Plane Estimation for Active Stop Sign Classification
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Solution Overview
Problem
Autonomous vehicles face challenges in determining whether to react to or ignore objects, such as stop signs, especially when they are temporary or inactive, due to difficulties in understanding context and distinguishing between active and inactive states, which can lead to unsafe maneuvers.
Innovation Solution
The system uses image processing and sensor data to detect objects, classify their status by analyzing the presence of a planar surface extending away from the object, and compares this information with detailed map data to determine if the object is temporary or permanent, thereby classifying its status as active or inactive, allowing the vehicle to appropriately react or ignore it.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous vehicle reacts to all detected objects as active, then safety is improved, but false positives lead to unnecessary stops and reduced efficiency
Solution Approach 1:
The system changes the parameter of object status classification by analyzing multiple features including planar surface detection, extension distance, and orientation. This allows the system to distinguish between active and inactive states, reducing false positives while maintaining safety for genuine obstacles
Solution Approach 2:
The system introduces an intermediary classification process that acts as a mediator between object detection and vehicle reaction. By classifying objects as active or inactive based on planar surface analysis, the system prevents direct reaction to false positives while maintaining appropriate responses to genuine threats
2Productivity
If the autonomous vehicle ignores all detected objects, then efficiency is improved, but safety is compromised by missing active stop signs
Solution Approach 1:
The system changes the parameter of object status from binary (detected/not detected) to multi-state (active/inactive) by analyzing planar surface extension. This enables the system to maintain high efficiency by ignoring inactive objects while ensuring safety through proper identification of active stop signs
Solution Approach 2:
The system replaces simple object detection with a more sophisticated classification mechanism that uses planar surface analysis. This substitution allows the system to automatically distinguish between active and inactive objects, maintaining safety without compromising efficiency
3Measurement precision
If the system uses detailed planar surface analysis to classify objects, then accuracy is improved, but computational complexity increases
Solution Approach 1:
The system segments the analysis process into distinct stages: detecting the object, identifying planar surfaces, measuring extension distance, and classifying status. This segmentation allows the complex task to be broken into manageable computational steps, improving accuracy while controlling complexity
Solution Approach 2:
The system performs preliminary detection of objects and their associated planar surfaces before conducting detailed classification analysis. This preliminary action allows the system to prepare data structures and identify candidate objects, reducing the computational burden during the actual classification phase
Data Source
AI summary
Aspects of the disclosure relate to classifying the status of objects. For examples, one or more computing devices detect an object from an image of a vehicle's environment. The object is associated with a location. The one or more computing devices receive data corresponding to the surfaces of objects in the vehicle's environment and identifying data within a region around the location of the object. The one or more computing devices also determine whether the data within the region corresponds to a planar surface extending away from an edge of the object. Based on this determination, the one or more computing devices classify the status of the object.