CAME

CAME

Italy

Non-Member

Leading company in sales and distribution of specialty chemicals. We represent major global producers of raw materials for different application. Our focus is the presence in the friction and sintering marketing worldwide.

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See CAME news on FISITA Spotlight

Congress: Blind Spot Reduction Effects on Behaviour & Opinions of Drivers Using Digital Wing Mirrors

Floris van Oosten to explain how blind spot reduction by optimal location of digital wing mirrors affects behaviour & opinions of drivers

9 Sept 2021

FISITA Congress 2021 programme introduction

This Spotlight gives a run-down of the FISITA Congress 2021 session titles & speakers, & provides links to posts from the contributors

3 Sept 2021

Congress: Effects of Fuels & After-Treatment Systems on Particle Formation from Diesel Engines

Kazutoshi Mori introduces JIS2 diesel fuel & Bio-diesel fuels & after-treatment system effects on emitted nanoparticles & particle clusters

1 Sept 2021

Congress: Development of a vehicle state estimator using the Unscented Kalman Filter algorithm

Development of, and different optimization based tuning approaches for, an Unscented Kalman Filter algorithm based vehicle state estimator

13 Aug 2021

See FISITA Library items from CAME

F2021-PIF-073

Paper + Video

Prof. Dr.-Ing. Francisco Javier Páez, INSIA-UPM, SPAIN
Ing. Ángel Losada, INSIA-UPM, SPAIN
Ing. Juan José Herrero, INSIA-UPM, SPAIN
Prof. Dr.-Ing. Asunción Santamaría, CEDINT-UPM, SPAIN
Dr.-Ing. Luca Piovano, CEDINT-UPM, SPAIN
Ing. Francisco Luque, CEDINT-UPM, SPAIN

Detail

This work is an initial activity in the OPREVU project. This project, funded by the Spanish Ministry of Science and Innovation, is aimed at the use of Virtual Reality (VR) and Artificial Intelligence tools to allow the extraction of Vulnerable Road Users (VRU) behaviour patterns in the event of pedestrian collisions, in order to optimize the Autonomous Emergency Braking (AEB) technologies, incorporated in the new generations of commercial vehicles. With the aim of developing and optimizing the current pedestrian identification systems, vehicle tests are performed on INSIA test track with different vehicles to analyse the behaviour of their AEB systems. These systems are equipped with a Lidar and a camera, whose joint operation allows detecting the proximity of the pedestrian and obtaining variables of interest to assess the automatic intervention of the braking system. The tests are inspired by the CPNA50 and CPNA25 tests, carried out by EURONCAP to validate and certify AEB systems. The reference variables are the TTC (Time-to-collision) and the TFCW. FCW (Forward Collision Warning) is a visual and acoustic signal that appears as a warning light or digitally on the instrument panel and warns of the presence of an obstacle in the vehicle's path, and TFCW is calculated from the sum of the driver's average reaction time and the time to stop if the driver applies pressure on the brake pedal until full detection. On the other hand, TTC is the time calculated from the distance and relative speed of the vehicle with respect to the pedestrian. If the TTC is less than TFCW, the system intervenes. By means of the CARSIM© simulation tool (vehicle-pedestrian-road), it is attainable to modify certain boundary parameters, such as the initial conditions of movement of the pedestrian and the vehicle, as well as their initial relative disposition at the beginning of each test. Along these lines, the virtual model incorporates reactions patterns for the pedestrian, such as stopping, running, or changing direction while crossing the road. These reaction patterns are defined by means of VR tests. The CARSIM© vehicle model integrates the fusion of camera and LiDAR data, and an operating algorithm to control the AEB activation. Using Machine Learning techniques, it is feasible to breed vehicle models based on behavioural patterns from the values obtained in the real tests, and to find out the correlation between the corresponding TTC and TFCW values with parameters measured by the calibration equipment, such as: the maximum speed and the time in which it is reached, the initial relative distance and the relative distance at the moment of AEB activation, or the average deceleration from the start of braking, among others. Hence, the data obtained on the INSIA test tracks allow the virtual models to be validated. Furthermore, the novelty of the approach is to consider the pedestrian reactions just before collision, extracted through users’ experiments made in ad-hoc VR environment, and to generate a more optimised and robust logic with a greater capacity for anticipation.

FISITA World Congress 2021

PIF - Passive and Integral Safety

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Improvement of the AEB activation algorithm based on the pedestrian reaction, F2021-PIF-073, FISITA World Congress 2021

F2021-PIF-070

Paper + Video

Mr. Robert Lugner, CARISSMA Institute of Safety in Future Mobility C-ISAFE, TH Ingolstadt, GERMANY
Mr. Robert Krause, Technische Hochschule Ingolstadt, GERMANY
Mr. Maximilian Inderst, Technische Hochschule Ingolstadt, GERMANY
Mr. Kilian Schneider, Technische Hochschule Ingolstadt, GERMANY
Mr. Gerald Sequeira, Technische Hochschule Ingolstadt, GERMANY
Prof. Dr.-Ing. Thomas Brandmeier, Technische Hochschule Ingolstadt, GERMANY

