Edge Driver Data Tracking for Secure Fleet Association
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Solution Overview
Problem
Current fleet management systems rely on manual processes and cloud-based infrastructure for associating drivers with vehicles, leading to inefficiencies and increased costs due to the need for frequent data transmission and storage.
Innovation Solution
The implementation of a system that uses non-transitory computer-readable media to execute instructions on user devices and servers, enabling secure and automated tracking of historic driver data on vehicles by authenticating user devices, waking telematic units, and transmitting data securely, thereby reducing manual processing and cloud storage needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud-based infrastructure is used for driver-vehicle association and data storage, then driver identification and data analysis can be achieved, but data transmission costs and cloud storage costs increase significantly
Solution Approach 1:
The system pre-establishes driver-vehicle association mappings in the back-end system before actual driving sessions. Telematic devices pre-sync necessary driver identification data and vehicle information during idle periods, so that when a driving session starts, the association is already confirmed and minimal real-time communication is needed.
Solution Approach 2:
The patent extracts and stores essential driver identification data (driver profiles, vehicle associations, authentication credentials) locally in the telematic device's onboard storage. This extraction eliminates the need to continuously query the cloud for basic identification information, reducing data transmission requirements while maintaining accurate driver-vehicle association.
2Device complexity
If manual processes are used for driver-vehicle association, then system complexity is reduced, but processing time and human intervention requirements increase
Solution Approach 1:
The telematic device automatically performs driver identification and vehicle association without human intervention. The system self-syncs data with the back-end, self-authenticates drivers using stored credentials, and self-manages the driver-vehicle mapping process. This automation eliminates manual processing while keeping the system architecture relatively simple.
Solution Approach 2:
Driver profiles, vehicle information, and association rules are pre-configured and stored in the back-end system and telematic device before operational need. This preliminary setup enables rapid automated processing during actual driving sessions without requiring complex real-time decision-making or manual intervention.
3Loss of information
If all driver data is stored in the cloud, then data accessibility is improved, but security risks and access control complexity increase
Solution Approach 1:
The patent segments driver data into different categories with different security requirements. Sensitive personal information and authentication credentials are stored encrypted in the telematic device's onboard storage, while only essential operational data is synced to the cloud. This segmentation allows selective access control and reduces the security surface area.
Solution Approach 2:
The telematic device acts as an intermediary layer between the cloud back-end and the actual driver data. It caches and manages driver profiles locally, providing a buffer that allows the system to operate with minimal cloud connectivity while maintaining data accessibility. The intermediary enforces security policies and controls what data is exposed to the cloud.
Data Source
AI summary
Driver insurance and risk management scores are calculated per driver based on vehicle and driver behavior data collected during driving sessions. Often one vehicle is shared by multiple drivers and one driver can drive multiple vehicles. The present disclosure securely tracks the individual drivers, stores, and retrieves associated driver data for analysis on the edge (vehicle) in near real time. Data collected is analyzed at various time intervals (each trip, daily, monthly) to produce the scores. The goal of the proposed solution minimizes cost associated with data transmission and cloud storage, tracks long term driver driving history on the edge for near real time analysis of driver behavior, minimizes driver distraction due to user device while driving, securely stores and retrieves driver driving data on the edge device associated with the driver, restricts access to the driver driving data, and restricts user device access to servers and telematic units.