Crafting a New Dynamic Classification for Passive Safety #1
Crafting a New Dynamic Classification Function
Introduction - Classification Function – Discussion Continued
Continuing the discussion on the most complex function, “Classification”, and the enhanced development of the Dynamic Classification, further delineating the evolution of capabilities pertaining to Data Acquisition and Processing in relation to Signal Output, the recognition of Alive Objects using the Minimum Criteria Set determines the Initial Classification status.
Sequentially, beyond this preliminary assessment, completing the algorithm for definitive classification necessitates waiting until the object’s motion completes at the designated seat. The evaluation of the seated occupant is the “Static” Usecase. Therefore, in this scenario, the occupant’s body remains motionless, allowing for the collection and computation of necessary information (attributes).
Consequently, this article explores the “Dynamic” Usecase, wherein the body is in motion due to sudden vehicle stops or accidents. Therefore, the nomenclature denotes the “Dynamic” Use Case for the development of the Dynamic Classification algorithm, highlighting body motion, distinct from vehicle motion.
The Significance of the Dynamic Classification Capability
In order to improve the Passive Safety Occupant protection functions by optimizing restraints and airbags, it’s crucial to understand algorithms governing restraint tension and airbag deployment decision-making, which are typically based on assumptions about the Static body condition in the Seat. A significant advancement would be the development of technology capable of detecting instantaneous body position relative to the Seat and Airbag in Space. This would mark a technological breakthrough, potentially revolutionizing safety measures.
Detecting instantaneous changes in body position and velocity in space from a Systems Engineering standpoint is imperative for the sudden changes in the Vehicle conditions.
In scenarios where the vehicle is static during an accident, default values, either preset or pre-generated based on power initiation time, are essential. Consequently, for a moving vehicle, the Static Usecase for occupants involves identifying their presence, location, belted status, and a basic level of classification.
Moreover, implicit in this discussion is the assumption that each occupied seat designated with airbag protection has undergone static classification with a certain degree of accuracy before the vehicle begins motion.
These assumptions serve as prerequisites for developing the Dynamic Classification algorithm. Ultimately, the ability to provide dynamic body position within specific time frames is integral to feeding data to potentially intelligent airbag deployment ECUs, capable of deploying custom-sized airbags for statically classified occupants when required. Note that the airbag deployment aspect of Use Cases is not addressed here.
Dynamic Classification function - Definition
Generally, the Dynamic Classification function is a derivative of the already computed occupant size taken from static body position, integrating a preliminary assessment of potential occupant body motion within the interior space. Furthermore, the Occupant Sensing System algorithm must deliver specific information sets to the On-board ECU within predefined timeframes, crucial for evaluating danger levels and subsequent vehicle system responses.
Consequently, predicting the motion of seated occupants entails anticipating both non-collision and collision-related behaviors of the vehicle, simulated through various available and newly developed tools. However, the complexity arises from the sudden and potentially multidirectional nature of collision events, resulting in uncontrollable movement of the head and limbs. Thus, prioritizing high-risk scenarios becomes paramount during initial algorithm development.
Moreover, it’s essential to acknowledge that occupants’ behaviors are not regulated in terms of location, posture, or limb positions. Overall, occupants may find themselves unable to control their bodies during sudden changes in vehicle conditions.
Approach to the “Dynamic Classification” Algorithm
Expanding on the Static assessment prerequisites, it’s crucial to consider the occupant’s behavior regarding seat belt usage. In addition, ff the seat belt has never been utilized, the Static Classification would be approximate, lacking accurate body volume verification. Therefore, in cases where the occupant unbuckles just before a collision, the algorithm would default to the Last Known information set. Conversely, for properly belted occupants, all prerequisites, including preliminary body position predictions, would be available.
Moreover, this encompasses potential head movements, leg positions, and belted body motion, accounting for the restrained body’s limited travel abilities. Consequently, ensuring the completion of the Static algorithm before the vehicle initiates motion is paramount.
