Vehicles

Transport automation is increasingly being studied from different perspectives; however, the perceptions of road haulage companies have received less attention. This study explores the views of representatives of small- and medium-sized road haulage companies on transport automation in Finland. We conducted an online survey to gather perceptions of automation, which received 254 responses from representatives of a range of different transport industries. The respondents’ views towards automation were generally negative. The overall view was that automation may not be possible for heavy vehicles in Finland due to the adverse weather and driving conditions. The perception was that road haulage automation is unlikely to occur before 2050 in Finland. The results provide valuable insight for vehicle manufacturers, technology developers, policy makers, and haulage companies. As the road haulage industry is dominated by small- and medium-sized companies, hauliers should be supported in actively implementing new technologies. Full article

The Greater Bay Area (GBA) accounts for a high percentage of pollution due to the large number of internal combustion engines. In the past few decades, there has been a significant increase in internal combustion engines vehicles while electric vehicles have not taken off yet in GBA. To a certain extent, the acceptance of electric vehicles is still questionable from the industrial practitioners and local communities. As such, this research study aims to identify the challenges and opportunities of electric vehicles in GBA to address the future direction of electric vehicles in GBA. In this study, it identifies technology and economy as the main driving forces behind the development of electric vehicles. Furthermore, sustainability, safety, and the life of the batteries may induce the slow adoption of electric vehicles. As expected, the study develops a research agenda and contributes new knowledge in the field of electric vehicle. Full article

Transport service provision in many urban areas is dominated by car users, resulting in several traffic externality issues (e.g., noise, pollution, accidents). This paper investigates the perception and satisfaction of private vehicle (PV) users, including micro-mobility users, during their commute by car in an Eastern European country context. The study used empirical data from a sample of 500 commuters in Budapest, Hungary, between October and November 2020. To achieve a deeper understanding of the motivation and explore the perception of PV users towards using sustainable transport services. For analysis in this study, descriptive statistics and segmentation techniques were applied. The key findings indicate that PV users can be attracted to using sustainable transport by designing the travel service quality to provide the level of service desired by customers. Moreover, the majority (73%) of PV commuters were satisfied or very satisfied with the quality attributes of the car service, assessed on a scale of 1 to 5; at the same time, PV users agreed that using public transport helps towards improving the environment and serves to reduce problems derived from traffic. In addition, various elements influence transport choice; for example, results from ordered logit models (OLMs) indicate that security, relaxation, flexibility and comfort are the main significant attributes influencing PV users’ overall satisfaction with cars. The results suggest the necessity for a segmentation technique in the analysis of travel attitudes and satisfaction aimed at reducing the frequency of existing car use to enhance sustainable transportation. Full article

While robotics have been widely used in many agricultural practices such as harvesting, seeding, cattle monitoring, etc., aquaculture farming is an important, fast-growing sector of agriculture that has not seen significant adoption of advanced technologies such as robotics and the Internet of Things (IoT). In particular, dissolved oxygen (DO) monitoring, a practice in pond aquaculture essential to the health of the fish crops, remains labor-intensive and time-consuming. The Hybrid Aerial Underwater robotiCs System (HAUCS) is an IoT framework that aims to bring transformative changes to pond aquaculture. This paper focuses on the latest development in the HAUCS mobile sensing platform and field deployment. To address some shortcomings with the current implementation, the development of a novel rigid Kirigami-based robotic extension subsystem that can expand the functionality of the HAUCS platform is also being discussed. Full article

In this paper, we propose a line planning and timetabling optimization model considering operation cost and passenger satisfaction based on passenger flow. By comprehensively considering the operation cost and passenger waiting cost, the comprehensive social benefits of the line network operation organization are evaluated from a unified perspective. The passenger flow model based on queuing theory is adopted, which can better describe the relationship between passenger flow change and passenger waiting time. The integrated optimization model of the line network is constructed, and mixed integer quadratic programming is adopted, which has the advantages of accurate results and fast convergence. Through the simulation case analysis, the correctness of the method proposed in this paper is verified. Full article

This study presents the development and analysis of a technique for planning the autonomous vehicle (AV) motion references using sequential optimization. The method determines the trajectory plan, speed and acceleration distributions, and other AV kinematic parameters. The approach combines the basics of the finite element method (FEM) and nonlinear optimization with nonlinear constraints. First, we briefly described the generalization of representing an arbitrary function by finite elements (FE) within a road segment. We chose a one-dimensional FE with two nodes and three degrees of freedom (DOF) in a node corresponding to the 5th-degree polynomial. Next, we presented a method for defining the motion trajectory. The following are considered: the formation of a restricted space for the AV’s allowable maneuvering, the motion trajectory geometry and its relation with vehicle steerability parameters, cost functions and their influences on the desirable trajectory’s nature, and the compliance of nonlinear restrictions of the node parameters with the motion area boundaries. In the second stage, we derived a technique for optimizing the AV’s speed and acceleration redistributions. The model considers possible combinations of objective functions, limiting the kinematic parameters by the tire slip critical speed, maximum speed level, maximum longitudinal acceleration, and critical lateral acceleration. In the simulation section, we compared several variants of trajectories and versions of distributing the longitudinal speed and acceleration curves. The advantages, drawbacks, and conclusions regarding the proposed technique are presented. Full article

