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The efforts of the ISO “Test Variability Task Force” have been aimed at improving the understanding and at reducing brake dynamometer test variability during performance testing. In addition, dynamometer test results have been compared and correlated to vehicle testing. Even though there is already a vast amount of anecdotal evidence confirming the fact that different procedures generate different friction coefficients on the same brake corner, the availability of supporting data to the industry has been elusive up to this point. To overcome this issue, this paper focuses on assessing friction levels, friction coefficient sensitivity, and repeatability under ECE, GB, ISO, JASO, and SAE laboratory friction evaluation tests.

Software testing is all too often simply a bug hunt rather than a well-considered exercise in ensuring quality. A more methodical approach than a simple cycle of system-level test-fail-patch-test will be required to deploy safe autonomous vehicles at scale. The ISO 26262 development V process sets up a framework that ties each type of testing to a corresponding design or requirement document, but presents challenges when adapted to deal with the sorts of novel testing problems that face autonomous vehicles. This paper identifies five major challenge areas in testing according to the V model for autonomous vehicles: driver out of the loop, complex requirements, non-deterministic algorithms, inductive learning algorithms, and fail-operational systems.

Large-eddy simulations (LES) of a motoring single-cylinder engine with transparent combustion chamber (TCC-II) are carried out using a commercially available computer code, CONVERGE. Numerical predictions are compared with high-speed particle image velocimetry (PIV) measurements. Predictions of two spatial discretization schemes, namely, numerically stabilized central difference scheme (CDS) and fully upwind scheme are compared. Four different subgrid scale (SGS) models; a non-eddy viscosity dynamic structure turbulence (DST) model of Pomraning and Rutland, one-equation eddy-viscosity (1-Eqn) model of Menon et al., a zeroequation eddy-viscosity model of Vreman, and the zeroequation standard Smagorinsky model are employed on two different grid configurations. Additionally, simulations are also performed by deactivating the LES SGS models. It is found that the predictions when using the numerically stabilized CDS are significantly better than using the fully upwind scheme.

A method for automatically generating hex-dominant meshes for Computational Fluid Dynamics (CFD) applications is presented in this article. Two important regions of the mesh for any CFD simulation are the interior mesh and the boundary layer mesh. The interior mesh needs to be fine in the critical flow regions to ensure accurate solutions. The proposed method uses Bubble Mesh algorithm which packs bubbles inside the geometry to generate the mesh nodes. Algorithm was tested for sample flow problems and improvements were made to interior and boundary layer mesh generation methods. The interior mesh is generated using directionality and sizing control functions specified on the points of a 3D grid generated over the entire geometry. This offers a flexible control over mesh sizing and local mesh refinement. Boundary layer mesh is important to accurately model the physics of boundary layer near the geometry walls.

There has been an increasing interest in exploring the potential to reduce energy consumption of future connected and automated vehicles. People have extensively studied various eco-driving implementations that leverage preview information provided by on-board sensors and connectivity, as well as the control authority enabled by automation. Quantitative real-world evaluation of eco-driving benefits is a challenging task. The standard regulatory driving cycles used for measuring exhaust emissions and fuel economy are not truly representative of real-world driving, nor for capturing how connectivity and automation might influence driving trajectories. To adequately consider real-world driving behavior and potential “off-cycle” impacts, this paper presents four collaborative evaluation methods: large-scale simulation, in-depth simulation, vehicle-in-the-loop testing, and vehicle road testing.

This paper proposes and evaluates improvements to a crash simulation of a fuel delivery module in a fuel tank. The simulations were performed in ANSYS/LS-DYNA. Deviations between the original simulation and test data were studied and reasons for the deviations hypothesized. These reasons stemmed from some of the simplifying assumptions of the model. Improvements consisted of incorporating plasticity and strain rate effects into the material models. Performance criteria were also directly incorporated into the material models such that non-performing portions of the model could be deactivated during the simulation. Finally, solid-fluid interactions were added into the simulation to include the momentum transfer from fuel to the fuel delivery module. It was previously thought that effects of a crash would be most severe on the module when the fuel tank was empty and the module was full with fuel.

Networking of active and passive safety is the fundamental basis for comprehensive vehicle safety. Situation-relevant information relating to driver reactions, vehicle behavior and traffic environment are fed into a crash probability calculator, which continually assesses the current crash risk and intervenes when necessary with appropriate measures to avoid a crash and reduce potential injuries. This provides effective protection not only for vehicle occupants but also for other, vulnerable road users. As this functionality up till now only relates to the vehicle itself, the next logical step is enhancement leading to the ultimate goal in safety performance, telematics. The integration of this embedded, in-vehicle wireless communication system allows Car-to-Car (C2C) and Car-to-Infrastructure (C2I) functionality for, e.g. hazard warning. This is an integral element of the cascaded ContiGuard® protection measures.

