False Positive Slippery Road Report Detection Using Map Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Service providers face challenges in differentiating between true and false slippery road reports from vehicles, as loss of adhesion can be caused by factors other than road conditions, leading to potential false positives, especially with increasing numbers of reporting vehicles.
Innovation Solution
A computer-implemented method that uses mapping data to evaluate the proximity of slippery road reports to geographic features associated with driver behavior, such as intersections or areas where vehicles tend to brake or accelerate, to classify reports as false positives, incorporating weather data and historical driver behavior for accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If slippery road reports are based on sensor information indicating loss of adhesion, then the system can detect potential slippery road conditions, but false positive reports increase due to other factors causing loss of adhesion
Solution Approach 1:
The patent segments the slippery road detection process into multiple independent evaluation components: sensor data analysis, map data matching, geographic feature proximity evaluation, and driver behavior pattern recognition. Each component processes specific aspects of the data independently, then combines results to determine whether a report is genuine or false positive, thereby improving overall detection reliability while reducing false alarms
Solution Approach 2:
The patent introduces map data and geographic feature information as intermediary elements between the sensor detection and the final slippery road classification. These intermediaries provide contextual information about the location (e.g., proximity to intersections, curves, ramps) that helps distinguish between actual road conditions and driver behavior artifacts, effectively mediating the evaluation process to reduce false positives
2Reliability
If the system processes reports from thousands or millions of vehicles in real-time, then comprehensive road condition monitoring is achieved, but computational resources are excessively consumed
Solution Approach 1:
The patent applies partial action by selectively processing only those reports that meet specific criteria for further evaluation. Instead of analyzing every single report from millions of vehicles in full detail, the system first performs quick filtering based on geographic features and driver behavior patterns, then applies more computationally intensive analysis only to reports that are ambiguous or potentially genuine, thereby maintaining comprehensive coverage while significantly reducing overall computational resource consumption
Solution Approach 2:
The patent performs preliminary evaluation of reports using map data matching and geographic feature proximity assessment before committing to full analysis. By pre-processing reports with lighter computational requirements and identifying obvious false positives early in the pipeline, the system prepares data in advance for more intensive processing only when necessary, optimizing the allocation of computational resources across millions of vehicle reports
Data Source
AI summary
An approach is provided for detecting false positive slippery road reports. For example, the approach involves receiving a slippery road report from a vehicle. The slippery road report, for instance, indicates that a slippery road event is detected at a location based on sensor information collected by the vehicle. The approach also involves map matching the location of the slippery road report to the mapping data to evaluate a proximity of the location to at least one geographic feature that is designated as an area where driver behavior is expected to be at least one cause of the slippery road event. The approach further involves classifying the slippery road report as the slippery road false positive report based on the evaluation.