Search Results

Abstract Truck platooning comprises a number of trucks equipped with automated lateral and longitudinal vehicle control technology, which allows them to move in tight formation with short following distances. This study is an initial step toward developing an understanding of the occupant injury risks associated with the multiple sequential impacts between truck platoons and roadside safety barriers, regardless of whether the crash is associated with a malfunction of automated control or human operation. Full-scale crash impacts of a tractor-trailer platoon into a concrete bridge guardrail were simulated for a specific Test Level condition according to the Manual for Assessing Safety Hardware (MASH) standards. The model of the bridge barrier was developed based on its drawings, and material properties were assigned according to literature data.

Abstract ISO 26262 needs to be considered when developing safety-relevant E/E systems within the automotive industry. One part of the development process according to ISO 26262 is the derivation of the safety requirements for component functions. Here, one attribute of the safety requirements is the Automotive Safety Integrity Level (ASIL). The ASIL at a component level can be determined using ASIL allocation and decomposition. Considering complex systems such as vehicle electrical systems, countless possibilities can be identified for how the ASILs at a component level can be assigned in line with safety goals. In terms of efficiency, manual assignment is not expedient. Therefore, an algorithm for automated assignment of the ASILs will be introduced which considers constraints based on a fault tree analysis. The function of the approach will be demonstrated using the example of a vehicle electrical system from an automated vehicle.

Abstract Tire-pavement interaction noise (TPIN) dominates for passenger cars above 40 km/h and trucks above 70 km/h. Numerous studies have attempted to uncover and distinguish the basic mechanisms of TPIN. However, intense debate is still ongoing about the validity of these mechanisms. In this work, the physical mechanisms proposed in the literature were reviewed and divided into three categories: generation mechanisms, amplification mechanisms, and attenuation mechanisms. The purpose of this article is to gather the published general opinions for further open discussions.

Abstract The innovations of vehicle connectivity have been increasing dramatically to enhance the safety and user experience of driving, while the rising numbers of interfaces to the external world also bring security threats to vehicles. Many security countermeasures have been proposed and discussed to protect the systems and services against attacks. To provide an overview of the current states in this research field, we conducted a systematic mapping study (SMS) on the topic area “security countermeasures of in-vehicle communication systems.” A total of 279 papers are identified based on the defined study identification strategy and criteria. We discussed four research questions (RQs) related to the security countermeasures, validation methods, publication patterns, and research trends and gaps based on the extracted and classified data. Finally, we evaluated the validity threats and the whole mapping process.

Abstract The main focus of this article is the online estimation of the terminal airspace sector capacity from the Air Traffic Controller 0ATC) dynamical neural model using Neural Partial Differentiation (NPD) with permissible safe separation and affordable workload. For this purpose, a primarily neural model of a multi-input-single-output (MISO) ATC dynamical system is established, and the NPD method is used to estimate the model parameters from the experimental data. These estimated parameters have a less relative standard deviation, and hence the model validation results show that the predicted neural model response is well matched with the intervention of the ATC workload. Moreover, the proposed neural network-based approach works well with the experimental data online as it does not require the initial values of model parameters, which are unknown in practice.

Abstract The Friction stir welding (FSW) is recently presented so to join different materials without the melting process as a solid-state joining technique. A widely application for the FSW process is recently developed in automotive industries. To create the welded components by using the FSW, the plunged probe and shoulder as welding tools are used. The Finite Element Method (FEM) can be used so to simulate and analyze material flow during the FSW process. As a result, thermal and mechanical stresses on the workpiece and welding tool can be analyzed and decreased. Effects of the welding process parameters such as tool rotational speed, welding speed, tool tilt angle, depth of the welding tool, and tool shoulder diameter can be analyzed and optimized so to increase the efficiency of the production process. Material characteristics of welded parts such as hardness or grain size can be analyzed so to increase the quality of part production.

Abstract This article analyses the flow field between two 1/8-scale Generalized European Transport System (GETS) models which are placed in a two-vehicle platoon at close distances. Numerical simulations using the lattice Boltzmann method together with a wind tunnel experiment (open jet facility, OJF) were executed. Next, to balance measurements, coaxial volumetric velocimetry (CVV) measurements were performed to obtain information about the flow field. Three intervehicle distances, 0.10, 0.45 and 0.91 times the vehicle length, were tested for various platoon configurations where the vehicles in the platoon varied in terms of front-edge radius and the addition of tails. At the smallest intervehicle distance, the greatest reductions in drag were found for both the leading and trailing vehicles. The flow in the gap between the two vehicles follows an S-shaped path with small variations between the configurations.

