Road Vector Fields for Sparse-Map Vehicle Navigation
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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 identify location, avoid obstacles, and make decisions in real-time.
Innovation Solution
The implementation of a system using cameras to analyze images and determine road topology features, allowing the vehicle to calculate an estimated path and implement navigational actions, while also utilizing a sparse map for efficient data storage and navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mapping technology is used for autonomous vehicle navigation, then comprehensive map data can be provided, but the data storage requirements and processing complexity increase significantly
Solution Approach 1:
The patent extracts only the essential navigation elements from traditional comprehensive maps, creating a sparse map that contains only road topology features, lane markings, and critical navigation points. This extraction reduces data storage requirements while maintaining navigation accuracy by focusing on the most important spatial information needed for autonomous driving decisions.
Solution Approach 2:
The navigation system segments the environment into discrete road topology features and lane markings that can be independently identified and processed. By dividing the continuous map data into segmented geometric primitives (lines, curves, polygons), the system reduces computational complexity and storage requirements while enabling efficient real-time processing of navigation information.
2Productivity
If vast volumes of data are collected and processed in real-time, then navigation decisions can be made, but processing time and computational load increase
Solution Approach 1:
The system performs preliminary processing of visual data by pre-identifying and categorizing road topology features and lane markings before navigation decisions are required. By pre-processing the environment into structured geometric representations, the system reduces the computational burden during critical decision-making moments, enabling faster real-time responses.
Solution Approach 2:
The patent replaces traditional mechanical map-matching approaches with vision-based direct perception of road features. Instead of comparing sensor data against pre-stored comprehensive maps, the system directly identifies and interprets lane markings and road topology from visual input, significantly reducing processing time and enabling real-time navigation decisions.
3Measurement precision
If comprehensive map data is stored and updated continuously, then accurate navigation information is available, but system complexity and update requirements increase
Solution Approach 1:
The system implements local quality by creating sparse maps that provide high-detail navigation information only where needed (at road intersections, lane changes, and critical navigation points) while using simplified representations for less critical areas. This approach maintains location identification accuracy at decision-critical locations while reducing overall system complexity and data requirements.
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.