Global Positioning System Reference
In-Depth Information
Required Accuracy
Low(10-20 m) Medium (1-5 m) High (less than 1 m)
Application
Message Routing (VANET)
X
Data Dissemination
X
Map Localization
X
Coop. Cruise Control
X
Coop. Intersection Safety
X
Blind Crossing
X
Platooning
X
Collision Warning Sys.
X
Vision Enhancement
X
Automatic Parking
X
Road Pricing
X
Table 2. Applications Requirement for Location Estimates Boukerche et al. (2008).
the system as calls for effective use of resources to achieve the target Quality of Service. For
example, there are applications where accuracy can be traded for faster response time. On the
other hand, there are applications where response time is not as important as accuracy (offline
vehicle track mapping). There are also applications where both requirements, accuracy and
response time, can not be compromised for any other gain.
Indeed, task or goal driven localization is about effective allocating system resources and
planning of localization tasks such that the system mission is achieved with maximum
integrity possible. This strategy to performance is a key issue to the new trends of hybrid
localization systems. In order for this strategy to work it is imperative that the impact of the
environment is not ignored. Without modeling the impact of the environment on the system,
the system can not be guaranteed to achieve its target performance, and even worst as it may
falsely determine its task is accomplished. Thus, modeling the impact of the environmental
conditions on the system is a central issue to the following proposed framework.
9. Task-driven localization through integrity assessment and control
It is well understood that the reported techniques can estimate the location of vehicles
relatively accurately in some situations if they are given adequate time to perform the
task. However, they may not perform as well in other situations. The deficiencies of these
localization techniques are uncorrelated as they are expected to be of diverse phenomena,
and/or utilize different algorithmic paradigms. This motivates the development of systems
that can take advantage of this diversity to achieve a reliable and accurate performance.
In this section, a high level concept of a novel framework for fusing different localization
techniques is proposed, Figure 5. What distinguishes this framework from existing ones is
its ability to take in account the impact of the measurement conditions on the individual
techniques. Thus, it is able to optimize the fusion process so as to maximize the accuracy
and integrity of the localization estimates. The framework consists of three logical layers: (1)
Primary Localization layer which provides preliminary location estimates using the available
localization techniques; (2) Integrity Monitoring layer which computes the reliability of
the vehicle's location estimates produced by the Primary Localization layer- a process that
captures the impact of measurement conditions; and (3) Estimate Fusion and Management
layer which interacts with the application task to ensure that the task's expected localization
 
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