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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.

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.

Considering the current trend towards the electrification of commercial vehicles, the development of Beam eAxle solutions has become necessary. The utilization of an electric drive unit in heavy-duty solid axle-based commercial vehicles presents unique and demanding challenges. These include the necessity for elevated peak and continuous torque while meeting packaging constraints, structural integrity requirements, and extended service life. One such solution was developed by BorgWarner to address these challenges. This paper offers a comprehensive overview of the design and development process undertaken for this Dual Motor Beam eAxle system. This includes the initial comparison of various eAxle solutions, the specifications of components selected for this design, and the initial results from dyno and vehicle development.

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.

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.

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.

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.

Automotive crash data analysis and reconstruction is vital for ensuring automotive safety. The objective of vehicle crash reconstruction is to determine the vehicle's motion before, during, and after the crash, as well as the impact on occupants in terms of injuries. Simulation approaches, such as PC CrashTM, have been developed to understand pre-crash and post-crash vehicle motion, rather than the crash phase behavior. Over the past few decades, crash phase simulations have utilized vehicle finite element models. While multibody simulation tools are suitable for crash simulations, they often require detailed crash test data to accurately capture vehicle behavior, which is not always readily available. This paper proposes a solution to this limitation by incorporating crash test data from databases, such as NHTSA, Global NCAP, consumer rating reports, and videos, along with a multibody-based approach, to conduct crash phase simulations.

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.

An accurate estimate of vehicle speed is essential for optimal anti-lock braking system (ABS) calculations. Currently, most vehicles including heavy-duty class 8 trucks mainly rely on wheel speed sensors (WSS) to estimate velocity. However, as soon as braking is applied, WSS become inaccurate for determining the velocity due to the longitudinal slip developed in the tires. Using the inertial measurement unit (IMU) to estimate vehicle speed allows for its use in conjunction with the WSS to accurately calculate the slip ratio at each tire. These slip ratio values can then be used as the main control variable in the ABS algorithm to utilize the grip available more fully at each tire, to improve stopping distance and controllability. A steady state braking analysis model is developed and validated against Federal Motor Vehicle Safety Standards (FMVSS) 121 60-0 mph stopping distance data for a loaded class 8 tractor semi-trailer combination.

An optically accessible hydrogen-fueled, heavy-duty engine was used to investigate the impact of mixture formation on the early flame kernel propagation and the resulting combustion cyclic variability. Direct injection from a centrally mounted medium-pressure outward-opening hollow-cone injector created a fuel- air mixture with a global equivalence ratio of 0.33. The engine was operated at 1200 RPM with dry air at an intake pressure and temperature of 1.0 bar and 305 K, respectively. The charge was ignited at three different locations using focused-laser ignition to allow for undisturbed flame evolution, and the fuel injection timing and injection pressure were varied to influence the mixture inhomogeneity.

The upcoming regulations to achieve zero-emission passenger transport present challenges for designing new ferry powertrains. The proposed work investigates the feasibility of using a Proton Exchange Membrane Fuel Cell (PEMFC) power system to power a long-haul ferry. The paper describes the zero-order cell model as well as the method for estimating cell degradation. The stack modeling, heat balance equations, and auxiliary modeling are also presented. The proposed model enables the simulation of the fuel cell under different operating conditions and includes the use of air or oxygen as an oxidizer. A thermal management strategy for the overall PEMFC system is also proposed. The model was calibrated on the characteristic curves of the PEMFC Ballard FCvelocity™ HD6 (150 kW) and validated by reproducing experimental results. Then, a real load profile of a ferry, as well as the proposed powertrain is considered as case study.

This study comprehensively describes the application of linear electromagnetic actuators in automotive suspension systems, focusing on the electromagnetic force necessary in suspension systems operating in passive, semi-active, and active modes and their capability for energy regeneration. The use of electromagnetic actuators as an alternative to traditional shock absorbers makes easy the energy harvesting that is typically lost during vertical vehicle displacement. An initial sizing technique for dimensioning electromagnetic actuators is applied to estimate the requirements for heavy vehicles. Experimental tests are conducted to validate the proposed method and assess the performance of the electromagnetic actuator in the suspension system for passive, semi-active, and active operations.

With the law on autonomous driving and the associated ordinance, it has been possible in Germany since June 2022 to grant operating permission to vehicles with automated driving function and to admit them to road traffic. For public transport, automated shuttle buses offer the opportunity to maintain the existing service despite a shortage of personnel and to close gaps in supply. The safety of vehicle occupants, other road users and third parties is a key concern in the operation of automated shuttle buses. As part of the project RABus “Real laboratory for automated bus operation in public transport in urban and rural areas”, which is funded by the Ministry of Transport Baden-Württemberg, the operational reliability of automated shuttle buses (SAE Level 4) in public transport is being scientifically investigated. In the implementation of automated driving, obstacle detection/avoidance is a safety-critical driving function.

Globally, automotive sector is moving towards improving off-road performance, durability and safety. Need of off-road performance leads to unpredictable overload to powertrain system due to unpaved roads and abuse driving conditions. Generally, shafts and gears in the transmission system are designed to meet infinite life. But, under abuse condition, it undergo overloads in both torsional and bending modes and finally, weak part in the entire system tend to fail first. This paper represents the failure analysis of one such an incident happened in output shaft under abuse test condition. Failure mode was confirmed as torsional overload using Stereo microscope and SEM. Application stress and shear strength of the shaft was calculated and found overstressing was the cause of failure. To avoid recurrence of breakage, improvement options were identified and subjected to static torsional test to quantify the improvement level.