Traffic Light Relevancy Mapping for Data-Efficient Junction Navigation
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating due to the sheer volume of data from sensors and traditional mapping technologies, which can limit or adversely affect navigation, especially in handling traffic lights and intersections.
Innovation Solution
A system using cameras to generate a crowd-sourced map that includes traffic light relevancy mapping, utilizing a trained model to aggregate vehicle behavior data and update traffic light relevancy indicators for improved navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional mapping technology is used for autonomous vehicle navigation, then navigation coverage is provided, but the sheer volume of data needed to store and update the map poses daunting challenges
Solution Approach 1:
The patent extracts only the necessary traffic light relevancy information from the vast amount of available map data, rather than storing and processing all data. The system identifies and stores only the relevancy indicators for traffic lights at intersections, filtering out unnecessary data while maintaining navigation capability.
Solution Approach 2:
The patent segments the complex navigation problem into manageable components by creating a specialized data structure that separates traffic light relevancy information from general map data. This segmentation allows the system to handle traffic light-specific tasks independently without being overwhelmed by the entire dataset.
2Productivity
If cameras are used to capture environmental information for navigation, then real-time navigation decisions can be made, but the sheer quantity of data captured poses challenges for processing and storage
Solution Approach 1:
The system extracts only the traffic light relevancy information from the vast amount of image data captured by cameras. Instead of storing and processing all captured images and data, the system identifies and stores only the necessary relevancy indicators for traffic lights, significantly reducing the data volume while maintaining real-time navigation capability.
Solution Approach 2:
The system performs preliminary processing of camera data to identify and extract traffic light relevancy information before storing it in the map. This preliminary action filters and prepares the data in advance, reducing the amount of data that needs to be stored and processed in real-time during navigation.
3Measurement precision
If detailed map data is stored to improve navigation accuracy, then positioning precision is improved, but the data volume increases making the system less efficient
Solution Approach 1:
The patent applies local quality by storing detailed traffic light relevancy information only at specific locations (intersections) where it is needed for navigation decisions, rather than storing detailed data throughout the entire map. This localized approach maintains positioning precision at critical points while improving overall system efficiency by reducing unnecessary data storage.
Data Source
AI summary
Systems and methods are provided for generating a crowd-sourced map for use in vehicle navigation. In one implementation, a system may include at least one processor configured to receive drive information collected from vehicles that traversed a junction; aggregate the received drive information to determine positions of traffic lights and spline representations for drivable paths; input the determined positions and the spline representations to a trained model configured to generate a traffic light relevancy mapping indicating a traffic light relevancy for traffic light to drivable path pairs of the junction; input an observed vehicle behavior to the at least one trained model to generate an updated traffic light relevancy mapping; store in the crowd-sourced map the indicators of traffic light relevancy for the traffic light to drivable path pairs; and transmit the crowd-sourced map to a vehicle for use in navigating the road segment.