Autonomous Vehicle Navigation Using Sparse Maps and Image Regions
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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 navigation efficiency and accuracy.
Innovation Solution
A system that utilizes a camera and processing unit to analyze visual information, combined with sparse maps and a language model architecture, to identify objects and navigate based on image and map information, enabling efficient navigation with reduced data storage and processing demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional mapping technology is used for autonomous navigation, then navigation accuracy is improved, but data storage requirements and processing complexity increase significantly
Solution Approach 1:
The patent extracts only the essential navigational features from complete map data, creating sparse maps that contain only critical information needed for navigation decisions. This selective extraction reduces data storage requirements while maintaining navigation accuracy by focusing on key elements such as lane markings, intersections, and important landmarks rather than storing complete high-definition map data.
Solution Approach 2:
The patent segments the navigation system into multiple components: sparse map data for contextual navigation, sensor data for real-time environmental perception, and processing algorithms for integrating these data sources. This segmentation allows the system to use minimal map data combined with rich sensor data, reducing overall data storage requirements while maintaining or improving navigation accuracy through multi-source fusion.
2Reliability
If complete sensor data is processed for autonomous navigation, then navigation reliability is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent applies partial action by processing only the most relevant sensor data and sparse map information needed for current navigation decisions, rather than analyzing all available sensor data comprehensively. The system focuses computational resources on critical processing tasks such as obstacle detection, lane identification, and intersection navigation, achieving reliable navigation with reduced processing time by avoiding unnecessary analysis of non-critical data.
Solution Approach 2:
The patent performs preliminary processing of sensor data and sparse map information to pre-identify potential navigation issues, obstacles, and critical path elements before making navigation decisions. By pre-processing and filtering data to highlight only the most relevant information, the system reduces the computational burden during real-time decision-making, maintaining navigation reliability while decreasing processing time.
3Adaptability or versatility
If vast volumes of data are collected and analyzed, then navigation completeness is improved, but system complexity and computational demands increase
Solution Approach 1:
The patent implements multi-functionality by using a unified sparse map data structure that serves multiple navigation purposes simultaneously: path planning, obstacle avoidance, intersection navigation, and contextual awareness. This single versatile data structure replaces multiple specialized data sets, reducing system complexity while maintaining comprehensive navigation capabilities across diverse driving scenarios.
Data Source
AI summary
In one implementation, a method includes receiving at least one image captured by a camera of a host vehicle from an environment of the host vehicle; analyzing the at least one image to identify a region of interest; selecting a portion of the at least one image based on the region of interest; receiving map information associated with the environment of the host vehicle; providing the portion of the at least one image and the map information to a trained system; and receiving an output provided by the trained system. The output includes an identifier of an object in the environment of the host vehicle and location information for the object relative to the map information. The method further includes causing the host vehicle to initiate at least one navigational action based on the identifier of the object and the location information for the object.