Mercedes-Benz Group AG (20240208530). EVALUATING INTEGRITY OF VEHICLE POSE ESTIMATES VIA SEMANTIC LABELS simplified abstract
Contents
EVALUATING INTEGRITY OF VEHICLE POSE ESTIMATES VIA SEMANTIC LABELS
Organization Name
Inventor(s)
Christopher Monaco of Sunnyvale CA (US)
EVALUATING INTEGRITY OF VEHICLE POSE ESTIMATES VIA SEMANTIC LABELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240208530 titled 'EVALUATING INTEGRITY OF VEHICLE POSE ESTIMATES VIA SEMANTIC LABELS
The subject matter of this patent application relates to systems, devices, and processes for monitoring and evaluating vehicle pose integrity for use with an autonomous driving controller.
- Memory and processor system for accessing sensor measurements generated by vehicle sensors
- Observation of semantic classes to physical entities based on sensor measurements
- Extraction of semantically-labeled map parameters from an electronic map
- Determination of correlation between observed semantic classes and expected classes from map parameters
Potential Applications: - Autonomous driving systems - Vehicle safety and navigation systems - Fleet management solutions
Problems Solved: - Ensuring accurate vehicle pose integrity in autonomous driving scenarios - Enhancing the reliability of sensor data for navigation and control
Benefits: - Improved safety and efficiency in autonomous driving - Enhanced decision-making capabilities for vehicles - Better overall performance of autonomous driving systems
Commercial Applications: Title: "Advanced Vehicle Pose Integrity Monitoring System" This technology can be utilized in the development of autonomous vehicles, transportation services, and logistics companies to enhance the accuracy and reliability of vehicle navigation and control systems.
Questions about the technology: 1. How does this system improve the safety of autonomous driving vehicles? - This system enhances safety by ensuring accurate vehicle pose integrity, which is crucial for making informed decisions in autonomous driving scenarios. 2. What are the key benefits of correlating observed semantic classes with expected classes from map parameters? - By correlating these classes, the system can improve the accuracy of navigation and control systems, leading to more reliable autonomous driving capabilities.
Original Abstract Submitted
subject matter disclosed herein may relate to systems, devices and/or processes for monitoring and/or evaluating vehicle pose integrity for use with an autonomous driving controller. a system may include memory, the memory comprising one or more memory devices and a processor coupled to the one or more memory devices, the processor configured to access, from the one or more memory devices, sensor measurements generated by one or more sensors mounted in a vehicle. the system may additionally observe one or more semantic classes to one or more physical entities based on the generated sensor measurements and extract one or more semantically-labeled map parameters from an electronic map the system may additionally determine a correlation between the one or more observed semantic classes and one or more expected classes extracted from the semantically-labeled map parameters.
