Camera-Based Vehicle Boundary Detection for Sparse Map Navigation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in navigating due to the vast amount of data they need to process and store, including image data, map data, and sensor data, which can limit their navigation capabilities and pose storage and update challenges.
Innovation Solution
The use of cameras to analyze images and determine vehicle boundaries, distances, and environmental features, along with the generation of sparse maps for navigation, allowing for efficient data storage and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mapping technology is used for autonomous vehicle navigation, then navigation capabilities are improved, but data storage requirements and update complexity increase significantly
Solution Approach 1:
The patent extracts only the essential navigational elements from complete maps, creating sparse maps that contain only critical information needed for navigation decisions. This selective extraction reduces data storage requirements while maintaining navigation reliability by focusing on key features such as road boundaries, intersections, and significant landmarks.
Solution Approach 2:
The patent segments the navigation data into hierarchical levels, with sparse maps providing high-level navigational context and detailed maps providing localized information only when needed. This segmentation allows the system to store minimal data permanently while loading detailed information temporarily during specific navigation tasks.
2Measurement precision
If complete environmental data is captured and stored for navigation decisions, then navigation accuracy is improved, but processing time and computational load increase
Solution Approach 1:
The patent applies partial action by capturing and processing only the portion of environmental data that is currently needed for navigation decisions. Rather than continuously processing all available sensor data, the system selectively processes data relevant to immediate navigational needs, reducing computational load while maintaining accuracy for critical decisions.
Solution Approach 2:
The patent performs preliminary processing of sensor data to identify and extract only the most relevant features before full navigation processing. This preliminary action filters out redundant information early in the processing pipeline, reducing the computational burden on subsequent navigation algorithms while preserving essential navigational information.
3Reliability
If multiple sensors and cameras are used to capture comprehensive environmental information, then navigation reliability is improved, but device complexity and energy consumption increase
Solution Approach 1:
The patent merges data from multiple sensors and cameras into a unified sparse map representation, consolidating redundant information and presenting a single integrated view of the environment. This merging reduces the complexity of processing separate sensor streams by combining them into a cohesive navigational model that maintains reliability through multi-sensor input.
Solution Approach 2:
The patent designs the sparse map data structure to serve multiple navigation functions simultaneously, including path planning, obstacle detection, and localization. This universal representation eliminates the need for separate processing pipelines for different navigation tasks, reducing overall system complexity while maintaining comprehensive navigation capabilities.
Data Source
AI summary
Systems and methods are provided for vehicle navigation. In one implementation, at least one processing device may receive, from a camera of the host vehicle, at least one captured image representative of an environment of the host vehicle. The processing device may analyze one or more pixels of the at least one captured image to determine whether the one or more pixels represent at least a portion of a target vehicle. For pixels determined to represent at least a portion of the target vehicle, the processing device may determine one or more estimated distance values from the one or more pixels to at least one edge of a face of the target vehicle; and generate, based on the analysis of the one or more pixels, including the determined one or more distance values associated with the one or more pixels, at least a portion of a boundary relative to the target vehicle.