Parking road vehicles is one of the most tedious and challenging tasks a human driver performs. Despite the low speeds involved, parking manoeuvres are among the main causes of minor and sometimes major traffic accidents, especially in urban areas where limited parking spaces are available. Furthermore, searching for a parking space wastes time and contributes to unnecessary road occupancy and pollution. This paper is dedicated to the development of an autonomous parking system for on-street parking in urban areas. The system is capable of fully automated parking manoeuvres from drop-off to pick-up zones, thus removing human drivers from the vehicle control loop. The system autonomously navigates to the parking space and parks the vehicle without human intervention. The proposed system incorporates a communication protocol that connects automated vehicles, parking infrastructure, and drivers. Several convenient human–machine interface concepts for efficient system communication and state monitoring have been developed. A methodology for validating the system in real time is proposed, which includes functionality requirements and a description of parallel and perpendicular parking manoeuvres. The proposed pipeline is tested on an electric vehicle platform with automated functions, where successful technological functionality is demonstrated. Full article

Helping vehicles estimate vanishing points (VPs) in traffic environments has considerable value in the field of autonomous driving. It has multiple unaddressed issues such as refining extracted lines and removing spurious VP candidates, which suffers from low accuracy and high computational cost in a complex traffic environment. To address these two issues, we present in this study a new model to estimate VPs from a monocular camera. Lines that belong to structured configuration and orientation are refined. At that point, it is possible to estimate VPs through extracting their corresponding vanishing candidates through optimal estimation. The algorithm requires no prior training and it has better robustness to color and illumination on the base of geometric inferences. Through comparing estimated VPs to the ground truth, the percentage of pixel errors were evaluated. The results proved that the methodology is successful in estimating VPs, meeting the requirements for vision-based autonomous vehicles. Full article

Drive cycle a is primary information useful for analyzing, designing, and optimizing automotive controllers for vehicle homologation. Conventional and electric vehicles are tested and certified based on the specified standard driving cycles as per vehicle category for emission compliance and energy consumption, respectively. In countries such as India, this drive cycle fails to conceal the real-time drive cycles on urban roads with heavy traffic. This real-time drive cycle details the driving skill, congestion, road characteristics, acceleration and deceleration durations, etc. In this context, the real-time drive cycle is captured with the help of an Inertial Measurement Unit. Analysis of IMU measured data with a suitable sampling rate is carried out and energy characterizations are presented in this article. For better accuracy, the IMU data logger is set for an 8 Hz sampling rate which logs the vehicle dynamics data of a scooter. For urban traffic data collection, Pune city is selected and actual energy spent is estimated with the engine, electric, and hybrid modes. State of Charge based switching is carried out with the help of a hybrid controller and observations are tabulated. State of Charge thresholds are monitored and energy-efficient switching is decided. It is estimated from the results that hybrid conversion of a scooter is more efficient due to charge/regeneration into a Lithium-ion battery when the engine powers the wheel and while braking. The range is extended with the above configuration, and further can be increased based on higher battery capacity. Energy management is better handled with a hybrid electric controller for urban roads. Range anxiety issues of EV are lowered in HEV configuration and it is also estimated that parallel Hybrid scooters are more energy-efficient and release lower carbon emissions than conventional vehicles. Full article

Estimation of combustion phasing and power production is essential to ensuring proper combustion and load control. However, archetypal control-oriented physics-based combustion models can become computationally expensive if highly accurate predictive capabilities are achieved. Artificial neural network (ANN) models, on the other hand, may provide superior predictive and computational capabilities. However, using classical ANNs for model-based prediction and control can be challenging, since their heuristic and deterministic black-box nature may make them intractable or create instabilities. In this paper, a hybridized modeling framework that leverages the advantages of both physics-based and stochastic neural network modeling approaches is utilized to capture CA50 (the timing when 50% of the fuel energy has been released) along with indicated mean effective pressure (IMEP). The performance of the hybridized framework is compared to a classical ANN and a physics-based-only framework in a stochastic environment. To ensure high robustness and low computational burden in the hybrid framework, the CA50 input parameters along with IMEP are captured with a Bayesian regularized ANN (BRANN) and then integrated into an overall physics-based 0D Wiebe model. The outputs of the hybridized CA50 and IMEP models are then successively fine-tuned with BRANN transfer learning models (TLMs). The study shows that in the presence of a Gaussian-distributed model uncertainty, the proposed hybridized model framework can achieve an RMSE of 1.3 × 10⁻⁵ CAD and 4.37 kPa with a 45.4 and 3.6 s total model runtime for CA50 and IMEP, respectively, for over 200 steady-state engine operating conditions. As such, this model framework may be a useful tool for real-time combustion control where in-cylinder feedback is limited. Full article

