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Abstract For taking counter measures in advance to prevent accidental risks, it is of significance to explore the causes and evolutionary mechanism of ship collisions. This article collects 70 ship collision accidents in Zhejiang coastal waters, where 60 cases are used for modeling while 10 cases are used for verification (testing). By analyzing influencing factors (IFs) and causal chains of accidents, a Bayesian network (BN) model with 19 causal nodes and 1 consequential node is constructed. Parameters of the BN model, namely the conditional probability tables (CPTs), are determined by mathematical statistics methods and Bayesian formulas. Regarding each testing case, the BN model’s prediction on probability of occurrence is above 80% (approaching 100% indicates the certainty of occurrence), which verifies the availability of the model. Causal analysis based on the backward reasoning process shows that H (Human error) is the main IF resulting in ship collisions.

Abstract Compared to other age groups, older adults are at more significant risk of hip fracture when they fall. In addition to the higher risk of falls for the elderly, fear of falls can reduce this population’s outdoor activity. Various preventive solutions have been proposed to reduce the risk of hip fractures ranging from wearable hip protectors to indoor flooring systems. A previously developed rubberized asphalt mixture demonstrated the potential to reduce the risk of head injury. In the current study, the capability of the rubberized asphalt sample was evaluated for the risk of hip fracture for an average elderly male and an average elderly female. A previously developed human body model was positioned in a fall configuration that would give the highest impact forces toward regular asphalt.

Abstract Super duplex stainless steel (SDSS) is a type of stainless steel made of chromium (Cr), nickel (Ni), and iron (Fe). In the present work, a 1.6 mm wide thin sheet of SDSS is joined using gas tungsten arc welding (GTAW). The ideal parameter for a bead-on-plate trial is found, and 0.216 kJ/mm of heat input is used for welding. As an outcome of the welding heating cycle and subsequent cooling, a microstructural study revealed coarse microstructure in the heat-affected zone and weld zone. The corrosion rate for welded joints is 9.3% higher than the base metal rate. Following the corrosion test, scanning electron microscope (SEM) analysis revealed that the welded joint’s oxide development generated a larger corrosive attack on the weld surface than the base metal surface. The percentages of chromium (12.5%) and molybdenum (24%) in the welded joints are less than those in the base metal of SDSS, as per energy dispersive X-ray (EDX) analysis.

Abstract Testing and verifying the security of connected and autonomous vehicles (CAVs) under cyber-physical attacks is a critical challenge for ensuring their safety and reliability. Proposed in this article is a novel testing framework based on a model of computation that generates scenarios and attacks in a closed-loop manner, while measuring the safety of the unit under testing (UUT), using a verification vector. The framework was applied for testing the performance of two cooperative adaptive cruise control (CACC) controllers under false data injection (FDI) attacks. Serving as the baseline controller is one of a traditional design, while the proposed controller uses a resilient design that combines a model and learning-based algorithm to detect and mitigate FDI attacks in real-time.

Abstract The use of appropriate loads and regulations is of great importance in weld fatigue assessment of rail on-track maintenance equipment and similar vehicles for optimized design. The regulations and available loads, however, are often generalized for several categories, which proves to be overly conservative for some specific categories of machines. EN (European Norm) and AAR (Association of American Railroads) regulations play a pivotal role in determining the applicable loads and acceptance criteria within this study. The availability of track-induced fatigue load data for the cumulative damage approach in track maintenance machines is often limited. Consequently, the FEA-based validation of rail track maintenance equipment often resorts to the infinite life approach rather than cumulative damage approach for track-induced travel loads, resulting in overly conservative designs.

Abstract The emerging use of rechargeable batteries in electric and hybrid electric vehicles and distributed energy systems, and accidental fires involving batteries, has heightened the need for a methodology to determine the root cause of the fire. When a fire involving batteries takes place, investigators and engineers need to ascertain the role of batteries in that fire. Just as with fire in general, investigators need a framework for determining the role that is systematic, reliant on collection and careful analysis of forensic evidence, and based on the scientific method of inquiry. This article presents a systematic scientific process to analyze batteries that have been involved in a fire. It involves examining Li-ion cells of varying construction, using a systematic process that includes visual inspection, x-ray, CT scan, and possibly elemental analysis and testing of exemplars.

Abstract This article presents a novel approach to optimize the placement of light detection and ranging (LiDAR) sensors in autonomous driving vehicles using machine learning. As autonomous driving technology advances, LiDAR sensors play a crucial role in providing accurate collision data for environmental perception. The proposed method employs the deep deterministic policy gradient (DDPG) algorithm, which takes the vehicle’s surface geometry as input and generates optimized 3D sensor positions with predicted high visibility. Through extensive experiments on various vehicle shapes and a rectangular cuboid, the effectiveness and adaptability of the proposed method are demonstrated. Importantly, the trained network can efficiently evaluate new vehicle shapes without the need for re-optimization, representing a significant improvement over classical methods such as genetic algorithms.

