Search Results

Abstract Autonomous vehicles (AVs) rely heavily upon their perception subsystems to “see” the environment in which they operate. Unfortunately, the effect of variable weather conditions presents a significant challenge to object detection algorithms, and thus, it is imperative to test the vehicle extensively in all conditions which it may experience. However, the development of robust AV subsystems requires repeatable, controlled testing—while real weather is unpredictable and cannot be scheduled. Real-world testing in adverse conditions is an expensive and time-consuming task, often requiring access to specialist facilities. Simulation is commonly relied upon as a substitute, with increasingly visually realistic representations of the real world being developed.

Abstract Vehicular communications face unique security issues in wireless communications. While new vehicles are equipped with a large set of communication technologies, product life cycles are long and software updates are not widespread. The result is a host of outdated and unpatched technologies being used on the street. This has especially severe security impacts because autonomous vehicles are pushing into the market, which will rely, at least partly, on the integrity of the provided information. We provide an overview of the currently deployed communication systems and their security weaknesses and features to collect and compare widely used security mechanisms. In this survey, we focus on technologies that work in an ad hoc manner. This includes Long-Term Evolution mode 4 (LTE-PC5), Wireless Access in Vehicular Environments (WAVE), Intelligent Transportation Systems at 5 Gigahertz (ITS-G5), and Bluetooth.

Abstract This article is motivated by observations of the wind tunnel measurement data acquired during benchmarking and program development for a variety of passenger vehicles over the years. In wind noise development, contribution analysis is a common practice to screen and identify the most significant sources and paths. In order to shed light on the whole picture of the contribution analysis, the work presented in this article falls into two categories. One is the analysis of underlying mechanisms for a better understanding of the phenomena observed in the contribution results. The other is the summarization of wind noise contributions obtained by wind tunnel testing for some representative subsystems, e.g., the contributions based on different reference states, the effect of grilles, underbody, acoustic glass, and auditory masking.

Abstract Car manufacturers are continuously improving passenger comfort by advancing technologies including highly automated driving. Before the broad introduction of automated driving, specific human factors regarding passenger comfort must be considered, including motion sickness. Therefore, the identification of the frequency of motion sickness and associated factors in the population is needed to extrapolate the effects for future mobility systems. We conducted three surveys between 2015 and 2020, asking people questions about their experience with motion sickness in cars. Based on the responses of 1165 participants, gender and age showed a strong influence on the self-reported frequency of motion sickness. For deeper analysis, a logistic order regression model was used to estimate the frequency of motion sickness for different user typologies.

Abstract Automated driving as one of the most anticipated technologies is approaching its market release in the near future. Since several years, the research in the automotive industry is largely focused on its development and presents well-engineered prototypes. The many aspects of this development do not only concern the function and its components itself, but also the proof of safety and assessment for its market release. It is clear that previous methods used for the release of Advanced Driver Assistance Systems are not applicable. In contrast to already released systems, automated driving is not restricted to a certain field of application in terms of driving scenarios it has to take action in. This results in an infeasible amount of required testing and unforeseeable scenarios the function can face throughout its lifetime. In this article, we show a scenario-based approach that promises to overcome those challenges.

Abstract This article introduces a methodology for conducting comparative evaluations of vibration-induced discomfort. The aim is to outline a procedure specifically focused on assessing and comparing the discomfort caused by vibrations. The article emphasizes the metrics that can effectively quantify vibration-induced discomfort and provides insights on utilizing available information to facilitate the assessment of differences observed during the comparisons. The study also addresses the selection of appropriate target scenarios and test environments within the context of the comparative evaluation procedure. A practical case study is presented, highlighting the comparison of wheel corner concepts in the development of new vehicle architectures. Currently, the evaluation criteria and difference thresholds available allow for comparative evaluations within a limited range of vehicle vibration characteristics.

Abstract Dynamic stresses exist in parts of a catalyst muffler caused by the vibration of a moving vehicle, and it is important to clarify and predict the vibration response properties for preventing fatigue failures. Assuming a vibration isolating installation in the vehicle frame, the vibration transmissibility and local dynamic stress of the catalyst muffler were examined through a vibration machine. Based on the measured data and by systematically taking vibration theories into consideration, a new prediction method of the vibration modes and parameters was proposed that takes account of vibration isolating and damping. A lumped vibration model with the six-element and one mass point was set up, and the vibration response parameters were analyzed accurately from equations of motion. In the vibration test, resonance peaks from the hanging bracket, rubber bush, and muffler parts were confirmed in three excitation drives, and local stress peaks were coordinate with them as well.

