe operation in all
weather, road, and traffic scenarios. Today, Ford Motor Com-
pany’s autonomous vehicle research platform makes use of a
complex sensor fusion strategy for localization and perception
as shown in Figure 7. This includes LiDAR, radar, cameras,
a Global Navigation Satellite System (GNSS) receiver, and
an Inertial Measurement Unit (IMU). Each of these are a
system in itself, and hence localization is a system of systems.
Since no one technology can achieve the requirements in
all scenarios, this will require multi-modal sensing and their
Fig. 6. The Stanford Diagram. This shows the relationship between actual
error, protection level, and alert limit.
Fig. 7. Example of Ford’s research autonomous vehicle platform sensors used
in localization and perception.
intelligent combination to achieve the integrity levels needed
for safe operation.
Though the sensor strategy shown in Figure 7 can meet
many of the requirements that will be developed here in a
research setting, the sensor costs are such that many challenges
lie ahead on the path to production. Decimeter accuracy
will require a trade-off between onboard sensors, compute
resources / storage, and supporting infrastructure such as high
definition maps and GNSS corrections. Such infrastructure
may also limit where self-driving features are enabled due to
availability of precise localization. Ultimately, precise vehicle
location is only useful in context and hence a-priori maps are a
major piece of the puzzle. We will limit the scope of this paper
to localization requirements as mapping is a vast and complex
area in its own right. We will touch on mapping requirements
as they relate to localization.