Stop Sign Detection via Telemetry and Trip Stills Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing map data systems often rely on outdated or incomplete information, posing safety risks for vehicle navigation and autonomous driving applications by inaccurately representing geographic features such as stop signs and intersections.
Innovation Solution
A system that combines algorithmic models with data sources like ground truth mapping, vehicle telemetry, and trip stills data to accurately predict the presence of stop signs at intersections, using weighted inputs to generate confidence levels and update map data, and provides interactive graphical user interfaces for improved human-computer interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple data sources and algorithmic models are combined to detect stop signs, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the stop sign detection task into multiple specialized models: a telemetry model that analyzes vehicle speed and distance data, a trip stills model that processes camera images, and a combination logic module that integrates their outputs. Each model focuses on specific aspects of detection, improving overall accuracy while maintaining manageable complexity through functional decomposition.
Solution Approach 2:
The system merges outputs from multiple independent data sources (telemetry data, trip stills images, ground truth mapping data) through a combination logic module that applies weighted scoring. This integration consolidates information from diverse sources into a unified detection decision, achieving high accuracy without proportionally increasing system complexity.
2Measurement precision
If multiple data sources are integrated to update map data, then map data accuracy is improved, but data processing time increases
Solution Approach 1:
The system performs preliminary processing of telemetry data and trip stills images as they are collected, pre-computing features and extracting relevant information before the actual map data update is needed. This preliminary action reduces the computational burden during the final integration phase, maintaining high map data accuracy while reducing overall processing time.
Solution Approach 2:
The system implements feedback mechanisms where detection results are continuously refined based on ground truth mapping data and previous detection outcomes. This feedback loop allows the system to learn from past performance and optimize processing efficiency over time, maintaining accuracy while reducing redundant computations.
Data Source
AI summary
An improved system and method of determining presence of stop signs at intersection based on multiple types of data including ground truth data, telemetry data, and/or trip stills gathered from vehicle devices. The data may be analyzed and aggregated by one or more models and/or neural networks to develop a prediction regarding presence of stop signs at individual intersections.