Sequentially, if the vehicle is equipped with exterior Active Safety sensors and algorithms for evasive maneuvers, the Passive Safety (Dynamic Classification) algorithm can leverage predicted danger levels to verify initial occupant conditions. Once the danger level threshold is met, the Dynamic Classification algorithm is executed.
Finally, the body motion is assessed against the threshold within the airbag deployment distance, ensuring timely signal provision for proper airbag deployment when necessary. Obviously, since collisions above certain speeds occur within fractions of a second, there may not be sufficient time for body travel computation. Hence, maintaining a proper set of “last known” values for Airbag ECU defaults is crucial, as Dynamic Classification serves as a late verification and contribution to the Airbag deployment decision making.
Review of the Usecases: Possible Conditions
Further examining applications of the Dynamic Classification algorithm, let’s start with a straightforward scenario: the driver enters and properly fastens the seat belt. Sequentially, the system efficiently registers the presence, location, and classification of the belted occupant before the vehicle exits park mode. This process typically takes between 30 seconds to 2 minutes, aligning with normal human behavior. Consequently, in the event of an impending collision, the system anticipates maximum head and limb displacement, while calculating body jerk related to the collision from zero to the threshold speed.
Generally, for frontal collisions below 45 mph, extensive Finite Element Analysis (FEA) studies provide insights into body positions across various human sizes. Furthermore, these scenarios undergo simulation within Virtual Development tools to validate predictions and assess body element conditions in real-time.
Alternatively, consider a front passenger of medium or small stature who neglects to use the seat belt. Hence, the preliminary static classification data is approximate, based solely on seated height without verified body depth. Moreover, in the event of a sudden stop or collision, the body may shift uncontrollably, potentially traveling forward to the windshield and across the width of the interior. Therefore, this presents a challenging scenario for injury prevention due to the passenger’s mobility and the direction of airbag deployment. Finally, accurately modeling such cases is feasible but inherently difficult, making it challenging to ensure the individual’s safety.
Conclusion: Dynamic Classification Function Development
In conclusion, the development of Dynamic Classification capabilities for occupant safety signifies a groundbreaking advancement in Passive Safety functionality. By integrating various algorithms, machine learning techniques, and harnessing diverse sensor types, systems engineers can propel occupant safety to unprecedented heights.
Enhanced classification capabilities empower vehicle systems to protect occupants across diverse driving scenarios, including potential collisions, thereby enhancing overall safety standards. The system’s ability to deliver timely information to the onboard ECU during collisions represents the pinnacle of technological advancement. This sophistication enables direct integration with Smart Airbag technology to tailor safety measures according to individual body sizes.
Furthermore, it underscores the readiness of Original Equipment Manufacturers (OEMs) and supply chains to deliver vastly improved Occupant Protection across both traditional and autonomous vehicle models. Notably, considerations regarding non-living objects and stratification of live occupants’ classification are addressed separately.
References:
- System Prerequisites https://georgedallen.com/usecases-development-of-the-prerequisites-new-databases/
- Governing Features https://georgedallen.com/development-of-the-prerequisites-new-passive-safety-features/
- Occupant Presence Detection https://georgedallen.com/develop-presence-detection-new-occupant-sensing-tech/
- Occupant Location https://georgedallen.com/develop-occupant-location-new-sensing-tech/
- Basic Classification https://georgedallen.com/develop-the-core-of-occupant-classification-new-sensing-tech/
- Seat Belt Monitoring Function development Part1 https://georgedallen.com/crafting-new-seat-belt-monitoring-tech-for-passive-safety/
- Seat Belt Monitoring Function development Part2 https://georgedallen.com/crafting-new-seat-belt-monitoring-tech-for-passive-safety-2/
- Classification – Stratification covered separately later
- Classification of Non Living Objects – https://georgedallen.com/develop-the-core-of-object-classification-new-sensing-tech/
- Seat Belt Monitoring Development https://georgedallen.com/advanced-seat-belt-monitoring-technology-in-occupant-safety/
- NCAP Standard https://www.euroncap.com/en/car-safety/the-ratings-explained/safety-assist/occupant-status-monitoring/
- Seat Belt Wikipedia: https://en.wikipedia.org/wiki/Seat_belt
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