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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.

Highway safety remains a significant concern, especially in mixed traffic scenarios involving heavy-duty vehicles (HDV) and smaller passenger cars. The vulnerability of HDVs following closely behind smaller cars is evident in incidents involving the lead vehicle, potentially leading to catastrophic rear-end collisions. This paper explores how automatic speed enforcement systems, using speed cameras, can mitigate risks for HDVs in such critical situations. While historical crash data consistently demonstrates the reduction of accidents near speed cameras, this paper goes beyond the conventional notion of crash occurrence reduction. Instead, it investigates the profound impact of driver behavior changes within desired travel speed distribution, especially around speed cameras, and their contribution to the safety of trailing vehicles, with a specific focus on heavy-duty trucks in accident-prone scenarios.

Proportional integral derivative (PID) control technique is a famous and cost-effective control strategy, in real implementation, applied in various engineering applications. Also, the ant colony optimization (ACO) algorithm is extensively applied in various industrial problems. This paper addresses the usage of the ACO algorithm to tune the PID controller gains for a semi-active heavy vehicle suspension system integrated with cabin and seat. The magnetorheological (MR) damper is used in main suspension as a semi-active device to enhance the ride comfort and vehicle stability. The proposed semi-active suspension consists of a system controller that calculate the desired damping force using a PID controller tuned using ACO, and a continuous state damper controller that predict the input voltage that is required to track the desired damping force.

Nowadays, Bismuth (Bi) is being applied as an overlay material for engine bearings instead of Lead (Pb) which is an environmentally harmful material. Bi overlay has already been a solid performer in some automotive engine sectors due to its superior load carrying capacity and good robustness characteristic which are necessary to maintain its longevity during the lifetime of engines. The replacement is also seen on relatively larger size engines, such as Trucks and Off-highway heavy duty applications. Basically, these applications require higher power output than passenger cars, and the expected component lifecycle becomes longer. Though Bi has similar material characteristic to traditional Pb, it becomes challenging for the material alone to satisfy these requirements. Polymer overlay is known for its superior anti-wear performance and longer lifetime due to less adhesion against a steel counterpart than metallic materials (included Bi).

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.

Analysis of pedestrian-to-vehicle collisions can be complex due to the nature of the interaction and the physics involved. The scarcity of evidence like video evidence (from CCTV or dashcams), data from the vehicle's ECU, witness accounts, and physical evidence such as tyre marks, complicates the analysis of these incidents. In cases with limited evidence, current forensic methods often rely on prolonged inquiry processes or computationally intensive simulations. Without adequate data, accurately estimating pedestrian kinematics and addressing hit-and-run scenarios becomes challenging. This research provides an alternative approach to enhancing pedestrian forensic analysis based on machine learning (ML) algorithms trained on over 3000 multi-body computer simulations with a diverse set of vehicle profiles and pedestrian anthropometries.

Determining occupant kinematics in a vehicle crash is essential when understanding injury mechanisms and assessing restraint performance. Identifying contact marks is key to the process. This study was conducted to assess the ability to photodocument the various fluids on different vehicle interior component types and colors with and without the use of ultraviolet (UV) lights. Biological (blood, saliva, sweat and skin), consumable and chemical fluids were applied to vehicle interior components, such as seatbelt webbing, seat and airbag fabrics, roof liner and leather steering wheel. The samples were photodocumented with natural light and UV light (365 nm) exposure immediately after surface application and again 14 days later. The review of the photos indicated that fabric type and color were important factors. The fluids deposits were better visualized on non-porous than porous materials. For example, blood was better documented on curtain airbags than side or driver airbags.

Lithium-ion batteries serve as the main power source for contemporary electric vehicles. Safeguarding these batteries against damage is paramount, as it can trigger accelerated performance deterioration, potential fire hazards, environmental threats, and more. This study explores damage progression of a commercial vehicle lithium-ion battery module containing prismatic cells under indentation crush loading. We employed computational simulations of mechanical loading tests to investigate this behavior. Physical tests involved subjecting modules to low-speed (0.05 m/s) indentations using a V-shaped stainless-steel wedge, under six unique loading conditions. During the tests, force, and voltage change with wedge displacement were monitored. Utilizing experimental insights, we constructed a finite element model, which included key components of the battery module, such as the prismatic cells, steel frames, and various plastic parts.

