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About this book

State-of-the-art airbag algorithms make a decision to fire restraint systems in a crash by evaluating the deceleration of the entire vehicle during the single events of the accident. In order to meet the ever increasing requirements of consumer test organizations and global legislators, a detailed knowledge of the nature and direction of the crash would be of great benefit. The algorithms used in current vehicles can only do this to a limited extent. André Leschke presents a completely different algorithm concept to solve these problems. In addition to vehicle deceleration, the chronological sequence of an accident and the associated local and temporal destruction of the vehicle are possible indicators for an accident’s severity.

​About the Author:

Dr. André Leschke has earned his doctoral degree from Tor-Vergata University of Rome, Italy. Currently, he is working as head of a team of vehicle safety developers in the German automotive industry.

Table of Contents

Frontmatter

Chapter 1. Introduction

Abstract
In this chapter, the basic motivation to deal with a topic from the field of classic passive vehicle safety is presented. The objectives and research questions are explained and the structure of the work is described.
André Leschke

State-of-the-Art

Frontmatter

Chapter 2. Basics of Traffic Safety

Abstract
This chapter deals with the various aspects of traffic safety. Different approaches will be shown how the field of traffic safety can be classified. In addition, the historical development of passive vehicle safety in the areas of restraint systems, vehicle structure and electronics will be presented.
André Leschke

Chapter 3. Vehicle Structure, Restraint and Electronic Systems

Abstract
The following chapter describes the state-of-the-art for all components involved in passive safety, such as the occupant cell, the vehicle structure and the various restraint systems. In particular, the electronic components such as the control unit and sensors will be discussed and the functionality of current airbag algorithms will be explained.
André Leschke

New Algorithm Concept and Simulation Model

Frontmatter

Chapter 4. New Algorithm Concept

Abstract
This chapter explains the basics of the new algorithm concept. The principle of the local component-specific load is introduced and a comparison between an algorithm based on a overall vehicle deceleration and the new concept is carried out on the basis of measurement signals.
André Leschke

Chapter 5. Model for the Description of Threshold-Based Algorithms

Abstract
The following chapter proposes a methodology according to which airbag algorithms can be grouped in a three-dimensional model according to their input and output variables, their processing stages and their temporal behaviour.
André Leschke

Chapter 6. Simulation Model for Component-Specific Local Load

Abstract
In this chapter, a methodology is proposed how a simulation can be set up on the basis of finite elements in order to evaluate the load on the components in the front end during an accident. For this purpose, the front end is geometrically divided, and the simulation model of the vehicle is extended to include sensor measuring points in line with the resulting grid. It is shown how a simple evaluation variable can be generated from the simulation values of the component decelerations.
André Leschke

Methods and Results

Frontmatter

Chapter 7. Algorithm for Local Component-Specific Load

Abstract
This chapter deals with the basics of the CI algorithm and introduces the new evaluation variables CI, CIT, CITn and MCITn.
André Leschke

Chapter 8. First Degree of Freedom: Holdmax Threshold

Abstract
In this chapter, a methodology is proposed to find the optimal solution from seven different Holdmax thresholds in the present application. To do so, the CI values determined in the simulation are to be used. A comprehensive comparison of the thresholds using four different criteria is carried out on the basis of proximity measures.
André Leschke

Chapter 9. Data Duality of Crash Intensity Values

Abstract
In this chapter a methodology is presented with the help of which an evaluation of the data quality of the CI values can be carried out. The aim is to make a statement as to whether the available data can be used to create an application that is suitable to meet the requirements on an algorithm which can be used to reach a decision on the firing time. For this purpose, a methodology of multivariate statistics is used to assess whether the data allow sufficient selectivity between NoFire / Misuse and Fire load cases.
André Leschke

Chapter 10. Second Degree of Freedom: Selection of Sensors

Abstract
In the previous chapter, it became clear that, in the case of purely symmetrical and grid-related sensor positioning, some load cases lead to MCITn values that do not correlate with the firing times. This skewness in the data can be influenced by a targeted selection of sensors. With regard to sensor selection, another aspect that can be considered and influenced is the question whether there are sensors that correlate so strongly that they will not make a significant contribution to the selectivity within an algorithm.
André Leschke

Chapter 11. Third Degree of Freedom: Application

Abstract
With the optimized sensor set determined in the previous chapters, a vehicle-specific algorithm can be developed on the basis of which the decision to fire the restraint systems is made. Diagram 11.1 shows the CITn values for all considered load cases in their temporal course and the behaviour of all considered load cases. The set of curves thus represents the number of load cases for which the algorithm must decide whether and, if so, when the restraint systems must be triggered.
André Leschke

Chapter 12. Algorithm Concept for the Classification of Load Cases

Abstract
In the previous chapter, a methodology was presented to derive a single-stage algorithm from the CIT values, on the basis of which a timely firing of restraint devices can be performed. This chapter proves that the information content of the CI values is also sufficient to classify the Fire load cases according to crash type and direction.
André Leschke

Chapter 13. Two-Stage Algorithm to Minimize the Number of Sensors

Abstract
In the preceding chapters, an algorithm concept was developed which, on the basis of sensors distributed locally in the front end, enables a robust algorithm concept for the timely triggering of the restraint systems and for the classification according to load case groups. The selection of the sensors was optimized to meet all requirements that result from the use of a single-stage algorithm concept. In this chapter, another algorithm is presented which is based on the same principle of action involving local component-specific accelerations.
André Leschke

Chapter 14. Validation of the Algorithm in Real Crash Tests

Abstract
In the previous chapters, a methodology based on local component loads was presented with the help of which an algorithm could be developed that enables timely triggering for accident scenarios that represent the real-world load case collective. This methodology was developed using simulation data.
André Leschke

Chapter 15. Summary and Qutlook

Abstract
In this chapter, the results of the work are presented in relation to the research questions, and a concluding examination of the results of the work is carried out. Subsequently, an outlook is given on further fields of investigation and new questions.
André Leschke

Backmatter

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