Traffic Light Navigation Using Worst-Time-to-Red Estimation
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating effectively due to the vast amounts of data they need to process and store, particularly with traditional mapping technologies, which can limit their ability to accurately interpret visual information and make decisions for safe navigation.
Innovation Solution
The use of multiple cameras to analyze images and determine traffic light states, with a processor comparing detection results to confirm the state and determine navigational actions for the vehicle, allowing for efficient data processing and reduced data storage needs through sparse map navigation models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional mapping technology is used for autonomous vehicle navigation, then comprehensive environmental information is available, but data storage requirements and processing complexity increase significantly
Solution Approach 1:
The patent extracts only the essential navigational elements (traffic lights, road markings, signage) from the complete environmental data set. Instead of storing and processing all possible map information, the system identifies and processes only those features directly relevant to navigation decisions, thereby reducing data volume while maintaining navigational functionality.
Solution Approach 2:
The system applies different levels of detail to different spatial regions. High-detail processing is applied only to the immediate vicinity of the vehicle where navigation decisions are critical, while peripheral areas receive lower-detail processing. This local differentiation reduces overall data storage requirements while preserving essential navigational information.
2Measurement precision
If multiple cameras are used to capture environmental images, then detection accuracy improves, but data processing load increases
Solution Approach 1:
The patent segments the processing workflow into distinct stages: initial detection by individual cameras, result comparison across multiple cameras, and final confirmation. This segmentation allows the system to leverage multiple cameras for improved accuracy while managing processing load through structured, modular operations rather than simultaneous full-image processing by all cameras.
Solution Approach 2:
The system performs partial processing by comparing only key detection results from multiple cameras rather than processing complete image data from all cameras equally. This selective comparison approach maintains detection accuracy by validating critical information across multiple sensors while avoiding the excessive processing burden of analyzing all image data in full detail.
3Measurement precision
If comprehensive map data is stored for navigation, then route planning accuracy improves, but system complexity and update requirements increase
Solution Approach 1:
The patent extracts only the critical navigational features needed for safe operation (traffic lights, stop signs, road boundaries) from complete map data sets. This extraction creates a simplified data structure that maintains navigation accuracy for essential decision-making while eliminating unnecessary geographical and demographic information that increases system complexity.
Data Source
AI summary
Systems and methods are provided for vehicle navigation. The systems and methods may detect traffic lights. For example, one or more traffic lights may be detected using detection-redundant camera detection paths, a fusion of information from a traffic light transmitter and one or more cameras, based on contrast enhancement for night images, and based on low resolution traffic light candidate identification followed by high resolution candidate analysis. Additionally, the systems and methods may navigation based on a worst time to red estimation.