Reconstruction of inline crashes between vehicles with a low closing speed, so-called “low speed” crashes, continues to be a class of vehicle collisions that reconstructionists require specific methods to handle. In general, these collisions tend to be difficult to reconstruct due primarily to the lack of, or limited amount of, physical evidence available after the crash. Traditional reconstruction methods such as impulse-momentum (non-residual damage based) and CRASH3 (residual damage based) both are formulated without considering tire forces of the vehicles. These forces can be important in this class of collisions. Additionally, the CRASH3 method depends on the use of stiffness coefficients for the vehicles obtained from high-speed crash tests. The question of the applicability of these (high-speed) stiffness coefficients to collisions producing significantly less deformation than experimental crashes on which they are generated, raises questions of the applicability.

Aluminium composites are remarkably used in automotive, aerospace, and agricultural sectors because of their lightweight with definable mechanical properties. The stir casting route was followed to fabricate cylindrical samples with base aluminium alloy LM4, LM4/SiC, LM4/Al2O3, and LM4/SiC/Al2O3. The tensile strength, compressive strength, hardness, and micro-structural analysis were performed on samples and Finite element analysis (FEA) was adopted to predict the failure modes of composites. The composites experimental results were found to be in line with the FEA results, however, the LM4/SiC/Al2O3 revealed better results on the mechanical properties when compared with other composite configurations. The mechanical properties improvement like hardness 5%-11%, tensile strength 10.26%-20.67%, compressive strength 15.19% - 32.58% and 71.52 - 82.1% reduction in dimension have been achieved in LM4/SiC/Al2O3 composite comparing to base metal.

Lightweight materials are in great demand in the automotive sector to enhance system performance. The automotive sector uses composite materials to strengthen the physical and mechanical qualities of light weight materials and to improve their functionality. Automotive elements such as the body shell, braking system, steering, engine, battery, seat, dashboard, bumper, wheel, door panelling, and gearbox are made of lightweight materials. Lightweight automotive metals are gradually replacing low-carbon steel and cast iron in automobile manufacture. Aluminium alloys, Magnesium alloys, Titanium alloys, advanced high-strength steel, Ultra-high strength steel, carbon fiber-reinforced polymers, and polymer composites are examples of materials used for light weighing or automobile decreased weight. The ever-present demand for fuel-efficient and ecologically friendly transport vehicles has heightened awareness of lowering weight and performance development.

Efficient transportation for carrying heavy loads is a common challenge across various applications, from supermarkets to industrial purposes. Conventional trolleys often fall short when loaded with heavy cargo, resulting in increased exertion and diminished productivity. Moreover, these challenges can adversely affect posture and lumbar spine health, especially for elder people and persons with cervical problems. There is a need for more user-friendly, ergonomic, and space-efficient solutions. This project addresses these challenges through an innovative design that encompasses various aspects of trolley functionality, including the study of comfort, wheel selection, and material considerations, drawing from ergonomic research. Multiple methods are employed to optimize the trolley’s dimensions to improve its overall performance. The trolley’s design features a collapsible basket for the transport of smaller-sized items and a base frame for larger goods and luggage.

Aluminum alloys are employed in agricultural equipment, aerospace sectors, medical instruments, machinery, automobiles, etc. due to their physical and mechanical characteristics. The geometrical shape and size of the parts are modified in turning operation by using a single-point cutting tool. A356 aluminum alloy is widely used in various engineering sectors, hence there is a necessity to produce A-356 components with quality. The inappropriate cutting parameters used in turning operation entail high production costs and reduce tool life. Box–Behnken design (BBD) based on response surface methodology (RSM) was used to design the experiments such that the experiment trials were conducted by varying cutting parameters like N-spindle speed (rpm), f-feed rate (mm/rev), and d-depth of cut (mm). The multi-objective responses, such as surface roughness (SR) and metal removal rate (MRR) were analyzed with the desirability method.

