Vehicles, Volume 5, Issue 1 (March 2023) – 21 articles

Energy moderation of the road transportation sector is required to limit climate change and to preserve resources. This work is focused on the moderation of vehicle consumption by optimizing the speed policy along an itinerary while taking into account vehicle dynamics, driver visibility and the road’s longitudinal profile. First, a criterion is proposed in order to detect speed policies that are impeding drivers’ eco-driving ability. Then, an energy evaluation is carried out and an optimization is proposed. A numerical application is performed on a speed limiting point with 20 usage cases and 5 longitudinal slope values. In the hypothesis of a longitudinal slope of zero, energy savings of 27.7 liter per day could be realized by a speed sign displacement of only 153.6 m. Potential energy savings can increase to up to 308.4 L per day for a $-4\%$ slope case, or up to 70.5 L per day for an ordinary $-2\%$ slope, with a sign displacement of only 391.5 m. This results in a total of 771,975 L of fuel savings over a 30 year infrastructure life cycle period. Therefore a methodology has been developed to help road managers optimize their speed policies with the aim of moderating vehicle consumption. Full article

Heavy vehicles exiting (or entering) a tunnel at high speed under a strong crosswind is a particularly critical condition since the aerodynamic load changes drastically, greatly affecting the lateral stability of the vehicle. Often, active control systems (active suspensions, active front steering, etc.) and infrastructure elements (e.g., wind fences) are proposed to reduce the induced risks. To help the design of these devices, the present paper investigates the response of the vehicle–driver system in the case of a high-sided lorry exiting a tunnel under crosswind, by using Driver-In-the-Loop simulations. The study was performed using the dynamic driving simulator of Politecnico di Milano and 28 test drivers. Vehicle and aerodynamic models have been developed to reproduce the phenomenon in a highly immersive environment. During the tests, several combinations of vehicle and wind speed were considered. The effect of vehicle loading condition (Empty and Laden) was also investigated. The performed tests allowed us to gain information about the sequence of the driver’s actions and associated delays, which may induce lane deviation or, in the worst case, rollover. It was found that lane invasion may happen for ratios of lateral aerodynamic force over vehicle weight force bigger than 0.1, while rollover could happen for ratios bigger than 0.3. Moreover, it was found that the driver’s response typically happens with a delay of ∼0.25 s with respect to the onset of the crosswind stimulus. Full article

In the vehicle-following scenario of autonomous driving, the change of driving style in the front vehicle will directly affect the decision on the rear vehicle. In this paper, a strategy based on a probabilistic neural network (PNN) for front vehicle intention recognition is proposed, which enables the rear vehicle to obtain the driving intention of the front vehicle without communication between the two vehicles. First, real vehicle data with different intents are collected and time—frequency domain variables are extracted. Secondly, Principal Component Analysis (PCA) is performed on the variables in order to obtain comprehensive features. Meanwhile, two cases are classified according to whether the front vehicle can transmit data to the rear vehicle. Finally, two recognition models are trained separately according to a PNN algorithm, and the two models obtained from the training are verified separately. When the front vehicle can communicate with the rear vehicle, the recognition accuracy of the corresponding PNN model reaches 96.39% (simulation validation) and 95.08% (real vehicle validation). If it cannot, the recognition accuracy of the corresponding PNN model reaches 78.18% (simulation validation) and 73.74% (real vehicle validation). Full article

In this paper, we pay significant attention to the most vulnerable road users (i.e., people with disabilities) when interacting with cyclists. The special needs of these groups are studied by distributing an online questionnaire about their perception and interaction with cyclists besides conducting an on-road experiment to test the possibility of sharing cycling infrastructure with wheelchair users. In an authentic case study, 2 cyclists and 5 wheelchair users were asked to ride their vehicles on a cycling lane in Madrid, in order to evaluate wheelchair users’ interaction with cyclists and reaction to the infrastructure by applying objective and subjective measures. The participants were provided with GPS, a speed sensor, and a head-mounted camera to record the experiment. The results show that people with disabilities feel threatened by cyclists who share the sidewalk with them; the respondents to the questionnaire suggested making the sidewalk free of cyclists to avoid conflict and improve safety. Moreover, the outputs of the experiment show positive feedback from wheelchair users when sharing cycling infrastructure regarding the improvement of speed and safety feeling. However, it is recommended to increase the number of wheelchair users to obtain more reliable and generalizable results. Full article