Abstract This article investigates the dynamics of non-smooth and nonlinear oscillations of a bicycle-car model, considering the tire-road separation. Road contact applies a non-holonomic constrain on the dynamics system that makes the equations of motion to be different under in-contact and off-contact conditions. The set of nonlinear equations of the system has been formulated based on nondimensionalization to minimize the number of parameters and generalize the results. To compare the quality of different suspensions in reducing the unpleasant no-contact conditions, we define a contact-free fraction indicator to measure the separation fraction time during a cycle of steady-state oscillation. An observation of frequency responses including vertical displacements, the pitch mode, and the domain of contact-free fraction of time has been investigated to clarify engineering design directions.

Abstract In this study, a computer-aided design (CAD) geometry system that is linked to each other to create a parametric form of the side rear door’s inner frame sheet piece on a passenger vehicle body in a Siemens NX environment was developed. The system was created in the NX CAD environment, using the program’s unique product development structure. The system was designed and modified for time-consuming parts. At the end of the study, the parameterized vehicle door geometries worked in the NX environment standardized the design process and accelerated the design works.

Abstract The riding-comfort of high-speed trains affects the travel experience of passengers, and the lightweight design technology of the carbody increases the flexible vibration and reduces passenger comfort. To this end, a vertical dynamics model of railway vehicles is established to demonstrate the potential of using passive inerter-based suspensions to reduce the flexible vibration of the carbody and improve riding-comfort. According to the characteristics of the inerter component, an appropriate inerter-based suspension is applied to the railway vehicle to reduce low-frequency resonance. The sum of the comfort indexes of the three reference points of the carbody is optimized as the objective function to improve the passenger comfort of the whole vehicle. The results reveal that the inerter-based suspension applied to the primary or secondary suspension has different effects on vehicle vibration.

Abstract Renewable energy sources provide an excellent opportunity for state departments of transportation (DOTs) to benefit from a dual use of land while providing flexible, resilient, affordable, and environmentally responsible modes of generation. Solar photovoltaic (PV) systems are particularly useful in this regard. This study presents a literature review on the types of solar project partnerships, application of solar PV systems by DOTs in the United States (U.S.), solar energy potential, energy policies, and incentives in Virginia. In addition, a feasibility assessment of installing solar PV systems at six (6) Virginia DOT (VDOT)-owned sites is presented. The review of the literature indicated that twenty state DOTs have implemented or are developing solar projects using their facilities. The feasibility assessment showed the benefits of installing solar PV systems at VDOT facilities.

Abstract The first objective of this study, addressed in Part 1, is to use finite element (FE) human body modeling (HBM) to evaluate the tangent of the Belt-to-Pelvis angle (tanθBTP) as a submarining predictor in frontal crashes for occupants in reclined seats. The second objective, addressed in Part 2, is to use this predictor to assess two technical solutions for reducing submarining risks for two different occupant anthropometries. In Part 1, tanθBTP (the lap belt penetration from the anterior superior iliac spine [ASIS] in the abdominal direction) was evaluated in impact simulations with varying seat belt anchor positions. Sled simulations with a 56 km/h full-frontal crash pulse were performed with the SAFER HBM morphed to the anthropometry of a small female and average male. A correlation was found between the submarining predictor and submarining.

Abstract The automotive industry offers many applications for machine learning (ML), in general, and deep neural networks in particular. However, the real-world deployment of neural networks into safety-critical components remains a challenge as models would need to offer robustness under a wide range of operating conditions. In this work, we focus on uncertainty estimation, which can be used to deliver predictors that fail gracefully, by detecting situations where their predictions are unreliable. Following Gräber et al. [1], we use Recurrent Neural Networks (RNNs) to perform sideslip angle estimation. To perform robust uncertainty estimation, we augment the RNNs with generative models. We demonstrate the advantage of the proposed model architecture over Monte Carlo (MC) dropout [2] on the Revs data set [3].

Abstract This study evaluates the applicability of the Octane Index (OI) framework under conventional spark ignition (SI) and “beyond Research Octane Number (RON)” conditions using nine fuels operated under stoichiometric, knock-limited conditions in a direct injection spark ignition (DISI) engine, supported by Monte Carlo-type simulations which interrogate the effects of measurement uncertainty. Of the nine tested fuels, three fuels are “Tier III” fuel blends, meaning that they are blends of molecules which have passed two levels of screening, and have been evaluated to be ready for tests in research engines. These molecules have been blended into a four-component gasoline surrogate at varying volume fractions in order to achieve a RON rating of 98. The molecules under consideration are isobutanol, 2-butanol, and diisobutylene (which is a mixture of two isomers of octene). The remaining six fuels were research-grade gasolines of varying formulations.

