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Field Issue (Malfunction) incidents are costly for the manufacturer’s service department. Especially for commercial truck providers, downtime can be the biggest concern for our customers. To reduce warranty cost and improve customer confidence in our products, preventive maintenance provides the benefit of fixing the problem when it is small and reducing downtime of scheduled targeted service time. However, a normal telematics system has difficulty in capturing useful information even with pre-set triggers. Some malfunction issue takes weeks to find the root cause due to the difficulty of repeating the error in a different vehicle and engineers must analyze large amounts of data. In order to solve these challenges, a machine-learning-based predictive software/hardware system has been implemented.

In aerodynamic development of ground vehicles, the use of Computational Fluid Dynamics (CFD) is crucial for improving the aerodynamic performance, stability and comfort of the vehicle. Simulation time and accuracy are two key factors of a well working CFD procedure. Using scale-resolving simulations, accurate predictions of the flow field and aerodynamic forces are possible, but often leads to long simulation time. For a given solver, one of the most significant aspects of the simulation time/cost is the temporal resolution. In this study, this aspect is investigated using the realistic vehicle model DrivAer with the notchback geometry as the test case. To ensure a direct and accurate comparison with wind tunnel measurements, performed at TU Berlin, a large section of the wind tunnel is included in the simulation domain. All simulations are performed at a Reynolds number of 3.12 million, based on the vehicle length.

Vehicle rollovers are one of the more severe crash modes in the US - accounting for 32% of all passenger vehicle occupant fatalities annually. One design enhancement to help prevent rollovers is Electronic Stability Control (ESC) which can reduce loss of control and thus has great promise to enhance vehicle safety. The objectives of this research were (1) to estimate the effectiveness of ESC in reducing the number of rollover crashes and (2) to identify cases in which ESC did not prevent the rollover to potentially advance additional ESC development. All passenger vehicles and light trucks and vans that experienced a rollover from 2006 to 2015 in the National Automotive Sampling System Crashworthiness Database System (NASS/CDS) were analyzed. Each rollover was assigned a crash scenario based on the crash type, pre-crash maneuver, and pre-crash events.

This paper presents an improvement in the accuracy of NOx sensors at high NOx concentration regions by optimizing the manufacturing process, sensor electrode materials and structure, in order to suppress the deterioration mechanism of sensor electrodes. Though NOx sensors generally consist of Pt/Au alloy based oxygen pump electrodes and Pt/Rh alloy based sensor electrodes, detailed experimental analysis of aged NOx sensors showed changes in the surface composition and morphology of the sensor electrode. The surface of the sensor electrode was covered with Au, which is not originally contained in the electrode, resulting in a diminished active site for NOx detection on the sensor electrode and a decrease in sensor output. Theoretical analysis using CAE with molecular dynamics supported that Au tends to be concentrated on the surface of the sensor electrode.

Recent studies evaluating motorcycle lean angle have compared theoretical lean angle equations with real-world-tested motorcycle lean angles. These studies have considered several factors affecting lean angle, including the simplified assumptions made when calculating theoretical lean angles, the speed of the motorcycle around a curve, and the geometry of the roadway/curve. This study further evaluates motorcycle lean angle as a function of speed, but primarily focuses on the effects of different travel paths selected by the rider around the same constant radius curve. The testing incorporates nine passes around the same curve traveling three different paths at three different speeds. The real-world-tested lean angles were compared to the predicted calculated lean angles for each tested travel path and speed.

Crash safety researchers have an increased concern regarding the decreased thoracic deflection and the contributing injury causation factors among the elderly population. Sternum fractures are categorized as moderate severity injuries, but can have long term effects depending on the fragility and frailty of the occupant. Current research has provided detail on rib morphology, but very little information on sternum morphology, sternum fracture locations, and mechanisms of injury. The objective of this study is two-fold (1) quantify sternum morphology and (2) document sternum fracture locations using computed tomography (CT) scans and crash data. Thoracic CT scans from the University of Michigan Hospital database were used to measure thoracic depth, manubriosternal joint, sternum thickness and bone density. The sternum fracture locations and descriptions were extracted from 63 International Center for Automotive Medicine (ICAM) crash cases, of which 22 cases had corresponding CT scans.

Evaluating the safety of Autonomous Vehicles (AV) is a challenging problem, especially in traffic conditions involving dynamic interactions. A thorough evaluation of the vehicle’s decisions at all possible critical scenarios is necessary for estimating and validating its safety. However, predicting the response of the vehicle to dynamic traffic conditions can be the first step in the complex problem of understanding vehicle’s behavior. This predicted response of the vehicle can be used in validating vehicle’s safety. In this paper, models based on Machine Learning were explored for predicting and classifying driver’s response. The Naturalistic Driving Study dataset (NDS), which is part of the Strategic Highway Research Program-2 (SHRP2) was used for training and validating these Machine Learning models.

