Applus IDIADA is a global partner to the automotive industry, supporting its clients in product development activities by providing design, engineering, testing and homologation services. The company has more than 2.400 professionals and an international network of subsidiaries and branch offices in 22 countries which ensures that our clients get customized, added-value solutions.
Engineering services: Comprehensive design, engineering and validation capabilities for turnkey vehicle development projects at international level: CAD, CAE and testing of all major vehicle functionalities with unique in house state-of-the-art facilities.
IDIADA provides an extensive range of product development services in the fields of passive and active safety, ADAS & CAV, powertrain & HEV, electronics and reliability. Our expertise in both physical and virtual testing means maximum efficiency in cost and time.
Proving grounds: IDIADA offers the most comprehensive proving grounds in Europe and Asia. The proving grounds, located in Spain and China, offer excellent customer support combined with first-class test tracks and fully-equipped confidential workshops. Both facilities offer the highest standards of safety and confidentiality.
Homologation services in accordance with all European EC and ECE Regulations. We are also accredited for Australia, Europe, Japan, Taiwan, Malaysia and give consultancy to other countries and regions such as South America (including Brazil), China, Russia, Middle East, Gulf Countries, ASEAN, USA, Canada, among others.

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ISC2021-21
Video
Detail
Q&A moderated by Dr. Hong Wang, Associate Research Professor at the School of Vehicle and Mobility, Tsinghua University, and Deputy Executive Director, CAICV-SOTIF Technical Alliance; with Dr. Adrian Zlocki, Head of Automated Driving, fka GmbH; and Stefan de Vries, Project Manager & Business Developer, Connected and Automated Vehicles, Applus IDIADA
FISITA Intelligent Safety Conference 2021 hosted by China SAE
1 SOTIF (Architecture, Perception, Planning, Control, Test, Evaluation)
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ISC2021-17
Video
Detail
Stefan de Vries, Project Manager & Business Developer, Connected and Automated Vehicles, Applus IDIADA
Abstract: Safety of the intended functionality (SOTIF) is about ensuring the absence of unreasonable risk due to performance limitations, function insufficiencies and foreseeable misuse. Whereas SOTIF verificationfocusses on reduction of the known unsafe scenarios in controlled environments, SOTIF validation aims to disclose unknown unsafe scenarios by virtual simulation and driving on public roads. To reduce the large driving effort required to validate the safety of automated vehicle functions, Applus IDIADA developed a route selection method designed to identify routes with an above-average probability of encountering challenging elements. And to statistically demonstrate reliability, Applus IDIADA developed a method to calculate a target mileage taking human performance as a benchmark.
FISITA Intelligent Safety Conference 2021 hosted by China SAE
1 SOTIF (Architecture, Perception, Planning, Control, Test, Evaluation)
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EB2021-STP-015
Paper + Video + Slides
Detail
Ing. Fabio Squadrani, Applus IDIADA, SPAIN
Mr. Danilo Mendes Pedroso, Applus IDIADA, SPAIN
Mr. Kenneth Mendoza, Applus IDIADA, SPAIN
Dr. Eng. Juan J. Garcia Bonito, Applus IDIADA, SPAIN
Mr. Juan Pablo Barles, Applus IDIADA, SPAIN
Mr. Antonio Rubio, Applus IDIADA, SPAIN
Mr. Antonio Jesus Contreras, Applus IDIADA, SPAIN
Mr. Jose Francisco Martinez, Applus IDIADA, SPAIN
Research and/or Engineering Questions/Objective:
The availability of big sets of data coming from brake durability tests paves the way for making predictions and decisions related to the noise coming from brakes. In this paper, the workflow for detecting brake squeal and all its main characteristics is presented.
Methodology:
Initially, a uniform set of data is generated, having a repetitive structure and format. This set of data will be used to train the machine learning algorithm. From the raw data coming from the vehicle data acquisition system, a spectrogram is mathematically generated, to graphically associate sound pressure level and noise frequency within the time domain. These spectrograms will be used to train the machine learning algorithm, which will be recognizing brake noise using the spectrogram images. The final objective is to detect squeal and to identify the frequency, sound pressure level, and subjective rating as well.
Results:
Once the algorithm is trained with thousands of brake noise events coming from real-life brake durability, brake noise is detected with a very high level of confidence. Currently, brake squeal is the noise being identified during this first phase of the project and is identified with a proper level of confidence, including frequency and SPL.
In the second phase of the project, the algorithm is also being evolved to associate a rating to the squeal noise event detected. The algorithm is capable to predict the subjective rating provided by a professional driver during standard driving or during specific noise research maneuvers.
Limitations of this study:
The real-time detection is currently under investigation and could affect the resolution of the spectrogram to be used to train the algorithm and to detect the brake noise. However, the current level of the study does not currently show any predictable problem that could arise when the machine learning algorithm is embedded within a real-time system.
Other brake noises should also be identified, even if less amount of data is available when compared with brake squeal.
Conclusion:
The study shows an alternative method for automatic noise detection and shows the possibility of automatically rating the brake noise. Real-time detection is also investigated and the results of its initial integration within embedded systems is shown.
EuroBrake 2021
NVHV
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