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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 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 A critical component of vehicle dynamic control systems is the accurate and real-time knowledge of the vehicle’s key states and parameters when running on the road. Such knowledge is also essential for vehicle closed-loop feedback control. Vehicle state and parameter estimation has gradually become an important way to soft-sense some variables that are difficult to measure directly using general sensors. In this work, a seven degrees-of-freedom (7-DOF) nonlinear vehicle dynamics model is established, where consideration of the Magic formula tire model allows us to estimate several vehicle key states using a hybrid algorithm containing an unscented Kalman filter (UKF) and a genetic algorithm (GA). An estimator based on the hybrid algorithm is compared with an estimator based on just a UKF. The results show that the proposed estimator has higher accuracy and fewer computation requirements than the UKF estimator.

Abstract Advanced passenger vehicles are complex dynamic systems that are equipped with several actuators, possibly including differential braking, active steering, and semi-active or active suspensions. The simultaneous use of several actuators for integrated vehicle motion control has been a topic of great interest in literature. To facilitate this, a technique known as control allocation (CA) has been employed. CA is a technique that enables the coordination of various actuators of a system. One of the main challenges in the study of CA has been the representation of actuator dynamics in the optimal CA problem (OCAP). Using model predictive control allocation (MPCA), this problem has been addressed. Furthermore, the actual dynamics of actuators may vary over the lifespan of the system due to factors such as wear, lack of maintenance, etc. Therefore, it is further required to compensate for any mismatches between the actual actuator parameters and those used in the OCAP.

Abstract Sustainable practices are taking precedence across many industries, as evident from their shift towards the use of environmentally responsible materials, such as natural fiber-reinforced acrylated epoxidized soybean oil (NF-AESO). However, due to the lower reactivity of AESO, the curing reaction usually requires higher temperatures and longer curing time (e.g., 150°C for 6-12 h), thus making the entire process unsustainable. In this study, we demonstrate the potential power of photons towards manufacturing NF-AESO composites in a sustainable manner at room temperature (RT) within 10 min. Two photoinitiators, i.e., the 2,2-dimethoxy phenylacetophenone (DMPA) and 1-hydroxycyclohexyl phenyl ketone (HCPK), were evaluated and compared with the thermal initiator, i.e., tert-butyl perbenzoate (TBPB). Based on the mechanical performance of the AESOs, the photoinitiation system for NF-AESO was optimized.

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 Current End-of-Life Vehicle (ELV) recycling processes are mainly based on mechanical separation techniques. These methods are designed to recycle those metals with the highest contribution in the vehicle weight such as steel, aluminum, and copper. However, a conventional vehicle uses around 50 different types of metals, some of them considered critical by the European Commission. The lack of specific recycling processes makes that these metals become downcycled in steel or aluminum or, in the worst case, end in landfills. With the aim to define several ecodesign recommendations from a raw material point of view, it is proposed to apply a thermodynamic methodology based on exergy analysis. This methodology uses an indicator called thermodynamic rarity to assess metal sustainability. It takes into account the quality of mineral commodities used in a vehicle as a function of their relative abundance in Nature and the energy intensity required to extract and process them.

Abstract Modern vehicles have incorporated numerous safety-focused advanced driver-assistance systems (ADAS) in the last decade including smart cruise control and object avoidance. In this article, we aim to go beyond using ADAS for safety and propose to use ADAS technology to enable predictive optimal energy management and improve vehicle fuel economy (FE). We combine ADAS sensor data with a previously developed prediction model, dynamic programming (DP) optimal energy management control, and a validated model of a 2010 Toyota Prius to explore FE. First, a unique ADAS detection scope is defined based on optimal vehicle control prediction aspects demonstrated to be relevant from the literature. Next, during real-world city and highway drive cycles in Denver, Colorado, a camera is used to record video footage of the vehicle environment and define ADAS detection ground truth. Then, various ADAS algorithms are combined, modified, and compared to the ground truth results.