Abstract The high-frequency noise issue is one of the most significant noise, vibration, and harshness problems, particularly in battery electric vehicles (BEVs). The sound package treatment is one of the most important approaches toward solving this problem. Owing to the limitations imposed by manufacturing error, assembly error, and the operating conditions, there is often a big difference between the actual values and the design values of the sound package components. Therefore, the sound package parameters include greater uncertainties. In this article, an uncertainty analysis method for BEV interior noise was developed based on an interval model to investigate the effect of sound package uncertainty on the interior noise of a BEV. An interval perturbation method was formulated to compute the uncertainty of the BEV’s interior noise.

Abstract Gasoline Particulate Filter (GPF) technology is the key method of meeting the new regulations for particulate matter emissions from gasoline cars. Computer-Aided Engineering is widely used for the design of such systems; thus the development of accurate models for GPFs is crucial. Most existing pressure loss models require experimental calibration of several parameters. These experiments are performed at room temperatures, or on an engine test bench, where gas properties cannot be fully controlled. This article presents pressure loss measurements for clean GPF cores performed with uniform airflow and temperatures up to 680°C. The flow regime in GPF is shown to be different to that in the Diesel Particulate Filters (DPF) due to high flow rates and temperatures. Therefore, most of the existing models are not suitable for design of the new generation of aftertreatment devices. To separate pressure loss contribution from different sources, unplugged filter cores are tested.

Abstract The external driving environment of an autonomous driving vehicle is complex and changeable. In this article, the trajectory tracking control with obstacle avoidance based on model predictive control was presented. Specifically, double-level control scheme by controlling the front steering angle was used in our research, and the double level is composed of the high level of model predictive controller for local trajectory planning and low level of model predictive controller for trajectory tracking. At high level, the local trajectory planner based on the point-mass model was designed. Then, at low level, the linear time-varying vehicle dynamics model was presented, and the trajectory tracking controller was proposed considering control variable, control increment, and output constraint. Finally, the trajectory tracking performance was tested in co-simulation environment with CarSim and Simulink, and the tracking errors were analyzed.

Abstract Recently, high-resolution sensors capable of multiple detections (MDs) per object are available for perception applications in autonomous or semi-autonomous vehicles. Conventional multi-target tracking (MTT) approaches start with the point-target assumption and thus cannot be applied directly to the MDs of high-resolution sensors. A popular solution widely used in literature starts with a measurement partitioning approach, followed by repurposing conventional tracking algorithms to accommodate the resulting partitions. However, the computational requirement increases combinatorially, especially under multi-sensor applications that also independently return multiple radar reflections as in the automotive radar sensors used in this work. Thus, a hybrid approach that combines a clustering technique (such as DBSCAN) to alleviate the computational complexity and an MD tracking scheme that admits multiplicity of the target detections is employed.

Abstract The promise and potential for a future of automated vehicles (AVs) remains great, with safety and societal transformations that may rival the original introduction of the automobile. Yet an inability for industry and governments to define what it means for an AV to drive safely has tempered enthusiasm and risks causing a “winter of AV” just like the one that affected Artificial Intelligence technologies decades ago, which is only now being overcome. Towards this end, the Responsibility-Sensitive Safety (RSS) model was introduced as an open and transparent white-box, an interpretable and scalable formal model that defines minimum safety requirements based on reasonable assumptions of others, balancing safety and usefulness for automated driving vehicles.

Abstract Traditionally, Electronic Control Units (ECUs) in vehicles have been left unsecured. Ensuring cybersecurity in an ECU network is challenging as there is no centralized authority in the vehicle to provide security as a service. While progress has been made to address cybersecurity vulnerabilities, many of these approaches have focused on enterprise, software-centric systems and require more computational resources than typically available for onboard vehicular devices. Furthermore, vehicle networks have the additional challenge of mitigating security vulnerabilities while satisfying safety and performance constraints. This article introduces a blockchain framework to detect unauthorized modifications to vehicle ECUs. A proof of concept blockchain prototype framework is implemented on a set of microprocessors (comparable to those used by simple ECUs) as a means to assess the efficacy of using our blockchain approach to detect unauthorized updates.