Detail

In recent years, emergency braking systems were introduced to detect and prevent potentialaccidents. However, it is not always possible to avoid a crash. Hence, active safety sensors and passive safety systems are merged into an integrated safety system to reach a maximum safety level. For this, exteroceptive sensors such as radar, LiDAR, and camera monitor the vehicle’s surrounding and create a virtual map. Critical vehicle constellations are predicted and used to activate passive safety actuators milliseconds before the inevitable crash occurs. However, to securely activate these systems, it is necessary to predict the vehicle movement in critical situations that lead to an accident. Prediction methods must evaluate and interpret the exteroceptive sensors’ information to determine the inevitability of a crash and its upcoming crash severity. This paper presents an interface which links a method for the predictive determination of inevitable crash constellations with a crash severity estimation algorithm. In a conventional approach, the crash inevitability provides the trajectories, and the crash severity is calculated separately for the different possible crash constellations. Thus, the expected crash severity can be interpolated, triggering suitable vehicle restraint systems. A novel approach includes the sensor and system tolerances and their effect on crash severity estimation. The physically feasible ego and bullet vehicle trajectories are calculated, and all combinations are investigated for a possible collision. An adapted vehicle dynamics model was created to suit the needed accuracy and the time-critical condition of a pre-crash situation. This model calculates the time until collision as well as the parameters describing the collision. The parameters like relative speed between both cars ∆v, the impact angle α, and the expected impact location allow the crash severity estimation to compute the associated severity of each trajectory combination. The inherent variances of these parameters due to sensor and data tolerances are also considered. A quadruple mass-spring-dampermodel physically approximates the involved vehicles’ in-crash behavior and provides the resulting crash severity values, e.g., ASI. The resulting array of crash severity will be analyzed to identify trajectory combinations of similar severity and corresponding crash constellations. The influence of the tolerances on the described system can be assumed, and future simplifications of the crash calculations can be achieved. This paper introduces a methodology for a fast and robust combination of inevitability detectionand crash severity estimation for integrated safety systems, including the effects of sensor and datatolerances. The first results show that the model can reproduce various complex frontal crash constellations accurately. More research will be done to implement the algorithm in a prototype vehicle, which can be tested for validation in the research and test center CARISSMA at Technische Hochschule Ingolstadt.

FISITA World Congress 2021

PIF - Passive and Integral Safety

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Interface and Optimizations for Crash Severity Estimation and Inevitability Modelling in Pre-Crash Safety Systems, F2021-PIF-070, FISITA World Congress 2021

F2021-PIF-067

Paper + Video

Azusa Nakata, Honda Motor Co., Ltd., JAPAN
Philipp Bösl, Fraunhofer Institute for High-Speed Dynamics, Ernst-Mach-Institut, EMI, GERMANY
Fumitoshi Kase, Honda Motor Co., Ltd., JAPAN
Toru Hashimoto, Honda Motor Co., Ltd., JAPAN
Shinsuke Shibata, Honda Motor Co., Ltd., JAPAN
Yann Léost, Fraunhofer Institute for High-Speed Dynamics, Ernst-Mach-Institut, EMI, GERMANY
Ines Butz, Fraunhofer Institute for High-Speed Dynamics, Ernst-Mach-Institut, EMI, GERMANY
Thomas Soot, Fraunhofer Institute for High-Speed Dynamics, Ernst-Mach-Institut, EMI, GERMANY
Malte Kurfiß, Fraunhofer Institute for High-Speed Dynamics, Ernst-Mach-Institut, EMI, GERMANY
Stefan Moser, Fraunhofer Institute for High-Speed Dynamics, Ernst-Mach-Institut, EMI, GERMANY
Dr. Jens Fritsch, Fraunhofer Institute for High-Speed Dynamics, Ernst-Mach-Institut, EMI, GERMANY
Dr. Siegfried Nau, Fraunhofer Institute for High-Speed Dynamics, Ernst-Mach-Institut, EMI, GERMANY

Detail

The passive safety of a vehicle is controlled mainly by passenger restraint systems (seatbelt, airbag, etc.) and body crashworthiness. Currently, while simulation is fully utilized to design a whole vehicle and to predict its performance, conducting actual vehicle collision testing is still necessary to prove vehicle safety performance. Although passenger safety can be confirmed and evaluated dynamically during the collision through many sensors attached to dummies, it is challenging to observe vehicle performance in detail during a collision. Especially with respect to a body-in-white, in spite of its high contribution to the crashworthiness, little information can be obtained from currently existing measuring methods during a collision. Therefore, post-collision measurement is often used to validate crashworthiness performance, however it does not validate dynamic performance directly. This paper introduces an approach to observation of in-crash vehicle body deformation via X-ray. Due to its characteristics to pass through objects, X-ray enables interior observation during a collision. The target object for observation was toeboard deformation under IIHS Small Overlap Offset test, because this part strongly affects leg injuries, but cannot be seen directly during a collision using cameras. A static X-ray experiment was conducted first to optimisze the X-ray setup in order to provideensure maximal visibility of the toeboard. Lead markers were set at each measuring point on the toeboard to enable allocation of these points on the X-ray image. As it was very challenging to conduct the Small Overlap Offset test at the sled facility with a high-speed X-ray system, an alternative test condition, called “reverse Small Overlap Offset” was developed. According to physical laws, it was found that causing a moving barrier to collide with a static vehicle can demonstrate results similar to those of an actual Small Overlap test, if the barrier and the vehicle weights have been adjusted so that energy absorption by the vehicle matches between the two cases. Simulation was used to confirm the validity of the test configuration. Positioning of the X- ray source and detector, as well as design of a running barrier, were also conducted through simulation. The newly designed moving barrier was prototyped and the tests were conducted while only a few X-ray images could be taken due to the limitation of the energy source. Nevertheless, the deformation of the toeboard was able to be observed and measured quantitatively. Traditional post-crash measurement was also conducted to confirm the equivalence of the reverse crash mode to a Small Overlap test. The test result was compared with the simulation result to validate the precision of the simulation. Through the research and the actual test, it was proved that the dynamic observation of body deformat