Abstract Automated guided vehicle systems (AGVS) are the prominent one in modern material handling systems used in small manufacturing enterprises (SMEs) due to their exciting features and benefits. This article pinpoints the need of AGVS in SMEs by describing the material handling selection in SMEs and enlightening recent technological developments and approaches of the AGVS. Additionally, it summarizes the analytical and simulation-based tools utilized in design problems of AGVS along with the influence of material handling management and key hurdles of AGVS. The current study provides a limelight towards making smart automated guided vehicles (AGVs) with the simplified and proper routing system and favorable materials and more importantly reducing the cost and increasing the flexibility.

Abstract Today’s explosive condition of the market is compelling the manufacturing organizations to switch from traditional manufacturing (TM) to lean manufacturing (LM) to create a footprint in this competitive era. In this article, 16 critical success factors (CSFs) for LM implementation are identified through a vast literature review, the opinion of academicians and industry experts and interpretive structural modeling (ISM) is used to create interrelationships among the identified CSFs, and interpretive ranking process (IRP) rank these CSFs based on dominance with respect to performance dimensions. Leadership and management made the foundation of an ISM model while the training and people development have secured the first rank in the IRP model. Implementation of such ISM- and IRP-based models of CSF would give a clear understanding of these CSFs so that LM researchers, decision-makers, managers, and practitioners of LM will use their resources more efficiently.

Abstract The advancement towards development of autonomy follows either the bottom-up approach of gradually improving and expanding existing Advanced Driver Assist Systems (ADAS) technology where the driver is present in the control loop or the top-down approach of directly developing autonomous vehicle hardware and software using alternative approaches without the driver present in the control loop. Most ADAS systems today fall under the classification of SAE Level 1 which is also referred to as the driver assistance level. The progression from SAE Level 1 to SAE Level 2 or partial automation involves the critical task of merging automated lateral control and automated longitudinal control such that the tasks of steering and acceleration/deceleration are not required to be handled by the driver under certain conditions [1].

Abstract Future SAE Level 4 and Level 5 autonomous vehicles (AV) will require novel applications of localization, perception, control, and artificial intelligence technology in order to offer innovative and disruptive solutions to current mobility problems. This article concentrates on low-speed autonomous shuttles that are transitioning from being tested in limited traffic, dedicated routes to being deployed as SAE Level 4 automated driving vehicles in urban environments like college campuses and outdoor shopping centers within smart cities. The Ohio State University has designated a small segment in an underserved area of the campus as an initial AV pilot test route for the deployment of low-speed autonomous shuttles. This article presents initial results of ongoing work on developing solutions to the localization and perception challenges of this planned pilot deployment.

Abstract To evaluate that automated vehicle is as safe as a human driver, a following question is studied: how does an automated vehicle react under extreme conditions close to collision? In order to understand the collision avoidance capability of an automated vehicle, we should analyze not only such post-extreme condition behavior but also pre-extreme condition behavior. We present a theory to analyze the collision avoidance capability of automated driving technologies. We also formulate a collision avoidance equation on the theory. The equation has two types of solutions: response driving plans and preparation driving plans. The response driving plans are supported by response strategy on which the vehicle reacts after detection of a hazard and they are highly efficient in terms of travel time.

Abstract This study proposes a method for the rapid detection and location of cavity defects in ballastless track structures of high-speed railways in service. First, the propagation law of air-coupled ultrasonic Lamb waves in the ballastless track structure is studied. Theoretical calculation results show that the ultrasonic Lamb wave group velocity of the A2 mode in the track plate is 4000 m/s. Then, the excitation and reception methods of the air-coupled ultrasound are studied. Theoretical and experimental results show that the A2 mode Lamb wave can be generated by the 3.8° oblique incidence of the ballastless track structure. Finally, an experimental system for air-coupled ultrasonic testing is constructed. A pair of air-coupled ultrasonic probes is used to provide excitation and reception Lamb wave signals at an inclined angle of 3.8°, 20 mm away from the surface of the track plate, and 40 mm/step along the scanning direction.

