Search Results

Abstract Taking the semi-active suspension system as the research object, the forward model and inverse model of a continuous damping control (CDC) damper are established based on the characteristic test of the CDC damper. A multi-mode semi-active suspension controller is designed to meet the diverse requirements of vehicle performance under different road conditions. The controller parameters of each mode are determined using a genetic algorithm. In order to achieve automatic switching of the controller modes under different road conditions, a method is proposed to identify the road roughness based on the sprung mass acceleration. The average of the ratio between the squared sprung mass acceleration and the vehicle speed within a specific time window is taken as the identification indicator for road roughness.

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 In order to efficiently predict and investigate a vehicle’s vertical dynamics, it is necessary to consider the suspension component properties holistically. Although the effects of suspension stiffness and damping characteristics on vertical dynamics are widely understood, the impact of suspension friction in various driving scenarios has rarely been studied in both simulation and road tests for several decades. The present study addresses this issue by performing driving tests using a special device that allows a modification of the shock absorber or damper friction, and thus the suspension friction to be modified independently of other suspension parameters. Initially, its correct functioning is verified on a shock absorber test rig. A calibration and application routine is established in order to assign definite additional friction forces at high reproducibility levels.

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 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 After a severe lane change, a wind gust, or another disturbance, the driver might be unable to recover the intended motion. Even though this fact is known by any driver, the scientific investigation and testing on this phenomenon is just at its very beginning, as a literature review, focusing on SAE Mobilus® database, reveals. We have used different mathematical models of car and driver for the basic description of car motion after a disturbance. Theoretical topics such as nonlinear dynamics, bifurcations, and global stability analysis had to be tackled. Since accurate mathematical models of drivers are still unavailable, a couple of driving simulators have been used to assess human driving action. Classic unstable motions such as Hopf bifurcations were found. Such bifurcations seem almost disregarded by automotive engineers, but they are very well-known by mathematicians. Other classic unstable motions that have been found are “unstable limit cycles.”

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 To realize the dynamics concept “enjoy driving” of new-model cars, engine sound was based on the concept of “exhilarating.” To achieve “exhilarating,” we compared current models with competitor cars to understand the countermeasure sound characteristics. As a result, it was found that the rumble noise at low-RPM medium load needs to be reduced. To reduce rumble noise, the crankshaft system and power train stiffness were refined. As a result, we were able to achieve our goal of exhilarating engine sound. However, as the evaluation of sound after a vehicle is sold is generally left to the user, there are few studies that examine whether a car is more highly evaluated based on the sounds it creates. Therefore, this study was conducted to evaluate concept compatibility and loyalty in relation to exhilarating engine sound in the U.S. market for Generation Z, the target group for the new car.

Abstract A time domain analysis method of ride comfort and energy dissipation characteristics is proposed for automotive vibration proportional–integral–derivative (PID) control. A two-degrees-of-freedom single wheel model for automotive vibration control is established, and the conventional vibration response variables for ride comfort evaluation and the energy consumption vibration response variables for energy dissipation characteristics evaluation are determined, and the Routh stability criterion method was introduced to assess the impact of PID control on vehicle stability. The PID control parameters are tuned using the differential evolution algorithm, and to improve the algorithm’s adaptive ability, an adaptive operator is introduced, so that the mutation factor of differential evolution algorithm can change with the number of iterations.

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 Being an engineer-to-order (ETO) operating industry, the control cabinet industry faces difficulties in process and workplace optimizations due to changing requirements and lot size one combined with volatile orders. To optimize workplaces for employees, current literature is focusing on ergonomic designs, providing frameworks to analyze workplaces, leaving out the optimal design for productivity. This work thus utilizes a Kano analysis, collecting empirical data to identify essential design requirements for assembly workplaces, incorporating input from switchgear manufacturing employees. The results emphasize the need for a balance between ergonomics and efficiency in workplace design. Surprisingly, few participants agree on the correlation between improved processes and workspaces having a positive impact on their well-being and product quality.

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 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 The uncertainty of a driver’s state, the variability of the traffic environment, and the complexity of road conditions have made driving behavior a critical factor affecting traffic safety. Accurate predicting of driving behavior is therefore crucial for ensuring safe driving. In this research, an efficient framework, distilled routing transformer (DRTR), is proposed for driving behavior prediction using multiple modality data, i.e., front view video frames and vehicle signals. First, a cross-modal attention distiller is introduced, which distills the cross-modal attention knowledge of a fusion-encoder transformer to guide the training of our DRTR and learn deep interactions between different modalities. Second, since the multi-modal learning usually requires information from the macro view to the micro view, a self-attention (SA)-routing module is custom-designed for SA layers in DRTR for dynamic scheduling of global and local attentions for each input instance.

Abstract Adaptive neural networks (ANNs) have become famous for modeling and controlling dynamic systems. However, because of their failure to precisely reflect the intricate dynamics of the system, these have limited use in practical applications and perform poorly during training and testing. This research explores novel approaches to this issue, including modifying the simple neuron unit and developing a generalized neuron (GN). The revised version of the neuron unit helps to develop the system controller, which is responsible for providing the desired control signal based on the inputs received from the dynamic responses of the vehicle suspension system. The controller is then tested and evaluated based on the performance of the magnetorheological (MR) damper for the main suspension system.