Only a small part of the high performance of electric drive systems in vehicles is used in everyday operation by customers. As a result, most drives are not operated in the optimum efficiency range. Designing a suitable drive system, whose performance is aligned with actual customer requirements, presents the potential to increase efficiency. Based on the findings of previous research, this paper serves to complement an existing method, which already introduced the basic method of transferring statistical customer data into relevant parameters for the design of a customer-specific drive system. In order to improve the method, further criteria for the selection of relevant time series come into place. Furthermore, the impact on maximum loads resulting from various sequences of the selected time series is identified and evaluated with time frame-based analysis. A new approach for the effective computation of maximum design-relevant loads in the admissible time frame range is introduced and validated. By taking this approach, the sensitivity of the derived design parameters regarding various time series sequence is evaluated in the context of selected datasets. In addition, concatenations of time series are identified which may have a relevant influence on the maximum loads. Consequently, the design process is safeguarded thoroughly against potential maximum loads as well as the associated thermal stresses. Full article

The forthcoming arrival of automated vehicles (AV) on the roads requires the re-evaluation or even adaptation of existing infrastructures as they are currently designed on the basis of human perception. Indeed, advanced driver-assistance systems (ADAS) do not necessarily have the same needs as drivers to detect road markings. One of the main challenges related to AV is the optimisation of the vehicle–infrastructure pair in order to guarantee the safety of all users. In this context, we compared the performance of a vehicle equipped with an ADAS machine-vision system with a dynamic retroreflectometer during the daytime on a road section. Our results questioned the reliability of the literature thresholds of the luminance contrast ratio on a dry road under sunny conditions. Despite the presence of old and worn road markings, the ADAS camera was able to detect the edge lines in more than 90% of the cases. The non-detections were not related to the poor condition of the markings but to the environmental conditions or the complexity of the infrastructure. Full article

Our paper introduces new reconstruction techniques of real-life critical road traffic accidents focusing on highly automated functions. The investigation method presented here focuses on the effect of relevant control parameters and environmental factors following the concept of sensitivity analysis. Two reconstruction tools are applied, the choice depending on the relevant causal factor of the accidents. Our measurement proves that the technical parameters of the control process, like time to collision or braking pressure that affects user satisfaction directly, can significantly influence the probability of accident occurrence. Thus, it is reasonable to consider safety with an increased weight compared to the user experience when identifying these parameters’ values. On the other hand, the effects of the investigated environmental factors were also found to be significant. Accordingly, future ADAS applications need to consider the change of environmental factors in the case of increased risk level, and driver-mode should be adapted to the new situation. Full article

At present, the noise of pure electric vehicles is a research hotspot, especially the noise of automatic transmission. In order to reduce the noise problem in the test, this paper proposes a method to optimize the local structure of the gearbox housing. First, the noise is evaluated and analyzed by combining subjective and objective methods, and the subjective score and noise order information are obtained. Then the factors that have great influence on the transmission error are explored, and the gearbox housing is finally determined as the optimization objective. Through finite element analysis, the weak position of the gearbox housing can be located quickly and accurately, and then the static and dynamic stiffness of the housing can be improved by adding and changing stiffeners. The simulation results show that the performance of the optimized housing is significantly improved. After the noise test of the whole vehicle, the noise of the two-speed automatic transmission is significantly reduced, and the subjective evaluation results are good. Full article

Ground vibrations near railway lines are generated by the forces that are acting between wheel and rail. It seems to be a straight forward assumption that the vehicle dynamics are important for the level and the frequencies of the excitation forces. Different vehicle dynamics phenomena are analysed for their role in the excitation of ground vibrations: rigid body modes of the bogies, elastic (bending) modes of the car body, and elastic modes of the wheelset. The theoretical analyses use rigid body models, simplified elastic models, and detailed elastic models. Some of these problems are vehicle–track interaction problems where 3D finite-element boundary-element models have been used for the track and soil. It is shown that the rigid or flexible vehicle modes are well in the frequency range of ground vibrations (4 to 100 Hz). They have an influence on the excitation force but the additional forces are rather small and can be neglected in ground vibration prediction. The theoretical results are checked by experimental results of a simultaneous measurement of vehicle, track, and ground vibrations. Full article

