Road Feature Binning for Fast Autonomous Vehicle Guidance
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Solution Overview
Problem
Traditional methods for 3D road geometry modeling and feature detection are resource-intensive and time-consuming, making them impractical for modern applications like autonomous vehicle navigation, and current feature detection methods can be unreliable, impacting autonomous driving.
Innovation Solution
A method that uses a binning strategy to consolidate features detected across multiple images or sensor readings by projecting them onto a ground plane, defining bins across the road segment, and laterally positioning them to consolidate lane lines and other features, guiding autonomous vehicles based on these consolidated features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods for 3D road geometry modeling and feature detection are used, then measurement precision and feature detection accuracy are improved, but resource consumption and time requirements increase significantly
Solution Approach 1:
The patent segments the road environment into discrete bins along the direction of travel, allowing parallel processing of different road segments. This segmentation enables the system to process large amounts of sensor data efficiently by dividing the complex 3D modeling task into smaller, manageable units that can be handled independently and concurrently.
Solution Approach 2:
The patent creates a simplified 2D representation (copy) of the 3D road environment by projecting sensor data onto a ground plane. This copied representation maintains the essential geometric features needed for navigation while reducing computational complexity, allowing fast processing without sacrificing critical measurement precision for lane detection and road geometry analysis.
2Manufacturing precision
If traditional 3D modeling methods are used, then manufacturing precision of road geometry models is improved, but device complexity and computational requirements worsen
Solution Approach 1:
The patent creates a simplified 2D representation (copy) of the 3D road environment by projecting sensor data onto a ground plane. This copied representation maintains the essential geometric features needed for navigation while reducing computational complexity, allowing fast processing without sacrificing critical measurement precision for lane detection and road geometry analysis.
Solution Approach 2:
The patent changes the dimensional parameters of the road geometry representation from 3D to 2D by projecting onto the ground plane. This parameter change reduces the complexity of geometric calculations while preserving the essential spatial relationships and features needed for autonomous navigation, effectively simplifying the system without losing critical accuracy.
3Productivity
If feature detection from image data is used, then productivity and processing speed are improved, but reliability and detection accuracy worsen
Solution Approach 1:
The patent merges data from multiple sensors (cameras, LIDAR, radar) to detect and validate road features. By combining information from different sensing modalities, the system achieves reliable feature detection at high speed, as each sensor type compensates for the weaknesses of others while maintaining fast processing throughput.
Solution Approach 2:
The patent implements feedback mechanisms where detected features are validated against multiple sensor readings and previous frame data. This feedback loop filters out erroneous detections and confirms reliable features, maintaining high detection reliability even at increased processing speeds required for real-time autonomous navigation.
Data Source
AI summary
A method is provided for generating a representation of an environment. Methods may include: determining location information of a vehicle including a road segment and a direction of travel; identifying features of the road segment based on sensor data from sensors carried by the vehicle; projecting the features of the road segment onto a ground plane of the road segment; defining bins across a width of the road segment; laterally positioning the defined bins relative to a determination of positions of the features of the road segment; consolidating detected features of each bin to define features of the road segment; and guiding an autonomous vehicle along the road segment based, at least in part, on the consolidated detected features of the road segment.