Roadside Transmitter Localization for Weak-GPS Urban Intersections
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Solution Overview
Problem
In urban environments, the accuracy of vehicle localization is compromised due to weak GNSS/GPS signals, leading to position estimation errors.
Innovation Solution
The use of DSRC messages from roadside transmitters, which include the time of flight of DSRC signals, is integrated with vehicle position estimates from a signal propagation model to improve localization accuracy, utilizing asynchronous and synchronous time comparisons to calculate the vehicle's movement relative to fixed RST locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GNSS/GPS information is used for vehicle localization, then position estimation can be obtained, but accuracy deteriorates in urban environments due to weak signals
Solution Approach 1:
The patent introduces roadside transmitters (RSTs) as intermediary infrastructure elements that relay position information. These RSTs act as mediators between satellites and vehicles, providing localized reference signals that strengthen the localization capability in urban canyons where direct satellite signals are weak or blocked
Solution Approach 2:
The system combines multiple localization approaches (GNSS/GPS, DSRC communications, signal propagation modeling) into a unified framework. The Kalman filter integrates diverse data sources including satellite positions, RST messages, and vehicle dynamics to provide robust multi-functional localization that works across different environmental conditions
2Measurement precision
If DSRC messages from roadside transmitters are used, then localization accuracy improves, but system complexity increases due to asynchronous time synchronization requirements
Solution Approach 1:
The system employs feedback mechanisms where the Kalman filter continuously compares expected signal arrival times (based on vehicle position and RST locations) with actual arrival times from DSRC messages. This feedback loop enables the system to detect and compensate for time synchronization offsets, allowing accurate localization even with asynchronous clocks
Solution Approach 2:
The patent performs preliminary time offset estimation by comparing timestamps from RST messages with expected arrival times before final position calculation. This preliminary action of characterizing time synchronization errors enables subsequent corrections to be applied, simplifying the overall processing by handling time issues proactively rather than reactively
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances vehicle localization accuracy by combining DSRC time-of-flight measurements with vehicle dynamics and GPS information, reducing position estimation errors in urban settings without requiring a base station or differential GPS corrections.
Implementation Method 1
how long it takes for a DSRC signal from one or more fixed RSTs to reach the vehicle
Data Source
AI summary
New measurement inputs for Kalman Filter or similar estimation approaches (at each sample) may include: DSRC messages from roadside transmitters (RSTs), such as: how long it takes for a DSRC signal from one or more fixed RSTs to reach the vehicle and comparison of that information with vehicle position estimates from a signal propagation model, which is based on how long it takes a DSRC signal to reach the vehicle GPS location from a fixed known RST location. From such measurements, it can be determined how much longer (or shorter) it takes to receive the RST message compared to the previous sample, which, in turn, gives an idea how far the vehicle has moved over a sample relative to a fixed RST location.