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.

With the rise of worldwide trends towards light weighting and the move towards electric vehicles, it is now more important than ever for the automotive industry to develop and implement lightweight materials that will result in significant weight reduction and product improvements. A great deal of research has been done on how to best combine and configure honeycomb cores with the right face sheets for Truck-Mounted Container Applications. Honeycomb structures possess the ability to bring about superior structural rigidity when the core parameters are selected and optimized based on the automotive application requirements.

India striving for carbon neutrality influences futures powertrain architecture of commercial vehicles. The use of CO2-free drives as battery electric have been demonstrated for various applications. The productivity still is a challenge due to missing high power charging infrastructure or limited range. This draws the attention to the use of sustainable fuels due to lower refueling times. The hydrogen engine got highest attention in the last couple of years. For markets as the EU the driver for hydrogen is the CO2 emission reduction, whereas for markets as India hydrogen offers the additional opportunity for more independence from fossil imports. Different OEMs all over the world have converted diesel engines to hydrogen operation with strong focus on performance and emission demonstration, so far with limited technology readiness of different key components.

The commercial vehicle sector (especially trucks) has major role in economic growth of a nation. With improving infrastructure, increasing number of commercial vehicles and growing amount of Vulnerable Road Users (VRUs) on roads, accidents are also increasing. As per RASSI (Road Accident Sampling System India) FY2016-21 database, commercial vehicles are involved in 43% of total accidents on Indian roads. One of the major causes of these accidents is Driver Drowsiness and Inattention (DDI) (approx. 10% contribution in total accidents). This paper describes novel driver-in-loop performance assessment methodology for comprehensive verification of Driver Monitoring System (DMS) for commercial vehicle application. Novelty lies in specification of test subjects, driving styles and variety of road traffic scenarios for verification of DMS system. Test setup is made modular to cater to different platform environments (Heavy, Intermediate, Light) with minor modifications.

For safety towards pedestrians and other road-vehicles, sound alert systems, like horns, have been in use since development of Automobiles. On the same line but with special purpose of preventing a Driver to sleep in a running vehicle, a Driver Monitoring System [DMS] is recently developed. For Electric Vehicles which are very quiet during starting and driving till 20 km/hr, Acoustic Vehicle Alert System (AVAS) is deployed in Europe and likely to be implemented in India soon. For all these Alert Devices, there are Standard Regulations across the world: ECE R-28 for Horns and ECE R-138 for AVAS. They, however, define only lower and upper dBA limits of the sound radiated by them. For DMS, no such regulation exists till today. With this, there has been a long time debate on what an optimal dBA level should be there, for all road-users as well as for vehicle- users inside the cabin.

Road infrastructure in India is being upgraded at a rapid pace. Quality of life of people has also improved significantly in the last decade. Such trends have significantly impacted design of commercial vehicles and vehicular systems in the country. This paper deals with the design and development of a modern futuristic instrument panel for trucks. Methodology to arrive at product features and solutions which retain their novelty and appeal for a longer term has also been illustrated. Regulatory scenario, modularity, HMI, Perceived Quality, Driver Comforts, evolving technologies, trends and materials are some of the considerations which have discussed in detail. International benchmarks and customer requirement have been analyzed for setting Performance targets. A digital approach for evaluating these considerations evolved during the design and development process has been elaborated in detail.

The automotive world is rapidly moving towards achieving shorter lead time using high-end technological solutions by keeping up with day-to-day advancements in virtual testing domain. With increasing fidelity requirements in test cases and shorter project lead time, the virtual testing is an inevitable solution. This paper illustrates method adopted to achieve best approximation to emulate driver behavior with 1-D (one dimensional) simulation based modeling approach. On one hand, the physical testing needs huge data collection of various parameters using sensors mounted on the vehicle. The vehicle running on road provides the real time data to derive durability test specifications. One such example includes developing duty cycle for powertrain durability testing using Road Torque Data Collection (RTDC) technique. This involves intense physical efforts, higher set-up cost, frequent iterations, vulnerability to manual errors and causing longer test lead-time.