Abstract This article proposes a new misfire detection index, the ΔGap slope, for a four-cylinder engine. However, the proposed index is not limited to four-cylinder engines. The ΔGap slope uses the tooth time measured using the existing crankshaft position sensor; therefore, an additional sensor is not required, which makes it economical. The ΔGap slope is defined as the difference between the gap slopes of the same cylinder for two adjacent cycles. Various factors that cause deviations in gap slopes between cylinders can be eliminated in the process of determining the difference between two gap slopes. Hence, in contrast with the existing engine roughness method, the ΔGap slope has the advantage of not requiring compensation for deviations between the cylinders. The conventional gap slope method must use different sets of thresholds for each cylinder located at the same position on the sensor wheel, which results in multiple thresholds being applied.

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 To protect ship equipment of river and sea transport, it is suggested to use polymeric protective coatings based on epoxy diane oligomer ED-20, polyethylene polyamine (PEPA) curing agent and filler, which is a departure from industrial production. Thus the purpose of the work is analysis of major dependency of the properties on the content of fillers that allowed to revealed the critical filler content (furnace black) in composites to form a protective coating with the required set of characteristics. The infrared (IR) spectral analysis was used to investigate the presence of bonds on the surface of particles of the PM-75 furnace black, which allows us to assess the degree of cross-linking of the polymer. The influence of the content of dispersed furnace black on the physicomechanical and thermophysical properties and the structure of the protective coating is investigated.

Abstract Pyrotechnic seat belt pretensioners typically remove 8–15 cm of belt slack and help couple an occupant to the seat. Our study investigated pretensioner deployment on forward-leaning, live volunteers. The forward-leaning position was chosen because research indicates that passengers frequently depart from a standard sitting position. Characteristics of the 3D kinematics of forward-leaning volunteers following pretensioner deployment determines if body size is correlated with subject response. Nine adult subjects (three female), ages 18–43 years old, across a wide range of body sizes (50–120 kg) were tested. The age was limited to young, active adults as pyrotechnic pretensioners can deliver a notable force to the trunk. Subjects assumed a forward-leaning position, with 26 cm between C7 and the headrest, in a laboratory setting that replicated the passenger seat of a vehicle.

Abstract The causes of failure due to cracking in the resistance spot welding of the advanced high-strength steels dual-phase 590 (DP590) were investigated using scanning electron microscopy (SEM), optical microscopy, and the tensile-shear test. The results showed that by increasing the current amount, the formation of the melting zone occurred in the heat-affected zone, leading to the cracking in this area, reducing the tensile strength and decreasing the mechanical properties; the initiation and growth of cracking and failure in this region also happened. In the heat-affected zone, by increasing the current amount with the softening phenomenon, the recrystallized coarse grains also occurred, eventually resulting in the loss of mechanical properties. The results of the tensile-shear test also indicated that by increasing the current up to 12 kA, the strength was raised, but the ductility was reduced.

Abstract Heavy commercial vehicles play an important role in creating the trade and economic balance of countries. Also, the durability and safety of heavy commercial vehicles come to the fore. Heavy commercial vehicles consist of two parts. These are the chassis area with the equipment that allows the vehicle to move and the cabin section where the driver is located. The cabin area is the most important area that ensures the highest level of driver safety. Considering that the production of trucks is increasing day by day, it is inevitable for companies to increase their R&D activities in the field of cabin and cabin suspension systems for much safer, durable, and comfortable trucks. This study aims to determine the safe torque value of the fasteners and their assembly sequence of the Cab Suspension Console, which is one of the most important connection parts in a truck and which can cause a fatal accident by breaking.

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Abstract Statistical machine learning classification methods have been widely used in the fault detection analysis in several engineering domains. This motivates us to provide in this article an overview on the application of these methods in the fault diagnosis strategies and also their successful use in unmanned aerial vehicles (UAVs) systems. Different existing aspects including the implementation conditions, offline design, and online computation algorithms as well as computation complexity and detection time are discussed in detail. Evaluation and validation of these aspects have been ensured by a simple demonstration of the basic classification methods and neural network techniques in solving the fault detection and diagnosis problem of the propulsion system failure of a multirotor UAV. A testing platform of an Hexarotor UAV is completely realized.