Route Reconstruction Using Sparse Location and Motion Data
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Solution Overview
Problem
Existing systems lack efficient methods to improve vehicle energy usage efficiency without continuous location and motion data collection, which drains battery life and is not accessible to third-party applications, and do not provide real-time recommendations for drivers to enhance fuel efficiency.
Innovation Solution
A user device collects sparse location and motion data at reduced frequency, reconstructs a route using dead reckoning techniques, and generates recommendations to improve driving efficiency by comparing actual and simulated energy usage, providing insights on reducing energy consumption through optimized driving behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous location and motion data collection is implemented to improve route reconstruction accuracy, then measurement precision is improved, but use of energy by moving object increases and battery life decreases
Solution Approach 1:
The system implements periodic data collection at reduced frequency instead of continuous collection. Location data is collected at specific time intervals or under specific conditions (e.g., when significant position changes occur), while motion data is collected periodically. This periodic approach maintains sufficient route reconstruction accuracy while dramatically reducing battery consumption compared to continuous data collection.
Solution Approach 2:
The system collects only the minimum necessary data required for acceptable route reconstruction. By using sparse location data combined with motion data from sensors, the system achieves sufficient accuracy without collecting excessive data points. This partial action approach collects just enough information to reconstruct routes adequately while minimizing energy usage.
2Use of energy by moving object
If sparse data collection is used to reduce battery consumption, then use of energy by moving object is reduced, but measurement precision deteriorates
Solution Approach 1:
The system introduces motion data from sensors as an intermediary to bridge the gap between sparse location data points. By collecting acceleration, orientation, and other motion parameters continuously or periodically, the system can interpolate and reconstruct the route between sparse GPS location points, maintaining measurement precision while using sparse location data to conserve battery.
Solution Approach 2:
The system replaces reliance on continuous GPS/mechanical location tracking with a combination of sparse location sampling and sensor-based motion tracking. Instead of continuously using power-intensive GPS to determine position, the system uses occasional GPS fixes combined with inertial sensor data to calculate position, substituting the mechanical GPS tracking system with a hybrid approach that reduces energy consumption while maintaining accuracy.
3Adaptability or versatility
If third-party applications access location data to provide driving efficiency recommendations, then adaptability or versatility is improved, but device complexity increases
Solution Approach 1:
The system enables third-party applications to independently collect and process their own required data using standard device sensors and APIs. Instead of a centralized complex system managing all data distribution, each application serves itself by directly accessing available sensors and processing data locally, reducing overall system complexity while maintaining versatility and broad application accessibility.
Data Source
AI summary
Techniques are described for improving driver efficiency. An example method can include a device accessing sparse location data indicative of one or more geographic locations along a route of the user device during a first time period. The route includes a starting location data point and an ending location data point. The device can access motion data collected by the sensors of the user device. The motion data can be collected by the sensors during the first time period. After a conclusion of the first time period, the device can generate, using the sparse location data and the motion data, a dense data set to reconstruct a route that includes the starting location data point and the ending location data point. The reconstructed route can include second dense location data and velocity data. The device can store the reconstructed route in a local memory of the user device.