The AUTomotive Open System ARchitecture (AUTOSAR) Development Partnership has published early 2008 the specifications Release 3.0 [1], with a prime focus on the overall architecture, basic software, run time environment, communication stacks and methodology. Heavy developments have taken place in the OEM and supplier community to deliver AUTOSAR loaded cars on the streets starting 2008 [2]. The 2008 achievements have been: Improving the specifications in order to secure the exploitation for body, chassis and powertrain applications Adding major features: safety related functionalities, OBD II and Telematics application interfaces.

The post forming performance of automotive components has continued to be an important but challenging issue in materials science and engineering. The modeling community must know the strength of the formed parts to be able to predict behavior such as crash worthiness, high cycle fatigue resistance and dent resistance. Furthermore, the ductility and toughness of the formed parts must be known for safety and warranty reasons. A review of some previous work reveals that there are levels of complexity in understanding these relationships that are often overlooked. Phenomena such as the Bauschinger Effect and compressive work embrittlement complicate the prediction of post formed properties. This is especially true when the forming strain path contains a compressive component. Previous work has shown that when a tensile stress is applied in a direction of compressive forming strain, drastic reductions in strength and toughness may result.

Driving through intersections can be potentially dangerous because nearly 23 percent of the total automotive related fatalities and almost 1 million injury-causing crashes occur at or within intersections every year [1]. The impact of traffic intersections on trip delays also leads to waste of human and natural resources. Our goal is to increase the safety and throughput of traffic intersections using co-operative driving. In earlier work [2], we have proposed a family of vehicular network protocols, which use Dedicated Short Range Communications (DSRC) and Wireless Access in Vehicular Environment (WAVE) technologies to manage a vehicle's movement at intersections Specifically, we have provided a collision detection algorithm at intersections (CDAI) to avoid potential crashes at or near intersections and improve safety. We have shown that vehicle-to-vehicle (V2V) communications can be used to significantly decrease the trip delays introduced by traffic lights and stop signs.

In the research project between the Institute of Automotive Engineering (FZD) of the Technische Universität Darmstadt (TUD) and Continental Teves AG & Co. oHG a new modeling concept has been developed. With the aim to enhance the current development process, the brake caliper is modeled based on coupled rigid bodies integrated into a nonlinear system model. Using an explicit interface definition, the number of degrees of freedom is minimized and the calculation of caliper performance is possible over a wide range of parameters. Compared to models based on the Finite Element Method (FEM), fully parameterized geometry from CAD is not necessary, thus the caliper can be optimized for a variation of its geometrical and physical parameters. With this modeling approach, typical performance criteria such as caliper fluid displacement, hysteresis, uneven pad wear and residual torque can be calculated in a virtual bench test.

The US American government introduced a law to mandatorily equip passenger vehicles with rear view cameras. Furthermore, US NCAP presented a test for passenger vehicles to brake on pedestrians while back up. These two circumstances lead to main motivation of the development of the Comfort Backup Assist (CBUA). Nevertheless, more and more passenger cars in general are being equipped with rear view cameras. Rear view system (RVS) allows to deliver a rear-view camera system including a braking functionality which is intended to make the driving mission safer and reduce the number of accidents in parking driving situations. RVS also focus on vehicle safety by reducing accidents while taking reversing/parking scenarios and to provides slow de-acceleration of the vehicle gradually to avoid jerk and increase the ride comfort.

We present a life cycle assessment of producing automobiles using the Environmental-Input-Output analysis tool (EIO-LCA). The tool is based on the 1987 U.S. 519 sector input-output table. Appended to the table are more recent data on energy usage and environmental discharges for each sector. All of the data, from the input-output matrix to the energy use and environmental discharges are U.S. government data and are publicly available. The EIO-LCA tool allows us to estimate all of the energy use and environmental discharges associated with making cars, not just that associated with automobile manufacturers. In contrast to the SETAC-EPA life cycle analysis tool, there is no need to draw an arbitrary boundary for the analysis or to engage in an expensive, time consuming effort to gather data on energy use and environmental discharges from each plant. In addition, the data are not confidential.

Driver assistance features have been introduced to the market focusing on basic, independent functional scenarios. The trend is showing that these kinds of products are facing more and more complex scenarios and we are transitioning from single independent functions to a strongly networked system. Some of the drivers for future autonomous vehicles are 360° monitoring by active safety technology and V2X (vehicle to vehicle or vehicle to infrastructure) communication. In the past vehicles were strictly operated by the driver. Advanced driver assistance products added so called feedback features like lane departure warning, forward collision warning, and blind spot monitoring. First steps towards semi-autonomous driving started with the development of active support functions like adaptive cruise control or lane keeping support. Collision mitigation with various authority levels is the next milestone towards automation followed by other, even more advanced, features.

