Vehicle Localization Using Sensor Fusion in Poor GPS Areas
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Solution Overview
Problem
Traditional transportation matching systems rely on GPS data, which is inaccurate in areas with poor connectivity and insufficient for autonomous vehicle navigation, leading to inaccuracies in vehicle location and path estimation.
Innovation Solution
The system utilizes additional sensor data from gyroscope, accelerometer, and barometer sensors to determine a vehicle's location and path, integrating this data with GPS data to enhance accuracy, especially in areas with poor GPS connectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS data is used to determine vehicle location and path, then the system is simple to operate, but the measurement precision is insufficient in areas with poor connectivity
Solution Approach 1:
The patent combines GPS data with sensor data from gyroscope, accelerometer, and barometer to determine vehicle location and path. This merging of multiple data sources resolves the contradiction by achieving high measurement precision through data fusion while managing device complexity through integrated sensor processing.
Solution Approach 2:
The system uses sensor data as an intermediary to bridge the gap in GPS accuracy. When GPS connectivity is poor, the sensor data (gyroscope for orientation, accelerometer for motion, barometer for elevation) serves as a mediator to maintain accurate vehicle location and path estimation.
2Reliability
If GPS data alone is used for path estimation, then the device complexity is low, but the reliability of location tracking deteriorates in challenging environments
Solution Approach 1:
The patent merges GPS data with multiple sensor data streams (gyroscope, accelerometer, barometer) to create a reliable location tracking system. This combination ensures high reliability in challenging environments by compensating for GPS weaknesses with complementary sensor information.
Solution Approach 2:
The system dynamically changes the weighting and usage of different parameters (GPS coordinates, gyroscope angles, accelerometer motion vectors, barometer elevation) based on environmental conditions. This parameter adjustment maintains reliability by relying more on sensor data when GPS is unreliable and vice versa.
3Measurement precision
If traditional GPS-based tracking is used, then the system is easy to operate, but the measurement precision of vehicle path is insufficient for autonomous navigation
Solution Approach 1:
The system performs self-service by automatically fusing GPS and sensor data without requiring manual intervention. The computing device autonomously processes multiple data streams, calculates vehicle location and path, and adjusts tracking based on environmental conditions, maintaining ease of operation while achieving high precision.
Solution Approach 2:
The patent combines multiple data sources (GPS coordinates, gyroscope orientation data, accelerometer motion data, barometer elevation data) to achieve high path estimation accuracy. This merging enables autonomous navigation precision while the system manages the complexity internally, preserving ease of operation for the user.
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 provides more accurate vehicle location and path estimation, improving navigation accuracy for both human-operated and autonomous vehicles in challenging environments.
Implementation Method 1
sensor data from a gyroscope of the mobile computing device
Implementation Method 2
sensor data from an accelerometer of the mobile computing device
Implementation Method 3
sensor data from a barometer of the mobile computing device
Data Source
AI summary
In one embodiment, a method includes: receiving historical data of a plurality of vehicles that traveled in an area, the historical data including a sequence of location data points and a sequence of motion-data points for each vehicle in the plurality of vehicles; determining, for each vehicle, a motion-data trace of a path traveled by that vehicle in the area based on the sequence of motion-data points associated with that vehicle; generating, for each vehicle, an estimated path traveled by that vehicle based on the sequence of location data points and the motion-data trace of the path associated with that vehicle; generating an average path traveled by the plurality of vehicles based on the estimated paths traveled by the plurality of vehicles; and providing the average path to a device associated with a driver of a subject vehicle traveling in the area to assist with navigation.