Develop the Core of Occupant Classification: New Sensing Tech
Develop Occupant Classification Function
Defining Occupant Classification Function for Vehicle Passive Safety Features
The purpose of this article is to discuss the evolution of Occupant Sensing functions as a sequential logic (algorithm for the Vehicle Passive Safety Features). Its progress from Detection and Location to the requirements necessary for the Classification. This discourse will delve into defining and establishing the foundational requirements for initial Occupant Classification.
Furthermore, the data acquisition is going to ensue upon the appearance of a living entity within the Field of View, triggering the execution of algorithm dedicated to Occupant Presence Detection. Crucially, there is a necessity to delineate a rudimentary criterion for an alive object recognition, facilitating the Signal Output for the Detection function. What, then, constitutes this fundamental requirement for discerning the presence of a living entity?
In addition, this criterion must strike a balance: it should possess dimensions diminutive enough to exhibit traits indicative of life, enabling swift assessment of the moving entity. At the same time simultaneously being substantial enough to avert misidentification as a bird or a squirrel. It is imperative to meticulously tailor the specifics of this requirement to align with the characteristics of the sensor (or sensor set) and the accompanying data processing system.
References:
Development of the Presence Detection Function: https://georgedallen.com/develop-presence-detection-new-occupant-sensing-tech/
Development of the Occupant Location Function: https://georgedallen.com/develop-occupant-location-new-sensing-tech/
Passive Safety Features: https://georgedallen.com/evolution-of-occupant-safety-systems-in-the-vehicle-development/
The Significance of Occupant Classification System Function
Beginning the initial data processing utilizing this very requirement definition, will allow to provide the initial Presence Detection. It may not be defined in terms of the “human” or “animal” classification. However, already it would be already useful for the applications related to the Notification Features. The initial alive Presence Detection can be called “Minimum Classification Standard” for the living entity with the Field of View.
Sequentially, the system is going to deliver the crucial Signal Outputs following the initial data processing cycle, which adheres to the minimum Classification Criteria. These include indicating the presence of an occupant within the Field of View, approximating the occupant’s location relative to the Vehicle Seat Map, and classifying the occupant as a living entity. This initial alive Presence Detection serves as the bedrock for basic alive Classification, establishing a minimum Classification Standard.
Unlocking the Core of Occupant Classification Function Development
Related to certain basic Usecases, the initial conditions for the Data Acquisition and processing need to be established. This is necessary in order to develop the basic algorithm for the Signal Outputs. (The development of the Usecases will be discussed separately.)
Consequently, in this hypothetical scenario, the static Usecase with present alive object is to be investigated by a “system” capable of collecting the data points and process them for the purpose of determining the “alive presence”.
Notes: Development of the Logic for the Classification Function
- The data acquisition is ongoing until the certain volume of the present alive object is computed
- This volume is equivalent to the “Minimum Presence” Standard stored in the Processor
- This Standard is to be used for the initial Classification of the Alive Presence
- Similar approach for the Non-Human Alive criteria development (dogs, for example) with additional criteria (to be developed), as the humans are uniquely different from animals
- The minimum presence size needs to be understood for the non-human alive presence, as animals can be smaller than smallest child and maybe necessary for the Vehicle Presence Detection Function
Notes on the Potential AI Application
Additionally, in exploring the potential AI application in vehicle Systems Engineering, leveraging AI tools for development of the Data Acquisition algorithm emerges as a highly beneficial strategy. Initially, focusing on static Usecases allows for sequential data processing until volume criteria are satisfied. This would be followed by dynamic movement scenarios within the Field of View.
Consequently, this approach facilitates early readiness to provide Signal Outputs as needed. Moreover, aligning this comprehensive development with Simulation technology, specifically Virtualization environment, offers dual benefits. It aids in crafting safe, high-quality products while enabling simulation and verification of intended performance.
Furthermore, the integration of AI tools presents an excellent opportunity to refine preliminary necessities and execute executive algorithms efficiently. Embracing AI in this manner enhances the efficacy and precision of Occupant Classification systems, driving advancements in vehicle safety and functionality. See references.
References.
Development of the Prerequisites: https://georgedallen.com/wp-admin/post.php?post=14241&action=edit
Virtual Development: https://georgedallen.com/virtual-development-embracing-tomorrow-today/
“Virtualization” definition: https://en.wikipedia.org/wiki/Virtualization
Conclusion: Ensuring Optimal Occupant Sensing Functionality
In conclusion, ensuring optimal Occupant Sensing functionality necessitates the comprehensive development of the system functions: Occupant Presence Detection, Location, and Classification. Therefore, a finite-scoped development project can achieve the requisite initial criteria sets. Such exercise would lay a groundwork for robust functionality by employing arbitrary vehicle size parameters, selecting a suitable sensor set with defined Field of View and attachment location, and establishing a rudimentary Vehicle Seat Map.
Moreover, the culmination of this project would not only establish basic requirements for production-level sensor set but also delineates minimum Signal Output requirements and fosters an understanding of ECU processing and power needs. These findings are pivotal for Subsystem SOR preparation, besides outlining the crucial algorithms necessary for the product software, offering insights into critical aspects such as the physical attachment of sensors, final application of Vehicle Seat Map, power availability, and timing readiness for initial Signal Outputs. This holistic approach ensures the efficacy of Occupant Classification systems in enhancing vehicle safety and functionality.
Reference to Systems Engineering Method: https://georgedallen.com/systems-v-model-strategy-in-automotive-design/
About George D. Allen Consulting:
George D. Allen Consulting is a pioneering force in driving engineering excellence and innovation within the automotive industry. Led by George D. Allen, a seasoned engineering specialist with an illustrious background in occupant safety and systems development, the company is committed to revolutionizing engineering practices for businesses on the cusp of automotive technology. With a proven track record, tailored solutions, and an unwavering commitment to staying ahead of industry trends, George D. Allen Consulting partners with organizations to create a safer, smarter, and more innovative future. For more information, visit www.GeorgeDAllen.com.
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