NASA's plans to implement the Vision for Space Exploration include extensive human-robot cooperation across an enterprise spanning multiple missions, systems, and decades. To make this practical, strong enterprise-level interface standards (data, power, communication, interaction, autonomy, and physical) will be required early in the systems and technology development cycle. Such standards should affect both the engineer and operator roles that humans adopt in their interactions with robots. For the engineer role, standards will result in reduced development lead-times, lower cost, and greater efficiency in deploying such systems. For the operator role, standards will result in common autonomy and interaction modes that reduce operator training, minimize workload, and apply to many different robotic platforms. Reduced quantities of spare hardware could also be a benefit of standardization.

Advanced vehicles with lower emissions or higher fuel economy cannot accomplish these social goals unless consumers choose to purchase them instead of conventional automobiles. What attributes will these advanced vehicles need in order to have significant market penetration? In this paper we investigate the importance of “vehicle comparability” in comparative analyses of conventional and alternative fuel/powertrain automobiles. Comparability can mean that the vehicles have identical power, size, safety, range, etc. Alternatively, it can mean that customers find the bundle of attributes of a vehicle equally attractive to the bundle of attributes of another. If customers insist on current vehicle attributes, the advanced vehicles will not be attractive. Analysts differ in the extent to which they account for vehicle equivalency.

We analyze alternative fuel/engine internal combustion engine and electric technology options that will be available over the next two decades for powering a large proportion of the U.S. light duty fleet (cars and light trucks). Lifetime private (vehicle, battery replacement and fuel) and social (externalities associated with pollutant and greenhouse gas emissions) costs for the vehicle options are estimated and compared to those of a baseline gasoline fueled internal combustion engine automobile. Technological advances continue to improve the efficiency and environmental performance of low sulfur reformulated gasoline fueled internal combustion engine automobiles. In our judgment, two fuels which may challenge gasoline for use in internal combustion engines are compressed natural gas and biomass ethanol. Compressed natural gas would lower our dependence on foreign energy supplies, would result in lower greenhouse gas emissions, and is cleaner burning than petroleum fuels.

Extensive modeling and simulation studies have been carried out to evaluate the performance of systems for avoiding run-off-road crashes. Results show that the effectiveness of in-vehicle crash avoidance systems depends on how well they can be tailored to specific vehicle, driver, and roadway characteristics. To this end, a major focus of these studies is the development of improved driver lane-keeping models based on statistical analyses of data collected in driving experiments conducted on highways, rural roads, and test tracks. In recent simulation studies using improved driver models, the performance of crash avoidance systems in tractor-trailers and passenger cars has been compared over a wide range of incipient run-off-road crash conditions. Heavy trucks present a greater challenge for run-off-road crash avoidance systems, because they slightly but frequently leave the lane even under controlled driving, and because they are less stable during recovery maneuvers.

Compressed natural gas (CNG) is an attractive, alternative fuel for spark-ignited (SI), internal combustion (IC) engines due to its high octane rating, and low energy-specific CO2 emissions compared with gasoline. Directly-injected (DI) CNG in SI engines has the potential to dramatically decrease vehicles’ carbon emissions; however, optimization of DI CNG fueling systems requires a thorough understanding of the behavior of CNG jets in an engine environment. This paper therefore presents an experimental and modeling study of DI gaseous jets, using methane as a surrogate for CNG. Experiments are conducted in a non-reacting, constant volume chamber (CVC) using prototype injector hardware at conditions relevant to modern DI engines. The schlieren imaging technique is employed to investigate how the extent of methane jets is impacted by changing thermodynamic conditions in the fuel rail and chamber.

Recommender systems guide a user to useful objects in a large space of possible options in a personalized way. In this paper, we study recommender systems for vehicles. Compared to previous research on recommender systems in other domains (e.g., movies or music), there are two major challenges associated with recommending vehicles. First, typical customers purchase fewer cars than movies or pieces of music. Thus, it is difficult to obtain rich information about a customer’s vehicle purchase history. Second, content information obtained about a customer (e.g., demographics, vehicle preferences, etc.) is also difficult to acquire during a relatively short stay in a dealership. To address these two challenges, we propose an interactive vehicle recommender system based a novel machine learning method that integrates decision trees and multi-armed bandits. Decision tree learning effectively selects important questions to ask the customer and encodes the customer's key preferences.