Driver Profiling via Distributed GPS Feature Computation
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Solution Overview
Problem
Conventional driver profiling systems rely on multiple sensors, making them computationally intensive and time-consuming, and also raise privacy concerns and logistical challenges, limiting their scalability and effectiveness.
Innovation Solution
A system and method that utilize GPS data to compute driver profiles by distributing computation between a local device and a cloud server, reducing the need for multiple sensors and enhancing computational efficiency, while maintaining privacy by relying solely on logged GPS data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are used for driver behavior detection, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the essential GPS location data from the vehicle system, eliminating the need for multiple complex sensors. By focusing solely on location tracking and deriving all behavioral metrics from this single data source, the system achieves driver behavior detection with minimal hardware complexity.
Solution Approach 2:
The GPS device is made multi-functional by using it not just for location tracking but also for detecting driver behavior patterns, vehicle usage patterns, and generating profiles. This universal approach allows one simple device to replace multiple specialized sensors.
2Reliability
If multiple sensors are deployed for comprehensive driver monitoring, then reliability is improved, but ease of manufacture deteriorates
Solution Approach 1:
The invention extracts driver behavior information solely from GPS location data, removing the need to manufacture and deploy multiple sensor systems. This extraction approach maintains profiling reliability by focusing on location-based behavioral patterns while dramatically simplifying manufacturing and deployment.
3Speed
If computational processing is performed locally for real-time driver behavior analysis, then speed is improved, but use of energy increases
Solution Approach 1:
The computational workload is segmented between the local GPS device and remote servers. The local device performs only lightweight tasks of collecting GPS data and transmitting it, while complex profile generation and analysis are performed remotely. This segmentation maintains real-time responsiveness while minimizing local energy consumption.
Solution Approach 2:
A communication network acts as an intermediary between the GPS device and processing servers. This allows the local device to offload computational tasks without requiring direct local processing power, reducing energy usage while maintaining analysis speed through efficient data transmission.
Data Source
AI summary
This disclosure relates generally to driver profiling, and more particularly to system and method for driver profiling corresponding to automobile trip. In one embodiment, the method includes selectively computing, based on GPS data captured during a trip, features such that a first set of features are computed at a computation device and a second set of features are computed at a cloud server. The first and second set of features differs in computational complexity level. Said features include statistical data for attributes computed from the GPS data. The features corresponding to the trip are stored at repository associated with the cloud server. The repository includes previously precomputed features data associated with a set of driver profiles for previously completed trips. A driver profile corresponding to the trip is identified from amongst the set of driver profiles based on comparison of the plurality of features and the previously computed features data.