Vehicle Sensor Error Probability Calculation
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Solution Overview
Problem
Current digital map data in vehicles often becomes outdated due to infrequent updates, especially in rural areas, leading to incorrect information being captured and transmitted by sensors, which can result in faulty data being used for navigation and predictive vehicle control systems.
Innovation Solution
A system that calculates an error probability for sensor data records using a central computer and reference database, where sensor data is classified, and reference data is used to assess its reliability, allowing for the identification and elimination of incorrect data before it is used in digital map applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital map data are updated using specially equipped vehicles in certain time cycles, then map data can be captured and updated, but the map data become outdated quickly, especially in rural areas with fewer measurement trips
Solution Approach 1:
The system uses periodic sensor measurements from vehicles passing through specific locations to continuously update map data. Instead of relying on scheduled updates by specially equipped vehicles, the system leverages periodic passings of regular vehicles to capture and transmit sensor data for map updates, thereby improving timeliness while maintaining accuracy through multiple observation opportunities.
2Loss of time
If sensor data is used to update digital map data in real-time, then map data recentness is improved, but incorrect sensor data can be captured and transmitted, leading to data quality issues
Solution Approach 1:
The system implements a feedback mechanism where sensor data from multiple vehicles is collected and evaluated against existing map data and sensor data from other vehicles. This cross-validation feedback loop allows the system to identify and eliminate incorrect sensor data while maintaining real-time update capabilities, thereby resolving the contradiction between timeliness and accuracy.
Solution Approach 2:
The system merges sensor data from multiple vehicles passing through the same location with existing digital map data. By combining multiple independent sensor measurements and comparing them against established reference data, the system can identify outliers and incorrect data while maintaining up-to-date map information, thus achieving both timeliness and reliability.
3Loss of information
If multiple sensors are used to capture environment data, then data comprehensiveness is improved, but the complexity of data processing and error identification increases
Solution Approach 1:
The system uses a universal evaluation approach that works across multiple sensor types (cameras, LIDAR, radar). The same data processing and validation algorithms are applied regardless of sensor type, allowing the system to handle diverse sensor data comprehensively while keeping the processing framework relatively simple and reusable across different sensing modalities.
Data Source
AI summary
Various embodiments include a system for calculating an error probability of a sensor data record in vehicles, the system comprising: a sensor unit with sensors in a vehicle, the sensor unit configured to provide a sensor data record for an object in an environment of the vehicle; a central computer receiving the sensor data record from the sensor unit; and a reference database storing reference data associated with a position of the vehicle noted at when the sensor data record was generated. The central computer is programmed to refer to reference data in the calculation of an error probability of the sensor data record.