Road Vector Fields Using Sparse Polynomial Maps for AV Navigation
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating due to the vast amount of data from sensors and traditional mapping technologies, which can limit navigation efficiency and accuracy.
Innovation Solution
The use of cameras to analyze images for identifying road topology features and implementing navigational actions, combined with a sparse map for autonomous vehicle navigation that stores polynomial representations of road trajectories and landmarks, reducing data storage and processing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mapping technology is used to navigate, then navigation completeness is improved, but data storage requirements and processing complexity increase significantly
Solution Approach 1:
The patent segments the road environment into discrete topology features (intersections, lanes, landmarks) represented by compact polynomial equations rather than storing complete map images. This segmentation allows the vehicle to process only relevant navigational elements, reducing data storage from gigabytes to kilobytes while maintaining navigation completeness.
Solution Approach 2:
Instead of storing and processing actual map images or complete spatial data, the patent creates simplified mathematical copies (polynomial representations) of road trajectories and features. These polynomial copies capture essential navigational information with minimal data, enabling efficient processing while preserving the functional equivalence of traditional mapping approaches.
2Measurement precision
If vast volumes of sensor data are collected and analyzed, then navigation accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent extracts only the essential navigational features from sensor data - specifically road topology elements like intersections, lanes, and landmarks - rather than processing complete sensor datasets. By taking out only the critical information needed for navigation decisions, the system achieves accurate positioning and routing while minimizing processing time and computational load.
Solution Approach 2:
The patent transforms complex sensor data and spatial relationships into simplified polynomial parameters that describe road trajectories and features. This parameter transformation converts large volumes of raw sensor information into compact mathematical representations, maintaining navigation accuracy while dramatically reducing the computational complexity and processing time required.
3Adaptability or versatility
If detailed map data is stored and updated continuously, then route planning capability is improved, but data transfer requirements and system complexity increase
Solution Approach 1:
The patent performs preliminary action by pre-processing sensor data to extract and store only essential road topology features in polynomial form during normal vehicle operation. This preliminary extraction creates a compact navigational database that enables efficient route planning without requiring continuous processing of large map datasets, thereby reducing system complexity while maintaining adaptability.
Solution Approach 2:
The patent replaces the traditional mechanical approach of storing and manipulating large map data structures with a mathematical substitution using polynomial representations. This substitution eliminates the need for complex data management systems while preserving route planning functionality, as polynomials can be efficiently stored, updated, and queried with minimal computational overhead.
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.