Road Vector Fields Using Sparse Maps for Autonomous Navigation
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating efficiently due to the vast amounts of data required for processing and storing road information, which can limit their navigation capabilities and increase data storage and transfer demands.
Innovation Solution
The implementation of a system using cameras to analyze images and identify road topology features, determining an estimated path, and implementing navigational actions, along with the use of sparse maps that store polynomial representations of road features and landmarks, reducing the need for extensive data storage and transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mapping technology is used to navigate autonomous vehicles, then navigation accuracy is maintained, but data storage and transfer demands increase significantly
Solution Approach 1:
The patent extracts only the essential navigational elements from complete map data by using polynomial representations to describe road features (lanes, curbs, intersections) and landmarks. Instead of storing and processing entire map datasets, the system extracts minimal sufficient data - polynomial coefficients that define road geometry and simplified landmark representations - thereby reducing data storage and transfer demands while maintaining navigation accuracy.
Solution Approach 2:
The patent transforms complex road feature data into simplified polynomial parameter representations. Road features are described using polynomial equations (e.g., quadratic or cubic polynomials) where only a few coefficients need to be stored and transmitted. This parameter transformation converts detailed geometric data into compact mathematical representations, significantly reducing data quantity while preserving the essential navigational information needed for accurate vehicle guidance.
2Loss of information
If vast volumes of road data are processed and stored, then comprehensive navigation information is available, but processing complexity and computational burden increase
Solution Approach 1:
The system extracts only the critical navigational parameters from comprehensive road data - specifically polynomial coefficients that define road geometry and essential landmark characteristics. By extracting only these minimal sufficient parameters, the system avoids processing the full volume of road data while maintaining complete navigational information needed for safe and accurate vehicle operation.
Solution Approach 2:
The patent uses simplified polynomial models as mathematical copies of complex road features. Instead of processing actual image data or detailed CAD representations of roads, the system creates compact polynomial representations that capture the essential geometric properties. These polynomial copies retain the necessary navigational information while being computationally efficient to process and store.
3Reliability
If detailed map data is stored and updated frequently, then current road conditions are accurately reflected, but data transfer and storage costs increase
Solution Approach 1:
The patent transforms detailed road feature data into compact polynomial parameters that require minimal storage space and bandwidth for transmission. By representing roads, curbs, and intersections as polynomial equations with a few coefficients, the system can frequently update road condition data without incurring prohibitive data transfer and storage costs, thereby maintaining accurate reflections of current road conditions.
Solution Approach 2:
The system applies partial action by updating only the essential polynomial parameters that define road geometry rather than transferring complete map datasets. This selective update approach ensures that current road conditions are accurately reflected while minimizing data transfer volume and associated costs, transferring only the minimal necessary information to maintain reliability.
Data Source
AI summary
Systems and methods are provided for vehicle navigation. In one implementation, at least one processor may receive, from a camera of a vehicle, at least one image captured from an environment of the vehicle. The processor may analyze the at least one image to identify a road topology feature in the environment of the vehicle represented in the at least one image and at least one point associated with the at least one image. Based on the identified road topology feature, the processor may determine an estimated path in the environment of the vehicle associated with the at least one point. The processor may further cause the vehicle to implement a navigational action based on the estimated path.