ADAS Usage Monitoring for Vehicle Risk Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing vehicle monitoring and control systems struggle to accurately determine whether advanced driving assistance systems (ADAS) are being used effectively, which hinders the assessment of their impact on vehicle safety.
Innovation Solution
A system that collects driving-related data from various sources, including ADAS components, mobile devices, and OEM servers, uses deep-learning models to generate ADAS-based target outputs, such as alerts or autonomous actions, based on vehicle operation behavior values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If ADAS features are made adjustable by drivers, then ease of operation is improved, but measurement precision of actual ADAS usage deteriorates
Solution Approach 1:
The system implements feedback by collecting actual ADAS usage data from vehicle sensors and telematics, comparing it against expected usage patterns, and providing this information to third parties (insurers, researchers) to enable accurate assessment of real ADAS effectiveness despite driver adjustability
Solution Approach 2:
The patent introduces an intermediary monitoring system that acts as a mediator between the driver-adjustable ADAS features and third-party assessors. This intermediary collects objective usage data through vehicle sensors, processors, and communication modules, translating subjective driver adjustments into measurable objective metrics
2Measurement precision
If comprehensive driving data is collected from multiple sources, then measurement precision of vehicle safety assessment is improved, but device complexity increases
Solution Approach 1:
The system applies universality by designing a multi-functional monitoring platform that consolidates data collection from diverse sources (vehicle sensors, mobile devices, telematics systems) into a single unified system. This platform performs multiple functions: data aggregation, processing, analysis, and dissemination to various third parties, thereby managing complexity through functional integration
Solution Approach 2:
The patent employs an intermediary data processing system that mediates between multiple complex data sources and the assessment requirements. This intermediary layer standardizes and harmonizes data from different formats and sources, simplifying the overall system architecture while maintaining comprehensive data collection capabilities
Data Source
AI summary
Implementations described herein provide systems, methods, and devices for vehicle monitoring and control based on advanced driving assistance system (ADAS) feature usage. The systems, methods, and devices include a driving data collection system for collecting driving-related data from an original equipment manufacturer (OEM) server and/or a mobile device. The OEM server receives the ADAS-related data from the vehicle and sends the ADAS-related data to the vehicle monitoring and control platform. Systems also include a vehicle/driver operation assessment system which uses one or more first deep-learning models to generate vehicle operation behavior values from the ADAS-related data. Furthermore the systems include a dynamic risk control model which uses one or more second deep-learning models to generate target outputs based on the vehicle operation behavior values. The target outputs include modification or control of the vehicle operations, one or more alerts, and/or a pricing variable.