View source for 20240013592. CRITICAL SCENARIO IDENTIFICATION FOR VERIFICATION AND VALIDATION OF VEHICLES simplified abstract (Siemens Industry Software NV)

=CRITICAL SCENARIO IDENTIFICATION FOR VERIFICATION AND VALIDATION OF VEHICLES=


==Organization Name==

[[:Category:Siemens Industry Software NV|Siemens Industry Software NV]]


[[Category:Siemens Industry Software NV]]

==Inventor(s)==

[[:Category:Saadhana B Venkataraman of Chennai, Tamil Nadu (IN)|Saadhana B Venkataraman of Chennai, Tamil Nadu (IN)]][[Category:Saadhana B Venkataraman of Chennai, Tamil Nadu (IN)]]

[[:Category:Vijaya Sarathi Indla of Bangalore, Karnataka (IN)|Vijaya Sarathi Indla of Bangalore, Karnataka (IN)]][[Category:Vijaya Sarathi Indla of Bangalore, Karnataka (IN)]]

[[:Category:Bony Mathew of Perumbavoor, Kerala (IN)|Bony Mathew of Perumbavoor, Kerala (IN)]][[Category:Bony Mathew of Perumbavoor, Kerala (IN)]]

[[:Category:Saikat Mukherjee of Bangalore, Karnataka (IN)|Saikat Mukherjee of Bangalore, Karnataka (IN)]][[Category:Saikat Mukherjee of Bangalore, Karnataka (IN)]]

[[:Category:Ram Padhy of Ganjam, Odish (IN)|Ram Padhy of Ganjam, Odish (IN)]][[Category:Ram Padhy of Ganjam, Odish (IN)]]

[[:Category:Sagar Pathrudkar of Pune, Maharashtra (IN)|Sagar Pathrudkar of Pune, Maharashtra (IN)]][[Category:Sagar Pathrudkar of Pune, Maharashtra (IN)]]

[[:Category:Bristi Singh of Bangalore, Karnataka (IN)|Bristi Singh of Bangalore, Karnataka (IN)]][[Category:Bristi Singh of Bangalore, Karnataka (IN)]]

==CRITICAL SCENARIO IDENTIFICATION FOR VERIFICATION AND VALIDATION OF VEHICLES - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240013592 titled 'CRITICAL SCENARIO IDENTIFICATION FOR VERIFICATION AND VALIDATION OF VEHICLES

==Simplified Explanation==

The patent application describes a system and method for identifying critical scenarios from vehicle data. Here is a simplified explanation of the abstract:

* The scenario identification system collects inertial measurement unit (IMU) data from vehicles.
* It derives driving parameters based on the IMU data.
* The system analyzes these driving parameters using predefined thresholds.
* The purpose is to identify one or more critical scenarios from the vehicle data.

Potential applications of this technology:

* Vehicle safety systems: The identified critical scenarios can be used to improve safety systems in vehicles, such as collision avoidance or emergency braking.
* Autonomous driving: The system can help autonomous vehicles identify and respond to critical scenarios on the road.
* Fleet management: Fleet operators can use this technology to monitor and analyze critical scenarios in their vehicles, allowing them to improve driver training and reduce accidents.

Problems solved by this technology:

* Identifying critical scenarios: The system solves the problem of efficiently and accurately identifying critical scenarios from vehicle data, which can be challenging without automated analysis.
* Improving safety: By identifying critical scenarios, this technology helps in improving vehicle safety and reducing accidents on the road.

Benefits of this technology:

* Enhanced safety: By identifying critical scenarios, vehicles can take appropriate actions to avoid accidents, potentially saving lives.
* Improved efficiency: Fleet operators can use the identified critical scenarios to optimize routes, reduce fuel consumption, and improve overall efficiency.
* Better decision-making: The analysis of critical scenarios can provide valuable insights for vehicle manufacturers, fleet operators, and policymakers to make informed decisions regarding vehicle design, infrastructure improvements, and road safety regulations.


==Original Abstract Submitted==

a scenario identification system and a computer implemented method for identifying one or more critical scenarios from vehicle data associated with one or more vehicles are provided. the scenario identification system obtains at least the inertial measurement unit (imu) data from the vehicle data, derives one or more imu-based driving parameters from the imu data, and analyzes the imu-based driving parameters based on one or more predefined thresholds for identifying the critical scenario(s).



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