Vehicles, Volume 3, Issue 4 (December 2021) – 15 articles

Vehicular technology has integrated many features in the system, which enhances the safety and comfort of the user. Among these features, heating, ventilation, and air conditioning (HVAC) is the only feature that maintains the set cabin air temperature (CAT). The user’s command drives the set CAT, and the thermostat provides feedback to the HVAC to maintain the set CAT. The CAT is increased by extracting the heat from the engine surface produced by the fuel combustion, whereas the CAT is reduced by the known processes of the air conditioning system (ACS). Therefore, the CAT driven by the user’s command may not be optimal, and estimating the optimal CAT is still unsolved. In this work, we propose a new process where the user can input a range for CAT instead of a single value. Optimal HVAC criteria were defined, and the CAT was estimated by performing iterative analysis in the user-selected range satisfying the criteria. The HVAC criteria were defined based on two measurable parameters: air conditioning refrigerant fluid pressure (ACRFP) and engine surface temperature (EST) empirically defined as the vector CATOP. In this article, a NARX DL model was used by mapping the vehicle-level vectors (VLV) to predict the CATOP in real-time using field data obtained from a 2020 Cadillac CT5 test vehicle. Utilising the DL model, CATOP for future time steps was predicted by varying the CAT in the definite range and applying HVAC criteria. Thus, an optimal set CAT was estimated, corresponding to the optimal CATOP defined by the HVAC criteria. We performed the validation of the DL model for multiple datasets using traditional statistical techniques, namely, signal-to-noise ratio (SNR) values, first-order derivatives (FOD), and root-mean-square error (RMSE). Full article

The electric vehicle market is an increasingly important aspect of the automotive industry. However, as a relatively new technology, several issues remain present within the industry. An analysis is utilised to examine these issues, along with how they affect the industry and how they can be tackled. Several key issues that affect the electric vehicle market, as well as how efforts to address these issues influence the market, are identified. The analysis also includes the examination of ethical issues, with the issues that arise from the production of raw materials for electric vehicles. The analysis and examination of ethical issues display a wide range of problems in the industry. However, it did highlight the efforts being made to lessen the effect of these problems by various groups, such as regulation by EU and US governing bodies on the materials mined. From this analysis, this paper identifies that many of the other factors examined are directly or indirectly influenced by political and economic factors, also examined in this review. This highlights the impact that governing bodies and businesses have on a vast number of issues that are present within the market and how they can resolve the harmful factors examined. Full article

In Taiwan, motorcycles are the most commonly used means of transportation and also have the highest accident rate. Because motorcycles are less stable and provide less protection than cars, motorcycle riders are vulnerable in traffic accidents. Furthermore, head trauma is often fatal, causing a great loss to society. Although helmets provide protection to the head, they also affect the visual field of motorcycle riders. However, the literature mostly focuses on the protective effect of helmets after a collision and rarely considers the influence of helmets prior to collisions. In the study design, participants wore three different types of helmet and watched a pre-recorded video of an actual street with pre-placed warning triangles at a speed of 60 km/h. Participants were asked to press a button when they saw a warning triangle. The time between participants seeing the warning triangle and arriving at the warning triangle was calculated. This time is referred to as the “early reaction time.” The number of missed presses and false presses was also recorded. The results of the study show that: (1) Of the three types of helmet, wearing half helmets produced the longest early reaction times, followed by 3/4 helmets, with full face helmets with the shortest early reaction times. (2) Early reaction times when wearing a half helmet were the same as early reaction times when not wearing a helmet. (3) The results for the total number of missed and false presses when wearing the three types of helmet were the same as for the results of the early reaction time analysis. (4) Sex and age had no effect on early reaction times. The experimental results can be used as a reference for helmet design and academic research. Full article