The quantity of heavy components in fuel is increasing as automotive fuels diversify, and engine oil formulations are becoming more complex. These trends result in the formation of larger amounts of carbon deposits as reaction byproducts during combustion, potentially worsening the susceptibility of the engine to knock [1]. The research described in this paper aimed to identify the mechanism that causes knocking to deteriorate due to carbon deposits in low to medium engine load ranges, which are mainly used when the vehicle drives off and accelerates. With this objective, the cylinder temperature and pressure with and without deposits were measured, and it was found that knocking deteriorates in a certain range of ignition timing.

48V mild hybrid powertrains are promising technologies for cost-effective compliance with future CO2 emissions standards. Current 48V powertrains with integrated belt starter generators (P0) with downsized engines achieve CO2 emissions of 95 g/km in the NEDC. However, to reach 75 g/km, it may be necessary to combine new 48V powertrain architectures with alternative fuels. Therefore, this paper compares CO2 emissions from different 48V powertrain architectures (P0, P1, P2, P3) with different electric power levels under various driving cycles (NEDC, WLTC, and RTS95). A numerical model of a compact class passenger car with a 48V powertrain was created and experimental fuel consumption maps for engines running on different fuels (gasoline, Diesel, E85, CNG) were used to simulate its CO2 emissions. The simulation results were analysed to determine why specific powertrain combinations were more efficient under certain driving conditions.

The performance of environment perceiving sensors such as e.g. lidar, radar, camera and ultrasonic sensors is safety critical for automated driving vehicles. Therefore, one has to assess the sensors’ performance to assure the automated driving system’s safety. The performance of these sensors is however to some degree sensitive towards adverse weather conditions. A challenge is to quantify the effect of adverse weather conditions on the sensor’s performance early in the development of an automated driving system. This challenge is addressed in this work for lidar sensors. The lidar equation was previously employed in this context to derive estimates of a lidar’s maximum range in different weather conditions. In this work, we present a stochastic simulation framework based on a probabilistic extension of the lidar equation, to quantify the effect of adverse rainfall conditions on a lidar’s raw detection performance.

Along with the auto industry’s proactive engagement in autonomous driving and the development of hybrid electric vehicles (HEVs) and electric vehicles (EVs) driven by regulatory compliance for vehicle emissions control and fuel economy, there is a growing need for quieter, lighter, more compact and efficient designs of electronic components for automobiles. For wiper functions, the demand for improving wiping performance at high driving speeds and increasing wiped areas is on the rise. To fulfill all these needs, this project aimed to develop the world’s first brushless controlled wiper system, focusing on the following key elements: 1) power efficiency improvement and noise reduction through an effective sensing design and control methods of field weakening and wide-angle commutation, and 2) lightweight and compact design by reducing the weight and shaft length.

Increasingly stringent pollutant and CO2 emission standards require the car manufacturers to investigate innovative solutions to further improve the fuel economy of their fleets. Among these techniques, an extremely lean combustion has a large potential to simultaneously reduce the NOx raw emissions and the fuel consumption of spark-ignition engines. Application of pre-chamber ignition systems is a promising solution to realize a favorable air/fuel mixture ignitability and an adequate combustion speed, even with very lean mixtures. In this work, the combustion characteristics of an active pre-chamber system are experimentally investigated using a single-cylinder research engine. Conventional gasoline fuel is injected into the main chamber, while the pre-chamber is fed with compressed natural gas. In a first stage, an experimental campaign was carried out at various speeds, spark timings and air-fuel ratios.

Small unmanned aerial systems have gained prominence in their use as tools for mapping the 3-dimensional characteristics of accident sites. Typically, the process of mapping an accident site involves taking a series of overlapping, high resolution photographs of the site, and using photogrammetric software to create a point cloud or mesh of the site. This process, known as image-based scanning, is explored and analyzed in this paper. A mock accident site was created that included a stopped vehicle, a bicycle, and a ladder. These objects represent items commonly found at accident sites. The accident site was then documented with several different unmanned aerial vehicles at differing altitudes, with differing flight patterns, and with different flight control software. The photographs taken with the unmanned aerial vehicles were then processed with photogrammetry software using different methods to scale and align the point clouds.