Leader-follower autonomy (LFA) systems have so far only focused on vehicles following other vehicles. Though there have been several decades of research into this topic, there has not yet been any work on human-vehicle leader-follower systems in the known literature. We present a system in which an autonomous vehicle—our ACTor 1 platform—can follow a human leader who controls the vehicle through hand-and-body gestures. We successfully developed a modular pipeline that uses artificial intelligence/deep learning to recognize hand-and-body gestures from a user in view of the vehicle’s camera and translate those gestures into physical action by the vehicle. We demonstrate our work using our ACTor 1 platform, a modified Polaris Gem 2. Results show that our modular pipeline design reliably recognizes human body language and translates the body language into LFA commands in real time. This work has numerous applications such as material transport in industrial contexts. Full article

In the paper, a Finite Element (FE) Analysis for investigating the electric properties of a Wireless Power Transfer System (WPTS) devoted to charging the batteries of electric vehicles is performed. In particular, the dynamic-WPTS, which is challenging because of the position-varying properties of the system, is considered. The field analysis is computationally heavy because of thin conductive layers modelling the car chassis: an effective analytical approximation for the field calculation in thin layers is applied to both the car frame bottom and the shielding aluminum layer. This approach allows for an accurate solution and, meanwhile, for a reduction in the computational costs, making the repeated simulations feasible. Full article

The steady enhancement of driver assistance systems and the automation of driving functions are in need of advanced driver monitoring functionalities. To evaluate the driver state, several parameters must be acquired. A basic parameter is the position of the driver, which can be useful for comfort automation or medical applications. Acquiring the position through cameras can be used to provide multiple information at once. When using infrared cameras, not only the position information but also the thermal information is available. Head tracking in the infrared domain is still a challenging task. The low resolution of affordable sensors makes it especially difficult to achieve high robustness due the lack of detailed images. In this paper, we present a novel approach for robust head tracking based on template matching and optical flow. The method has been tested on various sets of subjects containing different head shapes. The evaluation does not only include the original sensor size, but also downscaled images to simulate low resolution sensors. A comparison with the ground truth is performed for X- and Y-coordinate separately for each downscaled resolution. Full article

Due to changes in mobility and the emergence of new services, it is becoming necessary to establish new vehicle classes between conventional buses and privately owned vehicles. New mobility scenarios need concrete specifications to develop the most user-centered shuttle buses. As a result, we are looking for the requirements and needs of operators and customers. Initially, we want to determine the status quo, as there is no preliminary work in this regard. During the course of extensive literature research, expert interviews, and follow-up workshops, the respective solution space was highlighted and narrowed down. Services such as ride-pooling require adapted vehicle concepts to ensure optimal implementation of their offer. Due to its optimized processes, the automotive industry depends on producing vehicles in a certain quantity and manner. Faster changes and extensive experiments are not possible with the current production approach. Purpose-built vehicle concepts can make mobility services more attractive to customers while facilitating business operations. For instance, potential improvements can be identified in the seating concept. Full article

In order to further increase the efficiency of electrified vehicle drives, various predictive energy management strategies (driving strategies) have been developed. Therefore, a generic prediction approach is worked out in this paper, which enables a robust prediction of all traction torque-relevant variables for such strategies. It is intended to be useful for various types of electrification; however, the focus of this work is to the application in hybrid electric vehicles. In contrast to other approaches, no additional information (e.g., telemetry data) is required and thus a reliable prediction is guaranteed at all times. In particular, approaches from the fields of stochastics and artificial intelligence have proven to be effective for such purposes. Within the scope of this work, both so-called Markov Chains and Neural Networks are applied to predict real driving profiles within a required time horizon. Therefore, at first, a detailed analysis of the driver-specific ride characteristics is performed to ensure that real-world operation is represented appropriately. Next, the two models are implemented and the calibration is further discussed. The subsequent direct comparison of the two approaches is performed based on the described methodology, which includes both quantitative and qualitative analyses. Hereby, the quality of the predictions is evaluated using Root Mean Squared Error (RMSE) calculations as well as analyses in time domain. Based on the presented results, an appropriate approach is finally recommended. Full article