The purpose of this work is to determine as a function of velocity the minimal roadway luminance that is required to be judged as being bright enough for a driver to perform a nighttime driving task with an adequate feeling of safety. In this context, it shall also be evaluated which areas of the vehicle forefield are most crucial for the driver’s general brightness perception. A field study with 23 subjects and dimmable LED headlights was conducted, in which the subjects were given the task to assess their perceived brightness for different luminance levels caused by the headlights’ low-beam distribution in the vehicle’s forefield on a 5-step rating scale. The experiments were repeated for three different driving velocities of 0 km h⁻¹ (static case), 30 km h⁻¹, and 60 km h⁻¹, respectively. Results for the static case indicate that, for the roadway to be perceived as bright enough by 50% of the subjects, an average roadway luminance of 0.88 cd m⁻² is required in an area up to 32 m in front of the vehicle. Furthermore, a significant effect of driving speed is observed. For example, at 60 km h⁻¹, the luminance must be increased to 1.54 cd m⁻² to be still perceived as sufficiently bright by 50% of the subjects. Full article

The popularity of the eBus has been increasing rapidly in recent years due to its low greenhouse gases (GHG) emissions and its low dependence on fossil fuels. This incremental use of the eBus increases the burden to the power grid for its charging. Charging eBus requires a high amount of power for a feasible amount of time. Therefore, developing a fast-charging station (FCS) integrated with Micro Energy Grid (MEG) and hybrid energy storage is crucial for charging eBuses. This paper presents a design of FCS for eBus that integrates MEG with hybrid energy storage with the energy management system. To reduce the dependency on the main utility grid, a hybrid micro energy grid based on a renewable source (i.e., PV) have been included. In addition, hybrid energy storage of batteries and flywheels has also been developed to mitigate the power demand of the fast-charging station during peak time. Furthermore, a multiple-input DC-DC converter has been developed for managing the DC power transfer between the common DC bus and the multiple energy sources. Finally, an energy management system and the controller has been designed to achieve an extensive performance from the fast charging station. MATLAB Simulink has been used for the simulation work of the overall design. Different test case scenarios are tested for evaluating the performance parameters of the proposed FCS and also for evaluating its performance. Full article

With incessant increases in fuel prices worldwide and concerns for environmental pollution, the need for alternative sources of energy is becoming urgent. In this study, the potential of grape seed oil for biodiesel as an alternative fuel was evaluated. Refined grape seed oil was bought in liquid form and then subjected to an alkali-catalyzed transesterification process for biodiesel production. The physicochemical properties of the resulting biodiesel—namely, viscosity, cetane number, and heating value—were investigated. The biodiesel was blended with a conventional diesel in various proportions and combusted in a four-cylinder, four-stroke compression ignition (diesel) engine under two loading conditions. Experimental results revealed that the blend ratio of B70 (70% GS biodiesel and 30% conventional diesel) gave the best overall engine performance in terms of maximum power, minimum emissions, and fuel consumption. Furthermore, a novel neural network technique called extreme learning machine was adopted to investigate the optimal blend ratio using the dataset obtained from the experimental results. The results also indicate that the best choice of biodiesel blend ratio is approximately B73.67 (73.67% GS biodiesel and 26.33% conventional diesel). The study shows that grape seed oil could serve as a reliable source of production of quality biodiesel fuels, which could be used as an alternative to conventional diesel fuels. Full article

RTOB-SLAM is a new low-computation framework for real-time onboard simultaneous localization and mapping (SLAM) and obstacle avoidance for autonomous vehicles. A low-resolution 2D laser scanner is used and a small form-factor computer perform all computations onboard. The SLAM process is based on laser scan matching with the iterative closest point technique to estimate the vehicle’s current position by aligning the new scan with the map. This paper describes a new method which uses only a small subsample of the global map for scan matching, which improves the performance and allows for a map to adapt to a dynamic environment by partly forgetting the past. A detailed comparison between this method and current state-of-the-art SLAM frameworks is given, together with a methodology to choose the parameters of the RTOB-SLAM. The RTOB-SLAM has been implemented in ROS and perform well in various simulations and real experiments. Full article

