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The optimization and further development of automated driving functions offers great potential to relieve the driver in various driving situations and increase road safety. Simulative testing in particular is an indispensable tool in this process, allowing conclusions to be drawn about the design of automated driving functions at a very early stage of development. In this context, the use of driving simulators provides support so that the driving functions of tomorrow can be experienced in a very safe and reproducible environment. The focus of the acceptance and optimization of automated driving functions is particularly on vehicle lateral control functions. As part of this paper, a test person study was carried out regarding manual vehicle lateral control on the dynamic vehicle road simulator at the Institute of Automotive Engineering.

Investigating human driver behavior enhances the acceptance of the autonomous driving and increases road safety in heterogeneous environments with human-operated and autonomous vehicles. The previously established driver fingerprint model, focuses on the classification of driving style based on CAN bus signals. However, driving styles are inherently complex and influenced by multiple factors, including changing driving environments and driver states. To comprehensively create a driver profile, an in-car measurement system based on the Driver-Driven vehicle-Driving environment (3D) framework is developed. The measurement system records emotional and physiological signals from the driver, including ECG signal and heart rate. A Raspberry Pi camera is utilized on the dashboard to capture the driver's facial expressions and a trained convolutional neural network (CNN) recognizes emotion. To conduct unobtrusive ECG measurements, an ECG sensor is integrated into the steering wheel.

RAMBHA-LP (Radio Anatomy of Moon Bound Hypersensitive Ionosphere and Atmosphere - Langmuir Probe) is one of the key scientific payloads onboard the Indian Space Research Organization’s (ISRO) Chandrayaan-3 mission. Its objectives were to estimate the plasma density and its variations on the near lunar surface. The probe was initially kept in a stowed condition attached to the lander. A mechanism was designed and realized to meet the functional requirement of deploying the probe at a distance of 1 meter, equivalent to the Debye length of the probe in the moon’s plasma environment. The probe deployment mechanism consists of the Titanium alloy spherical probe with a Titanium Nitride coating on its surface to achieve a constant work function, a long carbon-fiber-reinforced polymer boom, a double torsion spring, a dust-protection box, and a shape-memory alloy-based Frangibolt actuator for low-shock separation. The entire mechanism weighed less than 1.5 kilograms.

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Evolving to MedDev provides a new opportunity for executives in aerospace, automotive and medical devices companies to connect and develop long-term growth strategies and find ways to meet the increased short-term demand for medical supplies

Evolving to MedDev provides a new opportunity for executives in aerospace, automotive and medical devices companies to connect and develop long-term growth strategies and find ways to meet the increased short-term demand for medical supplies

Evolving to MedDev provides a new opportunity for executives in aerospace, automotive and medical devices companies to connect and develop long-term growth strategies and find ways to meet the increased short-term demand for medical supplies

Evolving to MedDev provides a new opportunity for executives in aerospace, automotive and medical devices companies to connect and develop long-term growth strategies and find ways to meet the increased short-term demand for medical supplies

Find out why you should attend the 2025 Noise and Vibration Conference and Exhibition.

Evolving to MedDev provides a new opportunity for executives in aerospace, automotive and medical devices companies to connect and develop long-term growth strategies and find ways to meet the increased short-term demand for medical supplies

Evolving to MedDev provides a new opportunity for executives in aerospace, automotive and medical devices companies to connect and develop long-term growth strategies and find ways to meet the increased short-term demand for medical supplies

Evolving to MedDev provides a new opportunity for executives in aerospace, automotive and medical devices companies to connect and develop long-term growth strategies and find ways to meet the increased short-term demand for medical supplies

Evolving to MedDev provides a new opportunity for executives in aerospace, automotive and medical devices companies to connect and develop long-term growth strategies and find ways to meet the increased short-term demand for medical supplies

With the current trend of including the evaluation of the risk of brain injuries in vehicle crashes due to rotational kinematics of the head, two injury criteria have been introduced since 2013 – BrIC and DAMAGE. BrIC was developed by NHTSA in 2013 and was suggested for inclusion in the US NCAP for frontal and side crashes. DAMAGE has been developed by UVa under the sponsorship of JAMA and JARI and has been accepted tentatively by the EuroNCAP. Although BrIC in US crash testing is known and reported, DAMAGE in tests of the US fleet is relatively unknown. The current paper will report on DAMAGE in NCAP-like tests and potential future frontal crash tests involving substantial rotation about the three axes of occupant heads. Distribution of DAMAGE of three-point belted occupants without airbags will also be discussed. Prediction of brain injury risks from the tests have been compared to the risks in the real world.

Abstract Taking the semi-active suspension system as the research object, the forward model and inverse model of a continuous damping control (CDC) damper are established based on the characteristic test of the CDC damper. A multi-mode semi-active suspension controller is designed to meet the diverse requirements of vehicle performance under different road conditions. The controller parameters of each mode are determined using a genetic algorithm. In order to achieve automatic switching of the controller modes under different road conditions, a method is proposed to identify the road roughness based on the sprung mass acceleration. The average of the ratio between the squared sprung mass acceleration and the vehicle speed within a specific time window is taken as the identification indicator for road roughness.

Abstract In order to efficiently predict and investigate a vehicle’s vertical dynamics, it is necessary to consider the suspension component properties holistically. Although the effects of suspension stiffness and damping characteristics on vertical dynamics are widely understood, the impact of suspension friction in various driving scenarios has rarely been studied in both simulation and road tests for several decades. The present study addresses this issue by performing driving tests using a special device that allows a modification of the shock absorber or damper friction, and thus the suspension friction to be modified independently of other suspension parameters. Initially, its correct functioning is verified on a shock absorber test rig. A calibration and application routine is established in order to assign definite additional friction forces at high reproducibility levels.

The knowledge of representative load collectives and duty cycles is crucial for designing and dimensioning vehicles and their components. For human driven vehicles, various methods are known for deriving these load spectra directly or indirectly from fleet measurement data of the customer vehicle operation. Due to the lack of market penetration of highly automated and autonomous vehicles, there is no sufficient fleet data available to utilize these methods. As a result of increased demand for ride comfort compared to human driven vehicles, autonomous vehicle operation promises reduced driving speeds as well as reduced lateral and longitudinal accelerations. This can consequently lead to decreasing operation loads, thus enabling potentially more light-weight, cost-effective, resource-saving and energy-efficient vehicle components.

Semi-active suspension system (SASS) could enhance the ride comfort of the vehicle across different operating conditions through adjusting damping characteristics. However, current SASS are often calibrated based on engineering experience when selecting parameters for its controller, which complicates the achievement of optimal performance and leads to a decline in ride comfort for the vehicle being controlled. Linear quadratic constrained optimal control is a crucial tool for enhancing the performance of semi-active suspensions. It considers various performance objectives, such as ride comfort, handling stability, and driving safety. This study presents a control strategy for determining optimal damping force in SASS to enhance driving comfort. First, we analyze the working principle of the SASS and construct a seven-degree-of-freedom model.