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Human driving behavior's inherent variability, randomness, individual differences, and dynamic vehicle-road situations give human-machine cooperative (HMC) driving considerable uncertainty, which affects the applicability and effectiveness of HMC control in complex scenes. To overcome this challenge, we present a novel data-enabled game output regulation approach for HMC driving. Firstly, a global human-vehicle-road (HVR) model is established considering the varied driver's steering characteristic parameters, such as delay time, preview time, and steering gain, as well as the uncertainty of tire cornering stiffness and variable road curvature disturbance. The robust output regulation theory has been employed to ensure the global DVR system's closed-loop stability, asymptotic tracking, and disturbance rejection, even with an unknown driver's internal state. Secondly, an interactive shared steering controller has been designed to provide personalized driving assistance.

The soft and rough terrain on the planet's surface significantly affects the ride and safety of rovers during high-speed driving, which imposes high requirements for the control of the suspension system of planet rovers. To ensure good ride comfort of the planet rover during operation in the low-gravity environment of the planet's surface, this study develops an active suspension control strategy for torsion spring and torsional damper suspension systems for planet rovers. Firstly, an equivalent dynamic model of the suspension system is derived. Based on fractal principles, a road model of planetary surface is established. Then, a fuzzy-PID based control strategy aimed at improving ride comfort for the planet rover suspension is established and validated on both flat and rough terrains.

Compared with urban areas, the road surface in mountainous areas generally has a larger slope, larger curvature and narrower width, and the vehicle may roll over and other dangers on such a road. In the case of limited driver information, if the two cars on the mountain road approach fast, it is very likely to occur road blockage or even collision. Multi-vehicle cooperative control technology can integrate the driving data of nearby vehicles, expand the perception range of vehicles, assist driving through multi-objective optimization algorithm, and improve the driving safety and traffic system reliability. Most existing studies on cooperative control of multiple vehicles is mainly focused on urban areas with stable environment, while ignoring complex conditions in mountainous areas and the influence of driver status. In this study, a digital twin based multi-vehicle cooperative warning system was proposed to improve the safety of multiple vehicles on mountain roads.

Currently, the rapid expansion of the global road transport industry and the imperative to reduce carbon emissions are propelling the advancement of electrified highways (EH). In order to conduct a comprehensive economic analysis of EH, it is crucial to develop a detailed /8.and comprehensive economic model that takes into account various transportation modes and factors that influence the economy. However, the existing economic models for EH lack comprehensiveness in terms of considering different transportation modes and economic factors. This study aims to fill this gap by designing an economic model for an EH-based Online DC-driven system (ODS) for long distance heavy-duty transport vehicle incorporating multi-factor sensitivities. Firstly, the performance parameters of the key components of the system are calculated using vehicle dynamics equations which involves selecting and matching the relevant components and determining the fundamental cost of vehicle transformation.

Driving dynamics performance is one of the key customer attributes to be developed during product development. In the vehicle development process, freezing the hardware of the chassis aggregates is one of the major priorities to kick off the other vehicle development activities. The current work involves the development of a multilink suspension for an SUV class vehicle. Typically, each OEM performs several product development loops for maturing the vehicle design. The driving dynamics performance evaluation and tuning happens on a physical vehicle with the driver in Loop. Tuning of suspension parameter on the physical vehicle entails actual replacement of parts/components. This encompasses multiple tuning cycles in product development associated with increased cost and test time. To reduce the product development time and cost while delivering first time right chassis configuration, we took an approach of getting driver-in-loop through driving simulator in the concept phase.

For any two wheeler vehicle development, rider and pillion comfort while driving the vehicles over different kinds of road perturbations holds high importance. Designing a vehicle for comfort starts at the very beginning of its layout definition through vehicle geometric parameters, key hardpoints, mass-inertia distribution of subsystems and suspension characteristics. There is a need for highly reliable simulation models for comfort predictions as any change in layout during subsequent design stages is a very costly affair. Accurately predicting comfort using a full vehicle model is a challenging task though as it depends on how realistic the Simulation Model is to that of actual vehicle. While suspension stiffness and damping characteristics remain critical parameters for the comfort, selection of tyres are known to hold equal importance in vehicle comfort.

Potholes are a major cause of discomfort for riders and vehicle damage. The passive suspension systems which are used in the passenger vehicles are primarily reaction based. These can’t adapt to the changing road conditions which means the best ride quality and handling characteristics cannot be ensured for different driving situations. Passive suspension system also needs more maintenance due to its inability to reduce the impact of the road irregularities. In recent years, semi-active suspension systems have been developed to improve ride comfort and vehicle safety. This paper covers the integration of a semi-active suspension system with a road preview mechanism with a TATA car model to investigate its impact on ride comfort, handling characteristics and component loads in digital domain. A quarter car vehicle model is used to compare different active damping control strategies.

Virtual simulation is a fundamental tool for the development of new vehicles, both for individual components and for complete subsystems and full vehicles. Many software tools exist in the automotive sector to assess full-vehicle behavior and performance, including multibody software and algorithms based on 14 (or more) degrees-of-freedom vehicle dynamics models. In order to reproduce the testing maneuvers and typical vehicle mission, a key part of such simulation tools is the virtual driver algorithm. It is essential to implement a control logic that reproduces the handling response of the driver, so that the closed-loop maneuvers can be evaluated. However, the response of typical virtual drivers is not always similar to the human driving characteristics. Virtual driver algorithms can perform very fast, precise, and smooth steering and pedal actions, while humans display a more variable, delayed and often not optimal actions.