Abstract In order to enhance the path-tracking accuracy and adaptability of the electric flatbed vehicle (EFV) in the soft-target vehicle test system, an improved controller is designed based on the linear quadratic regulator (LQR) algorithm. First, the LQR feedback controller is designed based on the EFV dynamics tracking error model, and the genetic algorithm is utilized to obtain the optimal weight coefficient matrix for different speeds. Second, a weight coefficient matrix compensation strategy is proposed to address the changes in the relationship between the vehicle–road position and attitude caused by external disturbances and the state of EFV. An offline parameter table is created to reduce the computational load on the microcontroller of EFV and enhance real-time path-tracking performance. Furthermore, an adaptive preview time control strategy is added to reduce the overshooting caused by control delay. This strategy is based on road curvature and traveling speed.

Abstract Composite ceramic brake discs are made of ceramic material reinforced with carbon fibers and offer exceptional advantages that translate directly into higher vehicle performance. In the case of an electric vehicle, it could increase the range of the vehicle, and in the case of conventional internal combustion engine vehicles, it means lower fuel consumption (and consequently lower CO2 emissions). These discs are typically characterized by complex internal geometries, further complicated by the presence of drilling holes on both friction surfaces. To estimate the aerothermal performance of these discs, and for the thermal management of the vehicle, a reliable model for predicting the air flowing across the disc channels is needed. In this study, a real carbon-ceramic brake disc with drilling holes was investigated in a dedicated test rig simulating the wheel corner flow conditions experimentally using the particle image velocimetry technique and numerically.

Abstract To address the challenge of directly measuring essential dynamic parameters of vehicles, this article introduces a multi-source information fusion estimation method. Using the intelligent front camera (IFC) sensor to analyze lane line polynomial information and a kinematic model, the vehicle’s lateral velocity and sideslip angle can be determined without extra sensor expenses. After evaluating the strengths and weaknesses of the two aforementioned lateral velocity estimation techniques, a fusion estimation approach for lateral velocity is proposed. This approach extracts the vehicle’s lateral dynamic characteristics to calculate the fusion allocation coefficient. Subsequently, the outcomes from the two lateral velocity estimation techniques are merged, ensuring rapid convergence under steady-state conditions and precise tracking in dynamic scenarios.

Abstract In this study, a novel assessment approach of in-vehicle speech intelligibility is presented using psychometric curves. Speech recognition performance scores were modeled at an individual listener level for a set of speech recognition data previously collected under a variety of in-vehicle listening scenarios. The model coupled an objective metric of binaural speech intelligibility (i.e., the acoustic factors) with a psychometric curve indicating the listener’s speech recognition efficiency (i.e., the listener factors). In separate analyses, two objective metrics were used with one designed to capture spatial release from masking and the other designed to capture binaural loudness. The proposed approach is in contrast to the traditional approach of relying on the speech recognition threshold, the speech level at 50% recognition performance averaged across listeners, as the metric for in-vehicle speech intelligibility.

Abstract Single point incremental forming (SPIF) is a robust and new technique. In the recent research scenario, materials properties such as microstructure, micro-texture analysis, and crystal structure can be accessed through characterization non-destructive techniques, e.g., scanning electron microscope (SEM), electron backscattered diffraction (EBSD), and X-ray diffraction (XRD). XRD is a non-destructive method for analyzing the fine structure of materials. This study explores how process variables such as wall angle, step size, feed rate, and forming speed affect the parts of large-, medium-, and small-sized truncated cones of aluminum alloy AA3003-O sheet. Several cone parts of truncated cones are used in this investigation to implement Scherrer’s method. The two primary determining factors peak height and crystallite size are assessed for additional analysis in the present research.

Abstract Neutron diffraction is a powerful tool for noninvasive and nondestructive characterization of materials and can be applied even in large devices such as internal combustion engines thanks to neutrons’ exceptional ability to penetrate many materials. While proof-of-concept experiments have shown the ability to measure spatially and temporally resolved lattice strains in a small aluminum engine on a timescale of minutes over a limited spatial region, extending this capability to timescales on the order of a crank angle degree over the full volume of the combustion chamber requires careful design and optimization of the engine structure to minimize attenuation of the incident and diffracted neutrons to maximize count rates.