Many trucks are used for a class of activities involving a sequence of basic load-haul-dump operations. The repetitiveness of this operation has been an enabler for autonomous vehicle technology in efforts to increase safety and efficiency. In this paper, we present a framework for the automation of the load-haul-dump operation in a mine setting using an articulated dump truck. A simulation environment for the testing of autonomous driving algorithms is developed and a custom mining environment is generated to adapt to our simulation settings. We also present an operational decomposition of the sequence of tasks and develop a finite state machine for high-level decision making based on this decomposition. A path tracking module that considers both bodies of the articulated truck is also developed. The resulting architecture was implemented to achieve autonomy for a load-haul-dump operation in the simulated environment within a fixed path. Experiments show that the proposed FSM-path tracking system can automate the load-haul-dump operation; and that the simulation environment can support the testing and development of autonomous driving algorithms for configurations such as an articulated truck. Full article

The aim of herein work is to develop an automatized algorithm for detecting, as objectively as possible, the flame front evolution of lean/ultra-lean mixtures ignited by low temperature plasma-based ignition systems. The low luminosity characterizing the latter conditions makes both kernel formation and combustion development difficult to detect accurately. Therefore, to estimate the igniter capability to efficiently ignite the mixture, ever more performing tools are required. The present work proposes a new image analysis technique, based on a dual-exposure fusion algorithm and on Convolutional Neural Networks (CNNs), to process low brightness images captured via high-speed camera on an optical engine. The performance of the proposed algorithm (PA) is compared to the one of a base reference (BR) algorithm used by the same research group for the imaging analysis. The comparison shows the capability of PA to quantify the flame radius of consecutive combustion cycles with lower dispersion if compared to BR and to correctly detect some events considered as misfires or anomalies by BR. Moreover, the proposed method shows greater capability to detect, in advance, the kernel formation with respect to BR, thus allowing a more detailed analysis of the performance of the igniters. A metric quantitative analysis is carried out, as well, to confirm the above-mentioned results. Therefore, PA results to be more suitable for analyzing ultra-lean combustions, heavily investigated to meet the increasingly stringent legislation on the internal combustion engines. Finally, the proposed algorithm allows us to automatically estimate the flame front evolution, regardless of the user’s interpretation of the phenomenon. Full article

Vehicle drive systems are often oversized for common customer operation in order to cover the high demands of rare driving events such as towing a trailer, high acceleration or steep inclines. This high torque and power requirement affects the efficiency map and the highest efficiency is around the area of increased torque and speed. However, in everyday use, drive systems are mostly driven by customers at low speed and load, and therefore are not operating in the most efficient area. Designing a drive system that only covers the area of highest customer operation can increase efficiency by moving the sweet spot of efficiency to the relevant area, and thus reduce energy consumption. Therefore, customer data need to be analyzed in order to identify customer requirements and to localize the area of greatest operation. The method presented in this paper analyzes customer data in order to identify design-relevant parameters for a customer-specific drive system design. The available customer data results from event-based counts and are submitted as a statistical frequency distribution. These statistics are compared with discrete time series recorded during test drives in order to derive representative time series that correspond to customer behavior. By applying the time frame-based load analysis to these relevant time series, the desired design-relevant parameters are pointed out. Full article

There have been numerous attempts and investigations carried out with the objective to reduce the noise generated by railway freight wagons because noise is one of ever-present negative environmental pollution phenomena. This resulted in strong legislation requirements on noise reduction in railway transport, in the case of freight wagons, only exterior noise is a problem. However, the extremely hard metal structures of the wagons running on hard rails naturally generate high magnitudes of acoustic energy. One big initiative, especially in Germany, seeks a solution in replacement of the cast iron brake pads with the composite one which should result in so-called “silent trains”. But braking is used only during a minor part of the train run, leaving most of the acoustic phenomena of the train run unaffected. In our research, we focused on freight bogies type Y25Ls(s)e-K that are used, including in Slovakia. We simulated the structural natural frequencies to predict vibrations and consequent sound generated by these vibrations. The idea was to localize the vibrations and propose possibilities of noise attenuation. The more realistic view about sound fields was obtained by practical measurements on a moving bogie. Measurements on the test track at a maintenance workshop were done by using a digital acoustic camera Soundcam. For attenuation of noise radiated by the bogie frame, acoustic silencers made from recycled porous fiber material have been applied to the bogie frame. To determine the acoustic difference, the material was applied only on half of the bogie, and then the measurements were carried out. The results showed a promising improvement in reduced noise radiation, which gives support for further research in this area with more precise simulations and more precise coating of the bogie frame as well as the proposal and measurement of noise-attenuating coatings of other structural parts of the freight wagons. Full article