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This study assessed a driver’s ability to safely manage Super Cruise lane changes, both driver commanded (Lane Change on Demand, LCoD) and system triggered Automatic Lane Changes (ALC). Data was gathered under naturalistic conditions on public roads in the Washington, D.C. area with 12 drivers each of whom were provided with a Super Cruise equipped study vehicle over a 10-day exposure period. Drivers were shown how to operate Super Cruise (e.g., system displays, how to activate and disengage, etc.) and provided opportunities to initiate and experience commanded lane changes (LCoD), including how to override the system. Overall, drivers experienced 698 attempted Super Cruise lane changes, 510 Automatic and 188 commanded LCoD lane changes with drivers experiencing an average of 43 Automatic lane changes and 16 LCoD lane changes.

The automotive seat has undergone significant advancements in technology due to changing customer demands, levels of autonomy and vehicle regulations. These advancements have presented both opportunities and challenges in creating a pleasant experience for customers by ensuring optimal seat comfort and a joyful human experience. Seats are always being built to accommodate different percentiles of occupant comfort requirements; original equipment manufacturers come up with various seating adjustment features. However, there is considerable variation among each percentile of occupants in how they utilize these features to achieve a comfortable seating position based on their unique preferences and circumstances. Additionally, there are variations in occupant postures due to the ways people have adapted their driving habits or styles when it comes to the way they sit.

Automotive seat comfort systems provide occupants with a choice to adjust the seat to individual preference, enhancing the customized comfort feel. Seat comfort systems such as massager, lumbar support bladders, seat cushion bolster bladders and seat back bolster bladders are increasingly adopted in automotive seats as customer demand for customizable seats is on the rise. Development of seat comfort systems is mainly driven by Tier 1 suppliers to an automotive original equipment manufacturer (OEM). The Automotive OEM must wait until the final seat prototype is ready with all the seat comfort systems packaged to evaluate the seat comfort performance. Computer Aided Engineering (CAE) Tools like CASIMIR provide detail dummies representing humans with tissues and muscles, allowing occupant seat comfort to be predicted virtually.

The study of spray-wall interaction is of great importance to understand the dynamics during fuel-surface impingement process in modern internal combustion engines. The identification of droplet post-impingement pattern (contact, transition, non-contact) and droplet characteristics can quantitatively provide an estimation of energy transfer for spray-wall interaction, thus further influencing air-fuel mixing and emissions under combusting conditions. Theoretical criteria of single droplet post-impingement pattern on a dry wall have been experimentally and numerically studied by many researchers to quantify the hydrodynamic droplet behaviors. However, apart from model fidelity, another issue is the scalability. A theoretical criterion developed from one case might not be well suited to another scenario. In this paper, a data-driven approach for single droplet-dry wall post-impingement pattern utilizing arithmetical machine learning classification methods is proposed and demonstrated.

Off road navigation demands ground robots to traverse complex and often changing terrain. Classification and assessment of terrain can improve path planning strategies by reducing travel time and energy consumption. In this paper we introduce a terrain classification and assessment framework that relies on both exteroceptive and proprioceptive sensor modalities. The robot captures an image of the terrain it is about to traverse and records corresponding vibration data during traversal. These images are manually labelled and used to train a support vector machine (SVM) in an offline training phase. Images have been captured under different lighting conditions and across multiple locations to achieve diversity and robustness to the model. Acceleration data is used to calculate statistical features that capture the roughness of the terrain whereas angular velocities are used to calculate roll and pitch angles experienced by the robot.

The proliferation of small silicon micro-chips has led to a large assortment of low-cost transducers for data acquisition. Production vehicles on average exploit more than 60 on board sensors, and that number is projected to increase beyond 200 per vehicle by 2020. Such a large increase in sensors is leading the fourth industrial revolution of connectivity and autonomy. One major downfall to installing many sensors is compromises in their accuracy and processing power due to cost limitations for high volume production. The same common errors in data acquisition such as sampling, quantization, and multiplexing on the CAN bus must be accounted for when utilizing an entire array of vehicle sensors. A huge advantage of onboard sensors is the ability to calculate vehicle parameters during a daily drive cycle to update ECU calibration factors in real time. One such parameter is driveline damping, which changes with gear state and drive mode. A damping value is desired for every gear state.

An actual trend in the automotive industry is to have global products in order to have economy of scale. This paper presents how a Belt Drive Rack EPS developed for the North American market had to be modified in order to be assembled in a Vehicle sold all around the world. Main technical challenges for achieving that goal were generated from different Architectures, whether electrical or mechanical, used in each vehicle, Packaging issues and Regional Requirements. Main features affected are Database Configuration, Electromagnetic Compatibility, Smooth Road Shake mitigation and Pull Compensation.

