Driver Rating System for Real-Time Fingerprinting via Cloud-Edge ML
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Solution Overview
Problem
Conventional machine learning systems for intelligent vehicles face challenges in providing real-time responses due to intensive computation requirements and limited knowledge of the vehicle's context and user driving skills, leading to suboptimal decision-making.
Innovation Solution
A driver rating system (DRS) utilizing a virtuous cycle between a cloud-based network, machine learning center, and containerized sensors, which includes outward and inward facing cameras and audio sensors to collect and analyze metadata for driver behavior, enabling real-time driver fingerprinting and adaptive assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained with intensive computation to improve driver behavior analysis accuracy, then measurement precision improves, but response time deteriorates
Solution Approach 1:
Driver behavior models are trained offline in advance using historical data from multiple vehicles. The pre-trained models are then deployed to individual vehicles for real-time inference, separating the computationally intensive training phase from the real-time analysis phase to achieve both high accuracy and fast response
Solution Approach 2:
A single pre-trained driver behavior model is universally applied across multiple vehicles and drivers. The model serves multiple functions including driver identification, behavior pattern recognition, and risk assessment, eliminating the need for separate models for each vehicle while maintaining analysis accuracy
2Device complexity
If traditional vehicles operate with limited context knowledge to simplify decision-making, then device complexity reduces, but reliability deteriorates
Solution Approach 1:
A cloud-based platform serves as an intermediary that aggregates context information from multiple sources including weather data, road conditions, traffic patterns, and historical driver behavior. This centralized intermediary processes and synthesizes complex context information, providing reliable decision support to individual vehicles without requiring each vehicle to independently manage complex context knowledge
Solution Approach 2:
The system continuously collects real-time driver behavior data and vehicle operating conditions, feeds this information back to the cloud platform, and uses it to refine and update driver behavior models. This feedback loop enables the system to adapt to changing conditions and improve decision-making reliability over time while maintaining manageable vehicle complexity
Data Source
AI summary
A method and/or system is able to provide driver fingerprint via metadata extraction managed by a driver rating (“DR”) model trained by a machine learning center (“MLC”) coupled to a cloud based network (“CBN”). In one embodiment, a DR system includes a set of outward facing cameras, a set of inward facing cameras, and a vehicle onboard computer (“VOC”). The set of outward facing cameras mounted on a vehicle is used to collect external images representing a surrounding environment in which the vehicle operates. The set of inward facing cameras mounted in the vehicle is used to collect internal images including operator body expression representing at least operator's attention. The VOC is configured to determine the identity of operator and current operating style in response to the collected internal images, the collected external images, and historical stored data.