The quickly rising development of autonomous vehicle technology and increase of (semi-) autonomous vehicles on the road leads to an increased demand for more sophisticated human–machine-cooperation approaches to improve trust and acceptance of these new systems. In this work, we investigate the feeling of discomfort of human passengers while driving autonomously and the automatic detection of this discomfort with several model approaches, using the combination of different data sources. Based on a driving simulator study, we analyzed the discomfort reports of 50 participants for autonomous inner city driving. We found that perceived discomfort depends on the driving scenario (with discomfort generally peaking in complex situations) and on the passenger (resulting in interindividual differences in reported discomfort extend and duration). Further, we describe three different model approaches on how to predict the passenger discomfort using data from the vehicle’s sensors as well as physiological and behavioral data from the passenger. The model’s precision varies greatly across the approaches, the best approach having a precision of up to 80%. All of our presented model approaches use combinations of linear models and are thus fast, transparent, and safe. Lastly, we analyzed these models using the SHAP method, which enables explaining the models’ discomfort predictions. These explanations are used to infer the importance of our collected features and to create a scenario-based discomfort analysis. Our work demonstrates a novel approach on passenger state modelling with simple, safe, and transparent models and with explainable model predictions, which can be used to adapt the vehicles’ actions to the needs of the passenger. Full article

This article presents a novel methodology to predict the optimal adaptive cruise control set speed profile (ACCSSP) by optimizing the engine operating conditions (EOC) considering vehicle level vectors (VLV) (body parameter, environment, driver behaviour) as the affecting parameters. This paper investigates engine operating conditions (EOC) criteria to develop a predictive model of ACCSSP in real-time. We developed a deep learning (DL) model using the NARX method to predict engine operating point (EOP) mapping the VLV. We used real-world field data obtained from Cadillac test vehicles driven by activating the ACC feature for developing the DL model. We used a realistic set of assumptions to estimate the VLV for the future time steps for the range of allowable speed values and applied them at the input of the developed DL model to generate multiple sets of EOP’s. We imposed the defined EOC criteria on these EOPs, and the top three modes of speeds satisfying all the requirements are derived at each second. Thus, three eligible speed values are estimated for each second, and an additional criterion is defined to generate a unique ACCSSP for future time steps. A performance comparison between predicted and constant ACCSSP’s indicates that the predictive model outperforms constant ACCSSP. Full article

Due to increasing sales figures, the energy consumption of battery-electric vehicles is moving further into focus. In addition to efficient driving, it is also important that the energy losses during AC charging are as low as possible for a sustainable operation. In many situations it is not possible or necessary to charge the vehicle with the maximum charging power e.g., in apartment buildings. The influence of the charging mode (number of phases used, in-cable-control-box or used wallbox, charging current) on the charging efficiency is often unknown. In this work, the energy consumption of two electric vehicles in the Worldwide Harmonized Light-Duty Vehicles Test Cycle is presented. In-house developed measurement technology and vehicle CAN data are used. A detailed breakdown of charging losses, drivetrain efficiency, and overall energy consumption for one of the vehicles is provided. Finally, the results are discussed with reference to avoidable CO${}_2$ emissions. The charging losses of the tested vehicles range from 12.79 to 20.42%. Maximum charging power with three phases and 16 A charging current delivers the best efficiencies. Single-phase charging was considered down to 10 A, where the losses are greatest. The drivetrain efficiency while driving is 63.88% on average for the WLTC, 77.12% in the “extra high” section and 23.12% in the “low” section. The resulting energy consumption for both vehicles is higher than the OEM data given (21.6 to 44.9%). Possible origins for the surplus on energy consumption are detailed. Over 100,000 km, unfavorable charging results in additional CO${}_2$ emissions of 1.24 t. The emissions for an assumed annual mileage of 20,000 km are three times larger than for a class A+ refrigerator. A classification of charging modes and chargers thus appears to make sense. In the following work, efficiency improvements in the charger as well as DC charging will be proposed. Full article

