Connected Vehicle Traction Alerts With Location-Based Targeting
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Solution Overview
Problem
Existing systems struggle to accurately determine and communicate driving conditions, particularly loss of traction events, in regions with reduced road friction, often sending irrelevant notifications to a wide audience and requiring manual generation.
Innovation Solution
A system utilizing networked vehicles to automatically detect loss of traction events, associate them with geographic locations, and send targeted notifications based on thresholds, including vehicle and environmental parameters, without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual generation and broad broadcasting of driving condition notices is used, then coverage of potential hazards is achieved, but notification relevance to specific vehicles deteriorates
Solution Approach 1:
The system segments the broad audience of vehicle notifications into specific groups based on their geographic locations and proximity to detected loss of traction events. Instead of broadcasting to all vehicles, notifications are targeted only to vehicles in relevant geographic areas, improving notification relevance while maintaining manageable system complexity through automated segmentation logic.
Solution Approach 2:
The system applies local quality by providing different notification treatments to different vehicles based on their specific locations relative to loss of traction events. Vehicles near detected events receive targeted notifications, while vehicles far from events do not receive notifications, creating location-specific notification quality that improves overall system relevance.
2Measurement precision
If automated detection and targeted notification system is implemented, then notification relevance to specific vehicles is improved, but system complexity increases
Solution Approach 1:
The system implements self-service by automatically detecting loss of traction events through vehicle sensor data, determining affected geographic areas, identifying vehicles in those areas, and sending targeted notifications without human intervention. This automation improves measurement precision and location accuracy while managing system complexity through self-executing algorithms.
Solution Approach 2:
The system uses feedback from vehicle sensors and loss of traction event detections to continuously refine notification targeting. By monitoring vehicle responses and event patterns, the system adjusts its detection and notification algorithms to improve location accuracy and reduce false positives, balancing precision gains with complexity management.
3Loss of information
If wide-ranging notices are broadcast to all vehicles, then potential hazard coverage is achieved, but information relevance to individual vehicles deteriorates
Solution Approach 1:
The system segments the vehicle population into those that need notifications and those that don't, based on their proximity to detected loss of traction events. This segmentation eliminates information loss by ensuring only relevant vehicles receive notifications, while improving notification efficiency by reducing the total number of notifications sent.
Solution Approach 2:
The system changes the parameter of notification distribution from universal broadcasting to selective targeting based on geographic location parameters. By adjusting the notification send decision based on vehicle location relative to events, the system preserves information relevance and improves efficiency simultaneously.
Data Source
AI summary
A method for determining driving conditions from network vehicles, includes receiving at a backend portion information relating to a loss of traction event, associating the loss of traction event with a location, comparing the information relating to the loss of traction event to one or more driving condition thresholds for the location, and sending a notification from the backend portion to multiple vehicles when one or more driving condition thresholds are met. The multiple vehicles may be limited to vehicles associated with the location, and this may include, for example, vehicles in a geographic location including the location or vehicles determined to be heading toward the location.