Participative Road Friction Estimation via Vehicle Sensor Aggregation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current vehicle systems lack an effective method to collect and disseminate road friction data from multiple vehicles to improve safety by identifying and communicating road friction conditions in real-time, which is crucial for preventing accidents due to slippery or hazardous road surfaces.
Innovation Solution
A vehicular participative sensing system that collects road friction data from multiple vehicles, processes it centrally, and sends advisories to vehicles based on their location and heading, using sensors and telematics systems to estimate and classify road friction conditions, enabling driver warnings and automated responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If road friction data is collected from multiple vehicles and processed centrally, then road safety is improved through accurate friction condition identification, but system complexity increases due to data collection, aggregation, and communication infrastructure
Solution Approach 1:
The system divides the road friction detection task across multiple independent vehicles, each equipped with sensors and processing capabilities. Each vehicle independently estimates road friction using its own sensors (accelerometers, gyroscopes, wheel speed sensors) and vehicle dynamics models, then contributes its estimates to the central server. This segmentation allows the system to achieve comprehensive road coverage without requiring a single complex centralized detection system.
Solution Approach 2:
A central server acts as an intermediary to aggregate friction estimates from multiple vehicles, filter the data to identify consistent friction conditions, and distribute advisories back to vehicles. The server reconciles conflicting estimates from different vehicles and generates authoritative road friction condition notifications, mediating between distributed sensors and end-use applications.
2Loss of time
If real-time friction data is communicated to all vehicles, then response time is improved for safety warnings, but data transmission requirements and communication bandwidth increase
Solution Approach 1:
The system extracts only the essential friction condition information (friction coefficient estimates, location data, timestamp) from the full sensor datasets of participating vehicles. Instead of transmitting raw sensor data from all vehicles, each vehicle contributes processed friction estimates, and the central server distributes only relevant advisory notifications to vehicles based on their location and the identified friction conditions.
Solution Approach 2:
Vehicles perform preliminary processing of their sensor data to generate friction estimates before transmission to the central server. The server pre-filters and aggregates these estimates to identify consistent friction conditions before generating advisories. This preliminary action at multiple stages reduces the volume of data that needs to be transmitted and processed in real-time.
3Measurement precision
If friction estimates from multiple vehicles are aggregated and filtered, then measurement precision of road friction conditions is improved, but data processing time and computational requirements increase
Solution Approach 1:
The central server aggregates friction estimates from multiple vehicles but applies filtering that considers only a subset of estimates needed to achieve confident friction condition identification. The system filters for consistent estimates from vehicles in similar locations and conditions, rather than exhaustively processing all possible combinations. This partial action achieves sufficient precision without exhaustive computation.
Solution Approach 2:
Each participating vehicle independently performs initial friction estimation using its own sensors and vehicle dynamics models before contributing to the central aggregation. This self-service approach distributes computational work across many vehicles, reducing the processing burden on the central server while maintaining measurement precision through diverse independent measurements.
Data Source
AI summary
Methods and systems are disclosed for participative sensing of road friction conditions by vehicles, collection of the friction data from a large number of vehicles by a central server, processing the data to classify friction conditions by roadway and locale, and sending notifications of the friction conditions to vehicles as appropriate. A large number of vehicles use participative sensing systems to identify road friction estimates which are reported to the central server—where the vehicles use sensor data and vehicle dynamic conditions to estimate friction. The central server stores and aggregates the friction data, filters it and ages it. Vehicles requesting advisories from the central server will receive notices of road friction conditions which may be significant based on their location and heading. Driver warnings can be issued for low friction conditions ahead, and automated vehicle systems may also respond to the notices.