There are levels of automation in autonomous driving, and each level requires different performances of wireless communication, such as quality, delay time, and throughput. Therefore, the vehicle is required to adaptively control the level of automation when the performance of the wireless communication changes. In particular, it is essential to have a sufficient in-advance time for changing the level of automation. To ensure this time, an in-advance quality of service notification (IQN) has been considered in the fifth-generation mobile communications system (5G) standardization groups, in which predictive information about the quality of service is provided to vehicles from base stations. However, any specific utilizations of IQN for quality enhancement of wireless transmission were not considered. Therefore, in this study, we assume IQN as a prediction of throughput value and propose an improvement scheme for the uplink vehicle-to-network communication by distributing the traffic load and reducing the congestion of base stations. The effectiveness of the proposed scheme is evaluated via the summation of transmitted bits and counts when the target base stations connected by the target vehicles are fully loaded. The numerical results show that the proposed scheme realizes the reduction of network congestion without degrading the throughput performances of the vehicles. Full article

Step-ratio automatic transmission upshift performance can be improved by modulating the off-going (OFG) clutch during the inertia phase. In this paper, a static powertrain performance model is derived and applied for the purpose of numerically efficient, multi-objective shift control parameter optimization for the inertia phase. The optimization is aimed at finding the optimal node parameters for simplified, piecewise linear, open-loop profiles of oncoming (ONC) clutch, OFG clutch, and engine torque reduction control variables. The performance indices, i.e., the optimization objectives, include shift comfort, clutch thermal loss, and shift time. The optimization results in 3D Pareto optimal frontiers, which are then analyzed and compared with those obtained by using the previously developed, nonlinear model-based, genetic algorithm optimization tool. The derived method is employed in order to develop a static model-based predictive control (S-MPC) strategy, which commands ONC clutch torque control input while retaining open-loop controls for engine and OFG clutch control inputs. The S-MPC strategy aims at providing the prespecified shift time, while the shift time accuracy is relaxed to some extent by using a control input dead zone element to avoid chattering effect. The S-MPC system performance is verified through simulation and compared with the genetic algorithm benchmark. The simulation results demonstrate that the S-MPC strategy approaches the benchmark performance. Full article

In the literature the value of the driver’s head acceleration has been widely used as an objective function for the modification of the suspension and/or the seat characteristics in order to optimize the ride comfort of a vehicle. For these optimization procedures various lumped parameter Vehicle–Seat–Human models are proposed. In the present paper a Quarter Car model is integrated with three Seat–Human models with different levels of detail. The level of detail corresponds to the number of degrees of freedom used to describe the Seat–Human system. Firstly, the performance of the Quarter Car model, used as a basis, is analyzed in six excitations with different characteristics. Then, the performance of the three lumped parameter Vehicle–Seat–Human models are monitored in the same excitations. The results indicated that in the case of single disturbance excitations the Quarter Car model provided 50–75% higher values of acceleration compared with the eight degrees of freedom model. As far as the periodic excitation is concerned, the Vehicle–Seat–Human models provided values of acceleration up to eight times those of the Quarter Car model. On the other hand, in stochastic excitations the Vehicle–Seat–Human model with three degrees of freedom produced the closest results to the Quarter Car model followed by the eight degrees of freedom model. Finally, with respect to the computational efficiency it was found that an increase in the degrees of freedom of the Vehicle–Seat–Human model by one caused an increase in the CPU time from 2.1 to 2.6%, while increasing the number of the degrees of freedom by five increased the CPU time from 7.4 to 11.5% depending on the excitation. Full article

