Perception anomaly detection for autonomous driving
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current autonomous driving perception systems struggle with inconsistent anomaly detection across various scenarios, often relying on disparate approaches that are difficult to diagnose and can lead to incorrect identifications, especially in unpredictable environments, and fail to handle lens, scene, and object anomalies effectively.
Innovation Solution
A unified framework for anomaly detection in autonomous driving systems that categorizes anomalies into lens, scene, and object types, using neural networks to identify and classify these anomalies, enabling precise capture and efficient computational handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a unified framework for anomaly detection is implemented, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
The unified framework segments anomaly detection into three distinct modules: lens anomaly detection, scene anomaly detection, and object anomaly detection. Each module processes specific types of anomalies independently, improving detection precision while managing complexity through functional segmentation. The lens anomaly module detects camera lens issues, the scene anomaly module detects environmental anomalies, and the object anomaly module detects unusual objects in the driving environment.
Solution Approach 2:
The framework implements a universal anomaly detection system that handles multiple types of anomalies (lens, scene, and object anomalies) through a single integrated architecture. This multi-functional approach improves measurement precision by providing comprehensive anomaly coverage while managing device complexity through a unified processing pipeline that reuses computational resources across different anomaly types.
2Adaptability or versatility
If disparate anomaly detection approaches are used, then adaptability to various scenarios is improved, but reliability and consistency deteriorate
Solution Approach 1:
The unified framework provides a universal anomaly detection architecture that maintains consistent processing methods across all anomaly types (lens, scene, and object anomalies). This universal approach ensures reliable and consistent anomaly detection across diverse driving scenarios while maintaining adaptability through scenario-specific detection modules that operate within the unified framework.
Solution Approach 2:
The framework incorporates feedback mechanisms where detection results from each anomaly module are integrated and cross-validated. The system uses feedback loops to adjust detection parameters and improve reliability by comparing results across different anomaly types, ensuring consistent and reliable anomaly detection across various driving scenarios.
3Reliability
If comprehensive anomaly detection is implemented, then safety is improved, but computational time and complexity increase
Solution Approach 1:
The framework segments comprehensive anomaly detection into three parallel processing modules (lens anomaly detection, scene anomaly detection, and object anomaly detection). This segmentation enables simultaneous processing of different anomaly types, improving overall detection speed while maintaining comprehensive safety monitoring. Each module operates independently and contributes to the final safety assessment, reducing total computational time.
Solution Approach 2:
The system implements partial anomaly detection by prioritizing critical anomaly types based on driving safety requirements. When resources are limited, the framework focuses computational resources on the most safety-critical anomaly detections while maintaining the capability to detect all anomaly types. This approach ensures adequate safety monitoring within computational time constraints.
Data Source
AI summary
A unified framework for detecting perception anomalies in autonomous driving systems is described. The perception anomaly detection framework takes an input image from a camera in or on a vehicle and identifies anomalies as belonging to one of three categories. Lens anomalies are associated with poor sensor conditions, such as water, dirt, or overexposure. Environment anomalies are associated with unfamiliar changes to an environment. Finally, object anomalies are associated with unknown objects. After perception anomalies are detected, the results are sent downstream to cause a behavior change of the vehicle.