Abstract To gain a better understanding of the characteristics of corrugation, including the development and propagation of corrugation, and impact of vehicle and track dynamics, a computational model was established, taking into account the nonlinearity of vehicle-track coupling. The model assumes a fixed train speed of 300 km/h and accounts for vertical interaction force components and rail wear effect. Site measurements were used to validate the numerical model. Computational results show that (1) Wheel polygonalisation corresponding to excitation frequency of 545-572 Hz was mainly attributed to track irregularity and uneven stiffness of under-rail supports, which in turn leads to vibration modes of the bogie and axle system in the frequency range of 500-600 Hz, aggregating wheel wear. (2) The peak response frequency of rail of the non-ballasted track coincides with the excitation frequency of wheel-rail coupling; the resonance results in larger wear amplitude of the rail.

Abstract Management of expressway networks has been mainly focused on defect management without looking at the correlations with accidental risks. This causes unsustainability in expressway infrastructure maintenance since such defects may not be a contributing factor toward public safety. Thus it is necessary to incorporate accidental events for decision-making in infrastructure management. This study has developed a novel approach to machine learning (ML) that incorporates actual primary data from the last 10 years of single-vehicle accidents (SVA) by collisions with motorway facilities, or so-called single-vehicle collisions with fixed objects. The ML is firstly aimed at identifying the influential factors of SVA in relation to finding effective countermeasures for accidents by integrating the correlation analysis, multiple regression analysis, and ML techniques. The study reveals that wet pavement conditions have a significant effect on SVA.

Abstract A wind-tunnel study was undertaken to investigate the drag reduction potential of two-truck platooning, in the context of understanding some of the factors that may influence the potential fuel savings and greenhouse-gas reductions. Testing was undertaken in the National Research Council Canada 2 m × 3 m Wind Tunnel with two 1/15-scale models of modern aerodynamic tractors paired with dry-van trailers configured with and without combinations of side-skirts and boat-tails. Separation distances of 0.14, 0.28, 0.49, 0.70 and 1.04 vehicle lengths were tested (3 m, 6 m, 10.5 m, 15 m, and 22.5 m full scale). Additionally, within-lane lateral offsets up to 0.31 vehicle widths (0.8 m full scale) were evaluated, along with a full-lane offset of 1.42 vehicle widths (3.7 m full scale). This study has made use of a wind-averaged-drag coefficient as the primary metric for evaluating the effect of vehicle platooning.

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Abstract Connected vehicle technology consisting of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication falls under the umbrella of V2X, or Vehicle-to-Everything, communication. This enables vehicles and infrastructure to exchange safety-related information to enable smarter, safer roads. If driver alerts are raised or automated action is taken as a result of these messages, it is critical that messages are trustworthy and reliable. To this end, the Security Credential Management System (SCMS) and Cooperative Intelligent Transportation Systems (C-ITS) Credential Management System (CCMS) have been proposed to enable authentication and authorization of V2X messages without compromising individual user privacy. This is accomplished by issuing each vehicle a large set of “pseudonyms,” unrelated to any real-world identity. During operation, the vehicle periodically switches pseudonyms, thereby changing its identity to others in the network.

Abstract Truck crashes on Wyoming mountain passes due to brake heating has been a long-standing issue due to the steep downgrades that characterize some routes in the state. The grade severity rating system (GSRS) developed by the Federal Highway Administration (FHWA) to recommend maximum safe speeds has been identified as a viable countermeasure to reducing the incidence of downgrade truck crashes. However, several decades have passed since the GSRS was developed. In the decades since its development, truck features have undergone radical changes in terms of design. The streamlined design of tractors and trailers, use of drag reduction devices, changes in engine features, and adoption of radial tires have led to a reduction in the non-brake forces that retard motion. Truck brakes have also changed along with retarder characteristics. This has meant that maximum safe speeds recommended by the GSRS have been conservative.

Abstract The development of automated driving systems (ADS) necessitates procedures to validate system safety. The reliability of an ADS’s environment perception provided by lidar, radar, and camera sensors is of special interest in this context, because perception errors can be safety-critical. In this article, we formalize the reliability-based validation of environment perception for safe automated driving and discuss associated challenges. We describe a potential solution to a perception reliability validation by deriving performance requirements at the sensor level. We then summarize statistical methods to learn sensor perception reliabilities in field tests, on proving grounds, and through virtual simulations. With the developed safety validation framework, we show that, potentially, one can validate the safety of an ADS with feasible test effort.