Sensor uncertainty significantly affects the performance of autonomous vehicles (AVs). Sensor uncertainty is predominantly linked to sensor specifications, and because sensor behaviors change dynamically, the machine learning approach is not suitable for learning them. This paper presents a novel learning approach for predicting sensor performance in challenging environments. The design of our approach incorporates both epistemic uncertainties, which are related to the lack of knowledge, and aleatoric uncertainties, which are related to the stochastic nature of the data acquisition process. The proposed approach combines a state-based model with a predictive model, where the former estimates the uncertainty in the current environment and the latter finds the correlations between the source of the uncertainty and its environmental characteristics. The proposed approach has been evaluated on real data to predict the uncertainties associated with global navigation satellite systems (GNSSs), showing that our approach can predict sensor uncertainty with high confidence. Full article

Low vehicle occupancy rates combined with record conventional vehicle sales justify the requirement to optimize vehicle type based on passengers and a powertrain with zero-emissions. This study compares the performance of different vehicle types based on the number of passengers/payloads, powertrain configuration (battery and fuel cell electric configurations), and drive cycles, to assess range and energy consumption. An adequate choice of vehicle segment according to the real passenger occupancy enables the least energy consumption. Vehicle performance in terms of range points to remarkable results for the FCEV (fuel cell electric vehicle) compared to BEV (battery electric vehicle), where the former reached an average range of 600 km or more in all different drive cycles, while the latter was only cruising nearly 350 km. Decisively, the cost analysis indicated that FCEV remains the most expensive option with base cost three-fold that of BEV. The FCEV showed notable results with an average operating cost of less than 7 cents/km, where BEV cost more than 10 €/km in addition to the base cost for light-duty vehicles. The cost analysis for a bus and semi-truck showed that with a full payload, FCPT (fuel cell powertrain) would be more economical with an average energy cost of ~1.2 €/km, while with BPT the energy cost is more than 300 €/km. Full article

Road markings are beneficial to human drivers, advanced driver assistance systems (ADAS), and automated driving systems (ADS); on the contrary, snow coverage on roads poses a challenge to all three of these groups with respect to lane detection, as white road markings are difficult to distinguish from snow. Indeed, yellow road markings provide a visual contrast to snow that can increase a human drivers’ visibility. Yet, in spite of this fact, yellow road markings are becoming increasingly rare in Europe due to the high costs of painting and maintaining two road marking colors. More importantly, in conjunction with our increased reliance on automated driving, the question of whether yellow road markings are of value to automatic lane detection functions arises. To answer this question, images from snowy conditions are assessed to see how different representations of colors in images (color spaces) affect the visibility levels of white and yellow road markings. The results presented in this paper suggest that yellow markings provide a certain number of benefits for automated driving, offering recommendations as to what the most appropriate color spaces are for detecting lanes in snowy conditions. To obtain the safest and most cost-efficient roads in the future, both human and automated drivers’ actions must be considered. Road authorities and car manufacturers also have a shared interest in discovering how road infrastructure design, including road marking, can be adapted to support automated driving. Full article

This paper presents licence-plate recognition for identifying vehicles with similar licence-plates. The method uses a modified licence-plate recognition pipeline, with licence-plate template matching replacing character segmentation and recognition. Only edge detection is used, combined with a method for calculating line ratio to locate and extract licence-plates. The extracted licence-plate templates are then compared for licence-plate matching. The results show that the method performs well in differing circumstances, and that it is computationally cost-effective. Results also show that licence-plate template matching is a reliable method of identifying similar vehicles, and has a lower computational cost when compared with character segmentation and recognition. Full article

In this paper, a motion cueing algorithm (MCA) without a frequency divider is proposed, which aims to reproduce the longitudinal reference acceleration as far as possible via tilt coordination. Using a second-order rate limit, the human perception thresholds can directly be taken into account when parameterizing the MCA. The washout is compensated by tilt coordination and means of feedback from the translational acceleration. The proposed MCA is compared with the classical washout algorithm and the compensation MCA based on selected qualitative metrics and their workspace demand. In addition, a subjective study on the evaluation of the MCA was conducted. The results show that even high washout rates are not noticeable by the test subjects. Overall, the MCA was rated as very good. Full article