With the emergence of connected vehicle data and high-resolution weather data, there is an opportunity to develop models with high spatial-temporal fidelity to characterize the impact of weather on interstate traffic speeds. In this study, 275,422 trip records from 41,234 unique journeys on 42 rainy days in 2021 and 2022 were obtained. These trip records are categorized as no rain, slight rain, moderate rain, heavy rain, and very heavy rain periods using the precipitation rate from NOAA High-Resolution Rapid-Refresh (HRRR) data. It was observed that average speeds decreased by approximately 8.4% during conditions classified as very heavy rain compared to no rain. Similarly, the interquartile range of traffic speeds increased from 8.34 mph to 12.24 mph as the rain intensity increased. This study also developed a disaggregate approach using logit models to characterize the relationship between weather-related variables (precipitation rate, visibility, temperature, wind, and day or night) and interstate speed reductions. Estimation results reveal that the odds ratio of reducing speed is 5.8% higher for drivers if the precipitation rate is increased by 1 mm/h. The headwind was found to have a positive significant impact of only up to a 10% speed reduction, and speed reduction is greater during nighttime conditions compared to daytime conditions by a factor of 1.68. The additional explanatory variables shed light on drivers’ speed selection in adverse weather environments, providing more information than the single precipitation intensity measure. Results from this study will be particularly helpful for agencies and automobile manufacturers to provide advance warnings to drivers and establish thresholds for autonomous vehicle control. Full article

Teaching kinematic rotations is a daunting task for even some of the most advanced mathematical minds. However, changing the paradigm can highly simplify envisioning and explaining the three-dimensional rotations. This paradigm change allows a high school student with an understanding of geometry to develop the matrix and explain the rotations at a collegiate level. The proposed method includes the assumption of a point (P) within the initial three-dimensional frame with axes (${\widehat{x}}_i$, ${\widehat{y}}_i$, ${\widehat{z}}_i$). The method then utilizes a two-dimensional rotation view (2DRV) to measure how the coordinates of point P translate after a rotation around the initial axis. The equations are used in matrix notation to develop a rotation matrix for follow-on direction cosine matrixes. The method removes the requirement to use Euler’s formula, ultimately, providing a high school student with an elementary and repeatable process to compose and explain kinematic rotations, which are critical to attitude direction control systems commonly found in vehicles. Full article

In this work, a simulation framework for virtual testing of autonomous driving functions under the influence of a fault occurring in a component is presented. The models consist of trajectory planning, motion control, models of actuator management, actuators and vehicle dynamics. Fault-handling tests in a right-turn maneuver are described, subject to an injected fault in the steering system. Different scenarios are discussed without and with a fault and without and with counteractions against the fault. The results of five scenarios for different criticality metrics are discussed. In the case of a fault without a counteraction, a pronounced lateral position deviation of the ego vehicle from the reference curve is observed. Furthermore, the minimal and hence most critical time-to-collision (TTC) and post-encroachment time (PET) values are calculated for each scenario together with a parameter variation of the initial position of a traffic agent. The minimum TTC values are lowest in the case of a fault without counteraction. For the lateral position deviation and the TTC, the counteractions cause reduced criticality that can become even lower than in the case without a fault, corresponding to a decrease in the dynamic behavior of the vehicle. For the PET, only in the case of a fault without counteraction, a non-zero value can be calculated. With the implemented testing toolchain, the automated vehicle and the reaction of the HAD function in non-standard conditions with reduced performance can be investigated. This can be used to test the influence of component faults on automated driving functions and help increase acceptance of implemented counteractions as part of the HAD function. The assessment of the situation using a combination of metrics is shown to be useful, as the different metrics can become critical in different situations. Full article

In the European Union (EU), road transport contributes a major proportion of the total greenhouse gas (GHG) emissions, of which a significant amount is caused by heavy-duty commercial vehicles (CV). The increasing number of emission regulations and penalties by the EU have forced commercial vehicle manufacturers to investigate powertrain technologies other than conventional internal combustion engines (ICE). Since vehicle economics plays an important role in purchase decisions and the powertrain of a battery electric vehicle (BEV) contributes to about 8–20% of the total vehicle cost and the electric machine (EM) alone contributes to 33–43% of the drivetrain cost, it is necessary to analyze suitable EM topologies for the powertrain. In this paper, the authors aim to analyze the technical and cost aspects of an EM for electric commercial vehicles (ECV). Based on prior research and literature on this subject, an appropriate methodology for selecting suitable geometrical parameters of an e-machine for the use case of a heavy-duty vehicle is developed using MATLAB and Simulink tools. Then, for the economic analysis of the e-machine, reference ones are used, and their design parameters and cost structures are utilized to develop a cost function. Different use cases are evaluated according to the vehicle’s application. The results for a use case are compared by varying the design parameters to find the most cost-effective EM. Later, an analysis is performed on other decisive factors for EM selection. This highlights the importance of collaborative consideration of technological as well as the economic aspects of EMs for different use cases in ECVs. The method developed in this work contributes to understand the economic aspect of EMs as well as considering their performance factors. State-of-the-art methods and research are used to develop a novel methodology that helps with the selection of the initial geometry of the electric motor during the design process, which can serve to aid future designers and converters of electric heavy-duty vehicles. Full article

