Vehicle Navigation Using Warped Camera Views and Sparse Maps
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating due to the vast amount of data they need to process and store, which can limit or adversely affect navigation, and traditional mapping technologies pose daunting challenges in updating and managing this data.
Innovation Solution
The use of cameras to generate warped images simulating a view from an elevated viewpoint, allowing for the identification of road features and determining navigational actions, and the generation of sparse maps for vehicle navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional mapping technology is used to navigate, then the vehicle can access map data, but the sheer volume of data needed to store and update the map poses daunting challenges
Solution Approach 1:
The patent extracts only the essential navigational features from the environment (lane markings, road signs, intersections, obstacles) rather than storing complete map data. This selective extraction reduces data volume while maintaining navigation capability, as the vehicle processes only relevant visual information captured by cameras in real-time.
Solution Approach 2:
The navigation system segments the environment into discrete navigational elements (lane markings, signs, intersections, obstacles) that can be independently identified and processed. This segmentation allows the system to handle navigation data in manageable units rather than processing entire map datasets.
2Reliability
If vast volumes of information are collected by sensors to enable navigation, then the vehicle can make informed decisions, but the sheer quantity of data to analyze, access, and store limits or adversely affects autonomous navigation
Solution Approach 1:
The system applies local quality by processing only the specific regions of interest in captured images (lane markings, signs, obstacles) rather than analyzing entire image datasets. The camera system focuses computational resources on identifying and processing local navigational features that directly impact decision-making.
Solution Approach 2:
The patent employs partial action by capturing and processing only the minimum necessary visual information required for safe navigation (key road features, obstacles, signage) rather than comprehensively analyzing all environmental data. This selective processing maintains safety while improving efficiency.
3Measurement precision
If cameras capture detailed images of the environment for navigation, then the vehicle can identify road features accurately, but processing and analyzing the captured image data requires significant computational resources
Solution Approach 1:
The system performs preliminary action by pre-identifying and prioritizing key navigational features (lane markings, signs, intersections) before full image processing. This allows the camera system to focus computational power on critical features rather than analyzing entire images in detail.
Solution Approach 2:
The patent applies dynamics by adaptively adjusting the level of image processing based on navigation context. When approaching intersections or detecting obstacles, the system intensifies processing of relevant image regions, while reducing processing during straightforward driving segments, thereby optimizing power consumption.
Data Source
AI summary
Systems and methods are provided for vehicle navigation. In one implementation, at least one processor may be programmed to receive, from a camera, a captured image representative of features in an environment of the vehicle. The processor may generate a warped image based on the received captured image, which may simulate a view of the features in the environment of the vehicle from a simulated viewpoint elevated relative to an actual position of the camera. The processor may further identify a road feature represented in the warped image, which may be transformed in one or more respects relative to a representation of the road feature in the captured image. The processor may then determine a navigational action for the vehicle based on the identified feature represented in the warped image and cause at least one actuator system of the vehicle to implement the determined navigational action.