Detecting Changed Driving Conditions via Vehicle Parameter Clustering
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Solution Overview
Problem
Conventional methods for detecting and reporting changes in road conditions, such as rain, ice, potholes, and hazards, are slow and ineffective in adverse weather conditions, and sensors may fail to provide timely information.
Innovation Solution
A system that clusters vehicle operating parameter data using a k-means algorithm to identify reportable conditions by determining deviations from a baseline, allowing for real-time detection of changed driving conditions through vehicle-to-infrastructure communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional sensors are used to detect changed conditions, then detection capability is provided, but detection speed is slow and effectiveness deteriorates in adverse weather conditions
Solution Approach 1:
The patent uses vehicle operating parameters as an intermediary indicator to indirectly detect road conditions. Instead of relying on sensors that directly measure environmental conditions (which fail in adverse weather), the system monitors changes in vehicle behavior parameters such as acceleration, braking, and steering patterns that result from drivers reacting to changed conditions. This indirect detection method maintains reliability across all weather conditions while providing rapid detection through existing vehicle sensor networks.
2Loss of information
If conventional detection methods are used, then basic detection capability is provided, but response time is slow
Solution Approach 1:
The system continuously collects and analyzes vehicle operating parameters in real-time, performing preliminary detection and clustering analysis before conditions fully deteriorate or before multiple vehicles are affected. By maintaining continuous monitoring and using clustering algorithms to identify patterns early, the system provides advance warning of changing conditions, reducing both information loss and response time.
Solution Approach 2:
The patent implements a feedback mechanism where detected changes in vehicle operating parameters are immediately processed and used to identify reportable conditions. The clustering algorithm provides continuous feedback by comparing current parameter distributions against historical baselines, enabling rapid identification and reporting of changed conditions as they occur rather than detecting them after the fact.
3Area of stationary object
If sensors operate in adverse weather conditions, then detection coverage is provided, but sensor effectiveness deteriorates
Solution Approach 1:
The system replaces direct environmental sensing with indirect detection through vehicle operating parameters. By monitoring how vehicles respond to conditions (through acceleration, braking, steering patterns) rather than directly measuring the conditions themselves, the system maintains detection reliability in adverse weather while preserving broad coverage across all road segments where vehicles operate.
Data Source
AI summary
A system comprises a computer including a processor, and a memory. The memory stores instructions such that the processor is programmed to determine two or more clusters of vehicle operating parameter values from each of a plurality of vehicles at a location within a time. Determining the two or more clusters includes clustering data from the plurality of vehicles based on proximity to two or more respective means. The processor is further programmed to determine a reportable condition when a mean for a cluster representing a greatest number of vehicles varies from a baseline by more than a threshold.