Generally, vehicles do not need power during deceleration. Therefore, the fuel efficiency can be improved by stopping the fuel injection in this period. However, when the fuel cut is activated, NOx is emitted immediately after fuel cut. During the fuel cut period, a large amount of fresh air flows into the catalytic converter installed on a vehicle since there is no combustion. Thus, the catalytic materials are converted into an oxidizing atmosphere. As a result, NOx purification performance of the catalyst deteriorates, and eventually NOx is emitted when combustion restarts. The quantity of NOx in this period is relatively small. However, in case of increasing fuel cuts, emission problem could arise. Therefore, in order to meet the stringent regulation such as LEV III-SULEV20 or 30, the number of fuel cuts need to be limited. The problem is that this strategy leads to a disadvantage of fuel efficiency.

Electronic stability control (ESC) has been an essential part of road vehicle safety for almost three decades. In April of 2007, the United States federal government issued a regulation to test the validity of ESC in development vehicles, and the regulation is called Federal Motor Vehicle Safety Standards (FMVSS) 126 in North America (NA), and an equivalent test in other countries outside of NA called ECE13-H (Economic Commission for Europe). While these standards have been used to certify ESC in development passenger cars for over a decade, this has not yet been scrutinized for the application of autonomous vehicles. Autonomous cars have sensors and control systems which can be used to improve ESC, where commercial standard vehicles do not.

This SAE Aerospace Information Report (AIR) provides descriptions of test methods for determining if an aircraft surface coating of any thickness has adverse effects on aircraft deicing/anti-icing fluids with respect to fluid holdover time performance and aerodynamic performance. Although not the primary mandate of the G-12 Aircraft Ground Deicing Committee, this document also provides descriptions of suggested test methods for evaluating aircraft surface coatings with respect to durability, hardness, weathering, aerodynamic drag, ice adhesion, ice accumulation, contact angle, and thermal conductivity. These additional tests can provide informational data for characterizing the coatings and may be useful to operators when evaluating the coatings.

Many published studies have characterized walking and running speeds of young children. However, there is a paucity of data on the cycling speeds of very young children (4 to 5 years old). The purpose of this study was to obtain an estimate of cycling speed for boys and girls both who are learning to ride bicycles (i.e., younger children who still ride with training wheels) and who have already learned to ride bicycles (i.e., slightly older children who no longer use training wheels). A sample of 32 child riders (17 boys, 15 girls; 17 four-year-olds who still ride with training wheels, 15 five-year-olds who do not) were asked to ride a short pre-defined distance at their usual speed when riding, and again at their highest speed. We found that while age and experience can differentiate riders, there were only small differences between boys’ and girls’ speeds in either age group.

Wireless Power Transfer (WPT) promises automated and highly efficient charging of electric and plug-in-hybrid vehicles. As commercial development proceeds forward, the technical challenges of efficiency, interoperability, interference and safety are a primary focus for this industry. The SAE Vehicle Wireless Power and Alignment Taskforce published the Recommended Practice J2954 to help harmonize the first phase of high-power WPT technology development. SAE J2954 uses a performance-based approach to standardizing WPT by specifying ground and vehicle assembly coils to be used in a test stand (per Z-class) to validate performance, interoperability and safety. The main goal of this SAE J2954 bench testing campaign was to prove interoperability between WPT systems utilizing different coil magnetic topologies. This type of testing had not been done before on such a scale with real automaker and supplier systems.

While convolutional neural networks (CNNs) have revolutionized ground-vehicle autonomy in the last decade, this class of algorithms requires large, truth-labeled data sets to be trained. The process of collecting and labeling training data is tedious, time-consuming, expensive, and error-prone. In order to automate this process, an automated method for training CNNs with simulated data was developed. This method utilizes physics-based simulation of sensors, along with automated truth labeling, to improve the speed and accuracy of training data acquisition for both camera and LIDAR sensors. This framework is enabled by the MSU Autonomous Vehicle Simulator (MAVS), a physics-based sensor simulator for ground vehicle robotics that includes high-fidelity simulations of LIDAR, cameras, and other sensors.

This paper presents yaw testing of vehicles with tread removed from tires at various locations. A 2004 Chevrolet Malibu and a 2003 Ford Expedition were included in the test series. The vehicles were accelerated up to speed and a large steering input was made to induce yaw. Speed at the beginning of the tire mark evidence varied between 33 mph and 73 mph. Both vehicles were instrumented to record over the ground speed, steering angle, yaw angle and in some tests, wheel speeds. The tire marks on the roadway were surveyed and photographed. The Critical Speed Formula has long been used by accident reconstructionists for estimating a vehicle’s speed at the beginning of yaw tire marks. The method has been validated by previous researchers to calculate the speed of a vehicle with four intact tires. This research extends the Critical Speed Formula to include yawing vehicles following a tread detachment event.