Crash Pulse Prediction Using Regression Algorithm with Gradient
                    Descent Optimization Method for Integrated Safety Systems

Sequeira, Gerald Joy; Konda, Anudeep Reddy; Lugner, Robert; Jumar, Ulrich; Brandmeier, Thomas

doi:10.4271/09-10-02-0009

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While estimating crash pulses for a given crash scenario can be done straightforwardly using Finite Element Analysis, variations may occur between the crash pulses generated by simulation and those from real-world crash tests. A crash pulse prediction technique using regression analysis as presented in [7] could be a good alternative, but it is limited by the scope and quality of training data. If the calibration is done only using simulation results, these variations can lead to the misclassification of crashes and result in the misactivation of restraint systems (e.g.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Crash Simulation Models for Reinforcement Learning driven crash-detection algorithm calibration

Afraj

Vaculı́n

Bohmlander

et al. 2023

Preprint

View full text Add to dashboard Cite

The development of finite element vehicle models for crash simulations is a highly complex task. The main aim of these models is to simulate a variety of crash scenarios and assess all the safety systems for their respective performances. These vehicle models possess a substantial amount of data pertaining to the vehicle's geometry, structure, materials, etc., and are used to estimate a large set of system and component level characteristics using crash simulations. It is understood that even the most well-developed simulation models are prone to deviations in estimation when compared to real-world physical test results. This is generally due to our inability to model the chaos and uncertainties introduced in the real world. Such unavoidable deviations render the use of virtual simulations ineffective for the calibration process of the algorithms that activate the restraint systems in the event of a crash (crash-detection algorithm). In the scope of this research, authors hypothesize the possibility of accounting for such variations introduced in the real world by creating a feedback loop between real-world crash tests and crash simulations. To accomplish this, a Reinforcement Learning (RL) compatible virtual surrogate model is used, which is adapted from crash simulation models. Hence, a conceptual methodology is illustrated in this paper for developing an RL-compatible model that can be trained using the results of crash simulations and crash tests. As the calibration of the crash-detection algorithm is fundamentally dependent upon the crash pulses, the scope of the expected output is limited to advancing the ability to estimate crash pulses. Furthermore, the real-time implementation of the methodology is illustrated using an actual vehicle model.

show abstract

Section: Introductionmentioning

confidence: 99%