This paper focuses on lane-changing trajectory planning and trajectory tracking control in autonomous vehicle technology. Aiming at the lane-changing behavior of autonomous vehicles, this paper proposes a new lane-changing trajectory planning method based on particle swarm optimization (PSO) improved third-order Bezier curve path planning and polynomial curve speed planning. The position of Bezier curve control points is optimized by the particle swarm optimization algorithm, and the lane-changing trajectory is optimized to improve the comfort of lane changing process. Under the constraints of no-collision and vehicle dynamics, the proposed method can ensure that the optimal lane-changing trajectory can be found in different lane-changing scenarios. To verify the feasibility of the above planning algorithm, this paper designs the lateral and longitudinal controllers for trajectory tracking control based on the vehicle dynamic tracking error model.

The main objective of platoon control is coordinated motion of autonomous vehicle platooning with small intervehicle spacing while maintaining the same speed and acceleration as the leading vehicle, which can save energy consumption and improve traffic throughput. The conventional platoon control methods are confronted with the problem of manual parameter tuning. In order to addres this isue, a novel bifold platoon control approach leveraging a deep reinforcement learning-based model is proposed, which enables the platoon adapt to the complex traffic environment, and guarantees the safety of platoon. The upper layer controller based on the TD3 tuned PID algorithm outputs the desired acceleration. This integration mitigates the inconvenience of frequent manual parameter tuning asociated with the conventional PID algorithm. The lower layer controller tracks the desired acceleration based on the inverse vehicle dynamics model and feedback control.

The driving risk field model offers a feasible approach for assessing driving risks and planning safe trajectory in complex traffic scenarios. However, the conventional risk field fails to account for the vehicle size and acceleration, results in the same trajectories are generated when facing different vehicle types and unable to make safe decisions in emergency situations. Therefore, this paper firstly introduces the acceleration and vehicle size of surrounding vehicles for improving the driving risk model. Then, an integrated decision-making and planning model is proposed based on the combination of the novelty risk field and model predictive control (MPC), in which driving risk and vehicle dynamics constraints are taken into consideration. Finally, the multiple driving scenarios are designed and analyzed for validate the proposed model.

This SAE Recommended Practice defines key terms used in the description and analysis of video based driver eye glance behavior, as well as guidance in the analysis of that data. The information provided in this practiced is intended to provide consistency for terms, definitions, and analysis techniques. This practice is to be used in laboratory, driving simulator, and on-road evaluations of how people drive, with particular emphasis on evaluating Driver Vehicle Interfaces (DVIs; e.g., in-vehicle multimedia systems, controls and displays). In terms of how such data are reduced, this version only concerns manual video-based techniques. However, even in its current form, the practice should be useful for describing the performance of automated sensors (eye trackers) and automated reduction (computer vision).

Battery electric vehicle (BEV) adoption and complex powertrains pose new challenges to automotive industries, requiring comprehensive testing and validation strategies for reliability and safety. Hardware-in-the-loop (HIL) based real-time simulation is important, with cooperative simulation (co-simulation) being an effective way to verify system functionality across domains. Fault injection testing (FIT) is crucial for standards like ISO 26262. This study proposes a HIL-based real-time co-simulation environment that enables fault injection tests in BEVs to allow evaluation of their effects on the safety of the vehicle. A Typhoon HIL system is used in combination with the IPG CarMaker environment. A four-wheel drive BEV model is built, considering high-fidelity electrical models of the powertrain components (inverter, electric machine, traction battery) and the battery management system (BMS).

The aeroacoustic development of vehicles is still mainly carried out in wind tunnels under steady flow conditions, although the real situation is different. However, as discussed in several earlier publications, a vehicle experiences unsteady, turbulent flow on road, which results for example from natural wind, wakes of other vehicles, or obstacles at the roadside in combination with side wind. The resulting temporal variations of the wind noise inside the cabin affect the passengers’ comfort and safety through fatigue. To be able to also consider the unsteady aeroacoustics in the vehicle development process, a comprehensive method has been developed that is presented in full for the first time in this paper. The on-road situation is simulated in a realistic and reproducible manner in the full-scale wind tunnel of the University of Stuttgart by means of an active turbulence generator, developed by FKFS.

Extremely uncomfortable levels of bounce and pitch vibrations are produced when a CV moves over uneven terrain. The present study was carried out to ascertain the vibrational response at the driver’s, commander’s and trooper’s seats. A 23 -degrees of freedom (DOF) lumped parameter 3-D model of a combined CV and human body was made. The vehicle had 15 DOF corresponding to the bounce, pitch and roll of the hull (sprung mass) and bounce motions of the 12 wheel stations (unsprung masses) on either side. The human body was idealized as having 8 DOF corresponding to bounce motions of the pelvis, abdomen, diaphragm, thorax, torso, back and head. The seat was also assigned a bounce DOF. The lumped masses of the body parts were distributed and connected by springs. The differential equations of motion for the linear rigid body model were formulated and the natural frequencies of different parts of the human body and the military tank were determined by eigenvalue analysis using MATLAB.

It is estimated that the share of autonomous vehicles in the market will reach an important point between 2050s and 2060s. Some major benefits of autonomy in ground vehicles can be regarded as reducing traffic, saving fuel and reducing emissions. Accordingly, it is anticipated that autonomous vehicles (AVs) will prevent driver error from happening, which is the primary cause of 90% of traffic accidents. However, it is a prerequisite that the AVs are accepted by the public, and be used regularly in daily life. AVs obliges everyone to be a passenger, thereby occupants will lose authority on the vehicle and have to deal with non-driving tasks during an automated ride. This will increase the lack of situational awareness, leading occupants to be more sensitive to motion sickness, where the major reasons of motion sickness are conflict between vestibular and visual senses, lack of control, unable to predict the direction of movement.