Collective Perception Validation for Misreported Object Detection
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Solution Overview
Problem
Existing wireless communication systems face challenges in accurately identifying and addressing misbehaving wireless devices, which can lead to inaccurate object reporting and potentially endanger cooperative and automated driving decisions.
Innovation Solution
The system employs sensor sharing and collective perception mechanisms to validate object detection by receiving messages from wireless devices and determining whether they have misreported objects based on detected object data, allowing for the identification and reporting of misbehaving entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor data sharing is implemented to improve cooperative driving decisions, then the reliability of object detection is improved, but the system becomes vulnerable to misbehaving wireless devices that can report inaccurate information
Solution Approach 1:
The system implements a feedback mechanism where wireless devices send object detection messages to neighboring devices, which then validate the reported objects against their own sensor data and provide feedback on the accuracy of the reports. This creates a closed-loop system where detection accuracy is continuously verified and improved through inter-device communication and cross-validation.
Solution Approach 2:
The patent introduces an intermediary validation process where receiving devices act as mediators to verify the authenticity of object reports from transmitting devices. By comparing reported objects with independently detected objects, the system uses the receiving device as an intermediary to filter out misbehaving devices and ensure data reliability before incorporating the information into cooperative driving decisions.
2Measurement precision
If the system validates object detection by comparing sensor data from multiple devices, then the accuracy of identifying misbehaving devices is improved, but the device complexity increases
Solution Approach 1:
The system applies partial validation by focusing on verifying specific critical objects or detection scenarios rather than validating all sensor data from all devices equally. This selective approach maintains high detection accuracy for important objects while reducing the overall computational burden and system complexity by not performing exhaustive validation on every data point.
Solution Approach 2:
The validation process is segmented into distinct functional modules: object detection, message transmission, data comparison, misbehavior identification, and response generation. By dividing the complex validation task into these separate, manageable segments, each handled by specific system components, the patent reduces overall system complexity while maintaining comprehensive validation capability.
Data Source
AI summary
Systems and techniques are described for validating object detection. For example, an apparatus can receive a message from a wireless device. The message includes object data corresponding to one or more objects reported by the wireless device to be within a field-of-view of the apparatus. The apparatus can further determine, based on the object data, whether the wireless device has misreported at least one of the one or more objects.


