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Understanding left-turn vehicle-pedestrian accident mechanisms is critical for developing accident-prevention systems. This study aims to clarify the features of driver behavior focusing on drivers’ gaze, vehicle speed, and time to collision (TTC) during left turns at intersections on left-hand traffic roads. Herein, experiments with a sedan and light-duty truck (< 7.5 tons GVW) are conducted under four conditions: no pedestrian dummy (No-P), near-side pedestrian dummy (Near-P), far-side pedestrian dummy (Far-P) and near-and-far side pedestrian dummies (NF-P). For NF-P, sedans have a significantly shorter gaze time for left-side mirrors compared with light-duty trucks. The light-duty truck’s average speed at the initial line to the intersection (L1) and pedestrian crossing line (L0) is significantly lower than the sedan’s under No-P, Near-P, and NF-P conditions, without any significant difference between any two conditions.

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Heavy Vehicle Event Data Recorders (HVEDRs) have the ability to capture important data surrounding an event such as a crash or near crash. Efforts by many researchers to analyze the capabilities and performance of these complex systems can be problematic, in part, due to the challenges of obtaining a heavy truck, the necessary space to safely test systems, the inherent unpredictability in testing, and the costs associated with this research. In this paper, a method for simulating vehicle speed sensor (VSS) inputs to HVEDRs to trigger events is introduced and validated. Full-scale instrumented testing is conducted to capture raw VSS signals during steady state and braking conditions. The recorded steady state VSS signals are injected into the HVEDR along with synthesized signals to evaluate the response of the HVEDR. Brake testing VSS signals are similarly captured and injected into the HVEDR to trigger an event record.

Recognizing road conditions using onboard sensors is significant for the performance of intelligent vehicles, and the road profile is a widely accepted representation both in the temporal and frequency domains, greatly influencing driving quality. In this paper, a recurrent neural network embedded with attention mechanisms is proposed to reconstruct the road profile sequence. Firstly, the road and vehicle sensor signals are obtained in a simulated environment by modeling the road, tire, and vehicle dynamic system. After that, the models under different working conditions are trained and tested using the collected data, and the attention weights of the trained model are then visualized to optimize the input channels. Finally, field experiments on the real vehicle are conducted to collect real road profile data, combined with vehicle system simulation, to verify the performance of the proposed method.

This study focuses on the operation of trolley-assisted mining truck, which leverage overhead lines for uphill propulsion, substantially reducing fuel consumption and carbon emissions. The pantograph mounted at the truck body's front exhibits complex vibrational behavior due to the subgrade stiffness and the nonlinearities of the hydro-pneumatic suspension. Vertical dynamic model of the mining truck is constructed which considering the road conditions and suspension characteristics to illustrate the pantograph's contact force. The vibration characteristic of pantograph base is analyzed which using the spatial transformation relationship between the truck's center mass of gravity and the base of pantograph. The stiffness of pantograph is designed based on a pantograph-catenary system model considering different road conditions. The real mining truck is modeled in the Trucksim software to obtain the vibration of pantograph base.

By installing an automated mechanical transmission (AMT) on heavy-duty vehicles and developing a reasonable shift strategy, it can reduce driver fatigue and eliminate technical differences among drivers, improving vehicle performance. However, after detaching from the experience of good drivers, the current shifting strategy is limited to the vehicle state at the current moment, and cannot make predictive judgment of the road environment ahead, and problems such as cyclic shifting will occur due to insufficient power when driving on the ramp. To improve the adaptability of heavy-duty truck shift strategy to dynamic driving environments, this paper first analyzes the shortcomings of existing traditional heavy-duty truck shift strategies on slopes, and develops a comprehensive performance shift strategy incorporating slope factors. Based on this, forward-looking information is introduced to propose a predictive intelligent shift strategy that balances power and economy.

Since the early 1990’s, commercial vehicles have suffered from repeated vulnerability exploitations that resulted in a need for improved automotive cybersecurity. This paper outlines the strategies and challenges of implementing an automotive Zero Trust Architecture (ZTA) to secure intra-vehicle networks. Zero Trust (ZT) originated as an Information Technology (IT) principle of “never trust, always verify”; it is the concept that a network must never assume assets can be trusted regardless of their ownership or network location. This research focused on drastically improving security of the cyber-physical vehicle network, with minimal performance impact measured as timing, bandwidth, and processing power. The automotive ZTA was tested using a software-in-the-loop vehicle simulation paired with resource constrained hardware that closely emulated a production vehicle network.

Truck platooning is an emerging technology that exploits the drag reduction experienced by bluff bodies moving together in close longitudinal proximity. The drag-reduction phenomenon is produced via two mechanisms: wake-effect drag reduction from leading vehicles, whereby a following vehicle operates in a region of lower apparent wind speed, thus reducing its drag; and base-drag reduction from following vehicles, whereby the high-pressure field forward of a closely-following vehicle will increase the base pressure of a leading vehicle, thus reducing its drag. This paper presents a physics-guided empirical model for calculating the drag-reduction benefits from truck platooning. The model provides a general framework from which the drag reduction of any vehicle in a heterogeneous truck platoon can be calculated, based on its isolated-vehicle drag-coefficient performance and limited geometric considerations.