Abstract The sinking and trimming of the hull in the channel would directly affect the handling and navigation safety of the ship. In view of the ship sinking, a series of empirical formulas to estimate the subsidence have been put forward for vessel in spacious shallow water areas. However, most of the equations are based on seagoing vessels. They are not suitable for inland ships with small scales, shallow drafts, and narrow navigation width. Till now, research on ship squat in intermediate channel has not yielded more practical results. Here, a generalized physical model is used to study the sinking of 500t class ships in restricted intermediate channel under different channel widths, water depths, and speeds. The main factors affecting the squat are analyzed, the empirical relation is compared with the measured squat. The Barrass equation is modified, and the calculation relation of the settlement suitable for inland river ships is proposed.

Abstract A research program has been launched in Iran to develop an evaluation method for comparing the safety performance of vehicles in real-world collisions with crash test results. The goal of this research program is to flag vehicle models whose safety performance in real-world accidents does not match their crash test results. As part of this research program, a metric is needed to evaluate the severity of side impacts in crash tests and real-world accidents. In this work, several vehicle-based metrics were analyzed and calculated for a dataset of more than 500 side impact tests from the NHTSA crash test database. The correlation between the metric values and the dummy injury criteria was studied to find the most appropriate metric with the strongest correlation coefficient values with the dummy injury criteria.

Abstract This study investigates the use of a road weather model (RWM) as a virtual sensing technique to assist autonomous vehicles (AVs) in driving safely, even in challenging winter weather conditions. In particular, we investigate how the AVs can remain within their operational design domain (ODD) for a greater duration and minimize unnecessary exits. As the road surface temperature (RST) is one of the most critical variables for driving safety in winter weather, we explore the use of the vehicle’s air temperature (AT) sensor as an indicator of RST. Data from both Road Weather Information System (RWIS) stations and vehicles measuring AT and road conditions were used. Results showed that using only the AT sensor as an indicator of RST could result in a high number of false warnings, but the accuracy improved significantly with the use of an RWM to model the RST.

Abstract The closet in-path vehicle (CIPV) is recognized relying on the detection results for road lane lines in most current ACC system, which may not work well in the poor conditions, for example, unclear road lane lines, low light level, bad weather, and so on. To solve this problem, the article proposes a sensor fusion-based CIPV recognition algorithm independent of road lane lines. First, a robust Kalman filter based on the global coordinate system is designed to fuse the millimeter-wave radar and camera targets. The fusion algorithm can dynamically adjust the covariance matrix of sensor observations to avoid the influence of anomalous observations on the fusion results. Stable detection of targets by the fusion algorithm is the basis of the CIPV recognition algorithm.

Abstract In the pursuit of advancing autonomous vehicles (AVs), data-driven algorithms have become pivotal in replacing human perception and decision-making. While deep neural networks (DNNs) hold promise for perception tasks, the potential for catastrophic consequences due to algorithmic flaws is concerning. A well-known incident in 2016, involving a Tesla autopilot misidentifying a white truck as a cloud, underscores the risks and security vulnerabilities. In this article, we present a novel threat model and risk assessment (TARA) analysis on AV data storage, delving into potential threats and damage scenarios. Specifically, we focus on DNN parameter manipulation attacks, evaluating their impact on three distinct algorithms for traffic sign classification and lane assist.

Abstract Computational and experimental studies have been undertaken to investigate injurious head-first impacts (HFI), which can occur during automotive rollovers. Recent studies assume a torso surrogate mass (TSM) boundary condition, wherein the first or first two thoracic vertebrae are potted and constrained to only move in the vertical loading direction. The TSM boundary condition has not been compared with a full body (FB) model computationally or experimentally for HFI. In this study, the Global Human Body Models Consortium 50th percentile male detailed human body model (M50-O, Version 6.0) was applied to compare the kinematic, kinetic, and injury response of an HFI with a TSM boundary condition (M50-TSM), and a full body boundary condition (M50-FB). Impacts (to M50-TSM and M50-FB) were simulated between the head and a rigid plate using a commercial FE code (LS-DYNA).

Abstract Rib fractures are associated with high rates of morbidity and mortality. Improved methods to assess rib bone quality are needed to identify at-risk populations. Quantitative computed tomography (QCT) can be used to calculate volumetric bone mineral density (vBMD) and bone mineral content (BMC), which may be related to rib fracture risk. The objective of this study was to determine if vBMD and BMC from QCT predict human rib structural properties. 127 mid-level (5th–7th) ribs were obtained from adult female (n = 67) and male (n = 60) postmortem human subjects (PMHS). Isolated rib QCT scans were performed to calculate vBMD and BMC.