This paper proposes a simple and small-dimensioned antenna that can provide X band and Ku band for the low-earth-orbiting (LEO) satellite system in an Internet of vehicles system. The antenna is designed on the substrate Arlon DiClad 880. The antenna structure consists of an inverted triangle geometry and an inverted U-shaped slot. The dimensions of the antenna are 12.5 × 5 mm², and the area of the substrate is 30 × 13 × 0.254 mm³. The antenna is easy to make, and the manufacturing cost is low. The measurement results of the reflection coefficient (lower than −10 dB) of the antenna show that the working frequency band can cover the X-band (10.87–12.76 GHz) and the Ku band (15.19–16.02 GHz). The measured and simulated results are fairly similar. The efficiency of the antenna in the X-band is about 50–80.8%. The efficiency of the antenna in the Ku-band is about 50–74%. The gains of the antennas are about 3.34–6.08 dBi and 3.50–4.65 dBi in the X-band and Ku band, respectively, and the highest gain is 6.08 dBi. The antenna design can realize the features of low cost and small dimensions in autonomous vehicles and vehicle networking communication system equipment and achieve good wireless transmission capabilities from vehicles to the base station in the IOV. Full article

Inspired by the difference in the friction radii of the pads from the high-speed train brake system, stick–slip experiments for a disc–block friction system with different friction radii were carried out via a test device. Based on the test results, the stick–slip vibration characteristics of the disc–block friction system with variation in the friction radius were analyzed, and the corresponding Stribeck model parameters in exponential and fractional forms were identified. The experimental results show that with an increase in the friction radius the vibration amplitude first increased and then decreased and the frequency of stick–slip vibration increased. The identified Stribeck model parameters show that the decay factors increased, the static friction coefficient decreased, and the dynamic friction coefficient decreased first and then increased as the friction radius increased. Moreover, the identified Stribeck model in an exponential form can more accurately reflect the stick–slip characteristics of a disc–block friction system than the model in a fractional form. It can be further applied in the investigation of the dynamic behaviors of high-speed train brake systems. Full article

Emerging forms of shared mobility call for new vehicle routing models that take into account vehicle sharing, ride sharing and autonomous vehicle fleets. This study deals with the design of an optimal route network for autonomous vehicles, considering both vehicle sharing and ride sharing. The problem is modeled as a one-to-many-to-one vehicle routing problem with vehicle capacity and range constraints. An ant colony optimization algorithm is applied to the problem in order to construct a set of routes that satisfies user requests under operational constraints. Results show that the algorithm is able to produce solutions in relatively short computational times, while exploiting the possibility of ride sharing to reduce operating costs. Results also underline the potential of exploiting shared autonomous vehicles in the context of a taxi service for booking trips through electronic reservation systems. Full article

Driving style and external factors such as traffic density have a significant influence on the vehicle energy demand especially in city driving. A longitudinal control approach for intelligent, connected vehicles in urban areas is proposed in this article to improve the efficiency of automated driving. The control approach incorporates information from Vehicle-2-Everything communication to anticipate the behavior of leading vehicles and to adapt the longitudinal control of the vehicle accordingly. A supervised learning approach is derived to train a neural prediction model based on a recurrent neural network for the speed trajectories of the ego and leading vehicles. For the development, analysis and evaluation of the proposed control approach, a co-simulation environment is presented that combines a generic vehicle model with a microscopic traffic simulation. This allows for the simulation of vehicles with different powertrains in complex urban traffic environment. The investigation shows that using V2X information improves the prediction of vehicle speeds significantly. The control approach can make use of this prediction to achieve a more anticipatory driving in urban areas which can reduce the energy consumption compared to a conventional Adaptive Cruise Control approach. Full article