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.

Prime concern for electric vehicle where the application of the vehicle is public transport, is the charging of vehicle and operation of its infrastructure. Such an example of operating the EV buses is under the GCC (gross cost contract) model, with high operation time and comparatively lesser time for charging. It is challenging to meet these requirements. To counter this situation in fleet operated busses it is proposed to adapt an automated charging method which involves minimum man power intervention and automated mechanism to connect & disconnect the charging connectors. This paper proposes an automated pantograph mechanism based method of charging EV buses, meeting requirements as per SAE J3105 & ISO 15118 standards, which would be an ideal way to resolve the current situation.

For commercial vehicles, reliability is key since the vehicle is typically linked to the daily earnings of the owner. To ensure continuous vehicle operation, early diagnostics of critical issues and proactive maintenance are important. However, an electric vehicle is a complex and dynamic system consisting of numerous components interacting with each other and with external environments such as road conditions, traffic, weather, and driving behavior. Thus, vehicle operation and performance are highly contextual and for identifying an abnormal operation (diagnostics) the solution must consider the conditions under which it is driven. To address this, the paper proposes an AI-based digital twin of an electric three-wheeler vehicle. TabNet a deep-learning based model is used to learn and generate near-ideal vehicle behavior. The focus of the paper is motor subsystem. The model is trained using appx 200 vehicles first 1500 km driven data.

Rapid Urbanisation, in recent times, has created an exponential demand for light commercial vehicles. Electric vehicles are seen as a way to reduce the impact of emissions due to transportation in urban areas. Due to the growth of e-commerce, commercial transportation, and particularly last-mile delivery, is anticipated to increase. In this context, electric light commercial vehicles (eLCVs) have the potential to be a promising solution by tackling the emission impacts, ensuring faster delivery along with ideal running costs and payload capacity. To increase the range of electric vehicles, it has to be designed for lighter weight with optimal performance in order to meet the user requirements. Cargo capacity and payload have to be taken into account while design & validating the vehicle structure under static and dynamic conditions. Simulation driven product development will help the design team to account for the possible design failure cases at system and vehicle level.

A bus is integral part of public transportation in both rural and urban areas. It is also used for scheduled transport, tourism, and school transport. Buses are the common mode of transport all over the world. The growth in economy, the electrification of public transport, demand in shared transport, etc., is leading to a surge in the demand for buses and accelerating the overall growth of the bus industry. With the increased number of buses, the issue of safety of passengers and the crew assumes special importance. The comfort of driver and passenger in the vehicle involves the vibration performance and therefore, the structural integrity of buses is critically important. Bus safety act depicts the safety and comfort of bus operations, management of safety risks, continuous improvement in bus safety management, public confidence in the safety of bus transport, appropriate stakeholder involvement and the existence of a safety culture among bus service providers.

The battery electric buses (BEB) are set as key tools to enable cities to meet their challenging transport environmental targets, i.e. the reduction of Greenhouse gas (GHG) emissions, improvement of local air quality, as well as to provide a quieter system for both passengers and the urban community. The recent evolutions of the traction battery technology, with increasing battery energy and power densities, battery durability and dynamic performance, driven by both the light and heavy duty vehicles segment, has opened the way for a series of transit bus electrification initiatives, focused on the evaluation of the feasibility of the BEB technology for the zero local emission bus fleet targets, already set by transit authorities in some important cities worldwide.

Given the rapid advancement of connected and automated transportation, its applications have significantly increased. They are being studied worldwide to shape the future of mobility. Key promises are a more comfortable, efficient and socially adapted kind of mobility. As part of the EU Horizon2020 project SHared automation Operating models for Worldwide adoption (SHOW), the Karlsruhe Test Site in the Test Area Autonomous Driving Baden-Württemberg (TAF-BW) addresses aspects of scalability to overcome challenges, which have so far hindered market penetration of this future-oriented kind of mobility. The explored services, including passenger and cargo transport, are closely linked to the daily travel requirements of road users, particularly in peri-urban areas, to cover the last mile of their journeys, connecting them to public transport.

Abstract Parking an articulated vehicle is a challenging task that requires skill, experience, and visibility from the driver. An automatic parking system for articulated vehicles can make this task easier and more efficient. This article proposes a novel method that finds an optimal path and controls the vehicle with an innovative method while considering its kinematics and environmental constraints and attempts to mathematically explain the behavior of a driver who can perform a complex scenario, called the articulated vehicle park maneuver, without falling into the jackknifing phenomena. In other words, the proposed method models how drivers park articulated vehicles in difficult situations, using different sub-scenarios and mathematical models.