Unknown Object Detection Clustering for Autonomous Driving Scenarios
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Solution Overview
Problem
Modern computer vision systems in autonomous vehicles face limitations in identifying objects under varying conditions such as time of day, weather, and road layout, leading to unknown object detection events, which can compromise safety.
Innovation Solution
A system that captures and clusters unknown object events using spatial-temporal parameters and extended features to determine operating scenarios where object detection failures occur, enabling the identification of conditions for mitigating these failures and improving object recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer vision systems are used to detect objects in autonomous vehicles, then object detection capability is improved, but detection reliability deteriorates under certain conditions (time of day, weather, road layout)
Solution Approach 1:
The system performs preliminary clustering of unknown object events into operating scenarios before actual detection failures occur. By pre-processing and organizing detection failure data into clustered scenarios, the system prepares mitigation strategies in advance, improving reliability when detection failures are encountered in real-time without compromising the original detection capability
Solution Approach 2:
The system implements a feedback mechanism where detection failure data is continuously collected, clustered into operating scenarios, and used to generate mitigation actions that are fed back to improve future detection. This closed-loop feedback allows the system to learn from past failures and continuously improve both detection reliability and the identification of unknown objects
2Adaptability or versatility
If the computer vision system attempts to identify all objects under all conditions, then detection coverage is improved, but system complexity increases
Solution Approach 1:
The system segments the complex problem of unknown object detection by dividing it into distinct operating scenarios through clustering. Each cluster represents a specific scenario (e.g., nighttime, rain, specific road layouts) with its own characteristics and mitigation strategies. This segmentation allows the system to handle diverse detection conditions through modular, scenario-specific approaches rather than a single complex unified system
Solution Approach 2:
The system changes parameters by organizing detection failure data into clustered operating scenarios with distinct characteristics. By transforming raw detection failure data into structured scenario clusters with specific parameters (environmental conditions, object types, detection patterns), the system simplifies the complexity while maintaining comprehensive coverage across different detection conditions
Data Source
AI summary
An approach is provided for an unknown moving object detection system. The approach, for instance, involves capturing a plurality of unknown object events indicating an unknown object detected by one or more computer vision systems. The approach also involves clustering the plurality of unknown object events into a plurality of clusters based on one or more clustering parameters. The approach further involves selecting at least one cluster of the plurality of clusters based on a selection criterion. The approach further involves determining at least one operating scenario for the one or more computer vision systems based on a combination of the one or more clustering parameters associated with the selected at least one cluster.