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The new sound and vibration laboratory at Autoliv’s Ogden Technical Center (OTC) was purpose-built with a focus on automotive safety restraint product development (air bags, seat belts, steering wheels, etc.). The laboratory requirements stem from the continued industry trend of quieter vehicles which drives the need for components with extremely low levels of rattle noise. The laboratory at OTC complements similar Autoliv testing facilities around the world. Test articles range from several cubic inches up to approximately one cubic foot and contain varying degrees of moving elements. With the new laboratory at OTC, Autoliv can test new product designs earlier in the development process and obtain test results and feedback faster. The function of the OTC test lab is vibration-induced rattle noise; shake components with a known input and measure the resulting noise.

The requirements of suspension systems have become increasingly complex in recent years due to the expansion of global markets and diversification of the conditions under which vehicles are used in different parts of the world. It is also becoming increasingly important to ensure that vehicles offer the secure handling stability which are expected by drivers, but can also provide an adequate level of ride comfort when driving on a wide diversity of road surfaces in all parts of the world. From an environmental viewpoint, it is also essential to achieve weight reductions for better fuel economy. To meet these wide-ranging requirements, we have developed a new multi-link rear suspension that has a simple link configuration and a lower link that features a connecting bushing mechanism developed by Nissan.

Rollover tests are performed to design the algorithms for deployment of countermeasures to mitigate occupant ejection in rollover situations. The ditch fall-over test is one of the rollover test methods in which a vehicle on a steep slope, representing a ditch embankment, is subjected to a forced steering operation that results in a turnover. An accurate prediction method is needed to determine the specifications of the ditch fall-over test equipment and test conditions because a test-based trial-and-error process involves high cost of performing repeated experiments and preperation for various types of related test equipment. This paper presents a newly developed numerical simulation method for simulating vehicle behavior in ditch fall-over tests.

The use of a hydraulic drive system with accumulator energy storage has the potential of providing large gains in fuel economy of internal combustion engine passenger automobiles. The improvement occurs because of efficient regenerative braking and the practicality of decoupling the engine operation from the driving cycle demands. The concept under study uses an engine-driven pump supplying hydraulic power to individual wheel pump/motors (P/M's) and/or an accumulator. Available P/M's have high efficiencies (e.g., 95%) at the ideal point of operation, but the efficiency falls off considerably at combinations of pressure, speed, and displacement that are significantly away from ideal. In order to maximize the fuel economy of the automobile, it is necessary to provide the proper combination of components, system design, and control policies that operate the wheel P/M's as close as possible to their maximum efficiency under all types of driving and braking conditions.

The use of virtual prototyping early in the design stage of a product has gained popularity due to reduced cost and time to market. The state of the art in vehicle simulation has reached a level where full vehicles are analyzed through simulation but major difficulties continue to be present in interfacing the vehicle model with accurate powertrain models and in developing adequate formulations for the contact between tire and terrain (specifically, scenarios such as tire sliding on ice and rolling on sand or other very deformable surfaces). The proposed work focuses on developing a ground vehicle simulation capability by combining several third party packages for vehicle simulation, tire simulation, and powertrain simulation. The long-term goal of this project consists in promoting the Digital Car idea through the development of a reliable and robust simulation capability that will enhance the understanding and control of off-road vehicle performance.

Using a low-speed high-torque (LSHT) pump/motor to provide the speed range and torque for a hydrostatic automobile offers a number of advantages over using a high-speed low-torque pump/motor, combined with a gear reducer. However, there appear to be no LSHT units commercially available that have true variable displacement capability. Because of this void, a variable displacement pump/motor has been designed and built that could provide a direct drive for each wheel of a hydrostatic automobile. The unit uses some components such as the cylinder block, piston and modified rotating case from a commercially available radial piston pump/motor. Initial preliminary testing of the pump/motor indicates that it has good efficiency and performance characteristics, and, with further development should be very attractive for automotive use. This paper focuses on the design and kinematics of the device.

This study proposes an impact-triggered automatic braking system as a potential safety improvement based on the characteristics of the Multiple Impact Crashes (MICs). The system activates with a signal of airbag deployment in a collision to reduce the vehicle speed in the subsequent collisions. The effectiveness was estimated by an in-depth review of the National Automotive Sampling System-Crashworthiness Data System (NASS-CDS). The cases were extracted on the basis of the 3-point lap and shoulder belted occupants, incurring Maximum Abbreviated Injury Scale level 3 to 6 injuries (MAIS 3+), in the crashes occurred from 2004 to 2006, without vehicle rollover or occupant ejection, where the involved vehicles were 2000 and newer model year cars and light trucks.

Brake systems in current production automobiles are the result of a long evolutionary process beginning with the first practical hydraulic brake patent in 1917. While the basic hydraulic design has many advantages, recent modifications to this system for anti-lock braking and traction control considerably increase the cost of manufacture. As a result, many manufacturers are investigating the possibility of developing alternate braking system structures that cost less and can easily interface with vehicle electronics. Evaluating these systems for fault tolerance and failure effects is crucial to provide a safe and reliable vehicle braking system. This paper demonstrates the use of the Fault Tree Analysis method for carrying out such an evaluation. An example system is presented to illustrate the application of this method to automobile brake design.

Traffic state identification is a key problem in intelligent transportation system. As a new technology, connected and automated vehicle can play a role of identifying traffic state with the installation of onboard sensors. However, research of lane level traffic state identification is relatively lacked. Identifying lane level traffic state is helpful to lane selection in the process of driving and trajectory planning. In addition, traffic state identification precision with low penetration of connected and automated vehicles is relatively low. To fill this gap, this paper proposes a novel method of identifying traffic state in the presence of connected and automated vehicles with low penetration rate. Assuming connected and automated vehicles can obtain information of surrounding vehicles’, we use the perceptible information to estimate imperceptible information, then traffic state of road section can be inferred.

Identifying edge cases for designed algorithms is critical for functional safety in autonomous driving deployment. In order to find the feasible boundary of designed algorithms, simulations are heavily used. However, simulations for autonomous driving validation are expensive due to the requirement of visual rendering, physical simulation, and AI agents. In this case, common sampling techniques, such as Monte Carlo Sampling, become computationally expensive due to their sample inefficiency. To improve sample efficiency and minimize the number of simulations, we propose a tailored active learning approach combining the Support Vector Machine (SVM) and the Gaussian Process Regressor (GPR). The SVM learns the feasible boundary iteratively with a new sampling point via active learning. Active Learning is achieved by using the information of the decision boundary of the current SVM and the uncertainty metric calculated by the GPR.