The die wear up to 80,800 hits on a prog-die setup for bare DP1180 steel was investigated in real production condition. In total, 31 die inserts with the combination of 11 die materials and 9 coatings were evaluated. The analytical results of die service life for each insert were provided by examining the evolution of surface wear on inserts and formed parts. The moments of appearance of die defects, propagation of die defects, and catastrophic failure were determined. Moreover, the surface roughness of the formed parts for each die insert was characterized using Wyko NT110 machine. The objectives of the current study are to evaluate the die durability of various tooling materials and coatings for flange operations on bare DP 1180 steel and update OEM tooling standards based on the experimental results. The current study provides the guidance for the die material and coating selections in large volume production for next generation AHSSs.

The introduction of new safety critical features using software-intensive systems presents a growing challenge to hazard analysis and requirements development. These systems are rich in feature content and can interact with other vehicle systems in complex ways, making the early development of proper requirements critical. Catching potential problems as early as possible is essential because the cost increases exponentially the longer problems remain undetected. However, in practice these problems are often subtle and can remain undetected until integration, testing, production, or even later, when the cost of fixing them is the highest. In this paper, a new technique is demonstrated to perform a hazard analysis in parallel with system and requirements development. The proposed model-based technique begins during early development when design uncertainty is highest and is refined iteratively as development progresses to drive the requirements and necessary design features.

Since transient vehicle HVAC computational fluids (CFD) simulations take too long to solve in a production environment, the goal of this project is to automatically create a lumped-parameter flow network from a steady-state CFD that solves nearly instantaneously. The data mining algorithm k-means is implemented to automatically discover flow features and form the network (a reduced order model). The lumped-parameter network is implemented in the commercial thermal solver MuSES to then run as a fully transient simulation. Using this network a “localized heat transfer coefficient” is shown to be an improvement over existing techniques. Also, it was found that the use of the clustering created a new flow visualization technique. Finally, fixing clusters near equipment newly demonstrates a capability to track localized temperatures near specific objects (such as equipment in vehicles).

Pre-collision systems (PCS) use forward-looking sensors to detect the location and motion of vehicles ahead and provide a sequence of actions to help the driver either avoid striking the rear-end of another vehicle or mitigate the severity of the crash. The actions include driver alerts, amplification of driver braking as distance decreases (dynamic brake support, DBS), and automatic braking if the driver has not acted or has not acted sufficiently (crash imminent braking, CIB). Recent efforts by various organizations have sought to define PCS objective test procedures and test equipment in support of consumer information programs and potential certification. This paper presents results and insights from conducting DBS and CIB tests on two production vehicles sold in the US. Eleven scenarios are used to assess the systems' performance. The two systems' performance shows that commercial systems can be quite different.

At the onset of soak, air and surface temperatures in an engine bay enclosure are elevated since temperature of heat sources are high while convective cooling is sharply reduced as a result of airflow being shut off from the inlet grilles of the vehicle leading to temperature spikes. Accurate simulation of this important thermal and flow regime that is natural convection driven, highly transient and complex is therefore very important. In this investigation, we simulate flow in the engine bay at the onset of soak with fixed thermal boundary conditions where the geometries representing the engine bay and components are simplified. Good agreement was observed with detailed experimental data available in references for both velocities and temperatures.

Increasing fuel injection pressure has enabled reduction of diesel emissions while retaining the advantage of the high thermal efficiency of diesel engines. With production diesel injectors operating in the range from 300 to 2400 bar, there is interest in injection pressures of 3000 bar and higher for further emissions reduction and fuel efficiency improvements. Fundamental understanding of diesel spray characteristics including very early injection and non-vaporizing spray penetration is essential to improve model development and facilitate the integration of advanced injection systems with elevated injection pressure into future diesel engines. Studies were conducted in an optically accessible constant volume combustion vessel under non-vaporizing conditions. Two advanced high pressure multi-hole injectors were used with different hole diameters, number of holes, and flow rates, with only one plume of each injector being imaged to enable high frame rate imaging.

This study concerns the generation of response surfaces for kinematics and injury prediction in pedestrian impact simulations using human body model. A 1000-case DOE (Design of Experiments) study with a Latin Hypercube sampling scheme is conducted using a finite element pedestrian human body model and a simplified parametric vehicle front-end model. The Kriging method is taken as the approach to construct global approximations to system behavior based on results calculated at various points in the design space. Using the response surface models, human lower limb kinematics and injuries, including impact posture, lateral bending angle, ligament elongation and bone fractures, can be quickly assessed when either the structural dimensions or the structural behavior of the vehicle front-end design change. This will aid in vehicle front-end design to enhance protection of pedestrian lower limbs.

To investigate the effect of aeration on fluid-elastomer compatibility, 4 types of elastomers were aged in three gear lubes. The four types of elastomers include a production fluorinated rubber (FKM) and production hydrogenated nitrile rubber (HNBR) mixed by the part fabricator, a standard low temperature flexible fluorinated rubber (FKM, ES-4) and a standard ethylene-acrylic copolymer (AEM, ES-7) mixed by SAE J2643 approved rubber mixer. The three gear lubes are Fluid a, Fluid b and Fluid c, where Fluid b is a modified Fluid with additional friction modifier, and Fluid c is friction modified chemistry from a different additive supplier. The aeration effect tests were performed at 125°C for 504 hours. The aerated fluid aging test was performed by introducing air into fluid aging tubes as described in General Motors Company Materials Specification GMW16445, Appendix B, side-by-side with a standard ASTM D471 test.

The application of Model-Based Development (MBD) techniques for automotive control system and software development have become standard processes due to the potential for reduced development time and improved specification quality. In order to improve development productivity even further, it is imperative to introduce a systematic Verification and Validation (V&V) process to further minimize development time and human resources while ensuring control specification quality when developing large complex systems. Traditional methods for validating control specifications have been limited by control specification scale, structure and complexity as well as computational limitations restricting their application within a systematic model-based V&V process. In order to address these issues, Toyota developed Hierarchical Accumulative Validation (HAV) for systematically validating functionally structured executable control specifications.

In today's light-weight vehicles, the strength of spot welds plays an important role in overall product integrity, reliability and customer satisfaction. Naturally, there is a need for a quick and reliable technique to inspect the quality of the welds. In the past, the primary quality control tests for detecting weld defects are the destructive chisel test and peel test [1]. The non-destructive evaluation (NDE) method currently used in industry is based on ultrasonic inspection [2, 3, 4]. The technique is not always successful in evaluating the nugget size, nor is it effective in detecting the so-called “cold” or “stick” welds. Therefore, it is necessary to develop a precise and reliable noncontact NDE method for spot welds. There have been numerous studies in predicting the weld nugget size by considering the spot-weld process [5, 6].

Traditional warm forming of aluminum refers to sheet forming in the temperature range of 200°C to 350°C using heated, matched die sets similar to conventional stamping. While the benefits of this process can include design freedom, improved dimensional capability and potentially reduced cycle times, the process is complex and requires expensive, heated dies. The objective of this work was to develop a warm forming process that both retains the benefits of traditional warm forming while allowing for the use of lower-cost tooling. Enhanced formability characteristics of aluminum sheet have been observed when there is a prescribed temperature difference between the die and the sheet; often referred to as a non-isothermal condition. This work, which was supported by the USCAR-AMD initiative, demonstrated the benefits of the non-isothermal warm forming approach on a full-scale door inner panel. Finite element analysis was used to guide the design of the die face and blank shape.

In recent years, there has been a growing interest in driver visibility. This is, in part, due to increasing emphasis placed on design factors influencing visibility such as: aerodynamics, styling, structural stiffness and vehicle packaging. During the development process of a vehicle, it is important to be able to quantify all of these factors. Visibility, however, owing to its sensory nature, has been harder to quantify. As a result, General Motors (GM) has undertaken a study to gain deeper insight into customer perceptions surrounding visibility. Clinics were conducted to help determine the relative importance of different metrics. The paper also explores several new metrics that can help predict customer satisfaction based on vehicle configuration.

An extruded Mg-Al-Mn (AM30) magnesium alloy was subjected to uniaxial compression along the extrusion direction (ED) and the extrusion radial direction (RaD) at 450 °C and different strain rates. The microstructure and texture of the AM30 alloy under different deformation conditions were examined. Texture evolution was characterized by electron backscatter diffraction (EBSD). The activity of different deformation modes including twinning were simulated using the visco-plastic self-consistent (VPSC) and the simplistic Sachs polycrystal plasticity models. The results show that the microstructure and the mechanical property of the Mg alloy strongly depend on the strain rate, with twinning activated at strain rates >0.5 s−1. Dynamic recrystallization and twinning interacted with each other and affected the final microstructure and mechanical property of the magnesium alloy.