Effective emission control technologies and eco-friendly propulsion systems have been developed to decrease exhaust particle emissions. However, more work must be conducted on non-exhaust traffic-related sources such as tyre wear. The advent of automated vehicles (AVs) enables researchers and automotive manufacturers to consider ways to further decrease tyre wear, as vehicles will be controlled by the system rather than by the driver. In this direction, this work presents the formulation of an optimal control problem for the trajectory optimisation of automated articulated vehicles for tyre wear minimisation. The optimum velocity profile is sought for a predefined road path from a specific starting point to a final one to minimise tyre wear in fixed time cases. Specific boundaries and constraints are applied to the problem to ensure the vehicle’s stability and the feasibility of the solution. According to the results, a small increase in the journey time leads to a significant decrease in the mass loss due to tyre wear. The employment of articulated vehicles with low powertrain capabilities leads to greater tyre wear, while excessive increases in powertrain capabilities are not required. The conclusions pave the way for AV researchers and manufacturers to consider tyre wear in their control modules and come closer to the zero-emission goal.@en

Head motion is a key determinant of motion comfort and differs substantially from seat motion due to seat and body compliance and dynamic postural stabilization. This paper compares different human body model fidelities to transmit seat accelerations to the head for the assessment of motion comfort through simulations. Six-degree of freedom dynamics were analyzed using frequency response functions derived from an advanced human model (AHM), a computationally efficient human model (EHM) and experimental studies. Simulations of dynamic driving show that human models strongly affected the predicted ride comfort (increased up to a factor 3). Furthermore, they modestly affected sickness using the available filters from the literature and ISO-2631 (increased up to 30%), but more strongly affected sickness predicted by the subjective vertical conflict (SVC) model (increased up to 70%).

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Remote driving plays an essential role in coordinating automated vehicles in some challenging situations. Due to the changed driving environment, the experiences and behaviors of remote drivers would undergo some changes compared to conventional drivers. To study this, a continuous real-life and remote driving experiment is conducted under different driving conditions. In addition, the effect of steering force feedback (SFF) on the driving experience is also investigated. In order to achieve this, three types of SFF modes are compared. According to the results, no SFF significantly worsens the driving experience in both remote and real-life driving. Additionally, less force and returnability on steering wheel are needed in remote driving, and the steering force amplitude appears to influence the steering velocity of remote drivers. Furthermore, there is an increase in lane following deviation during remote driving. Remote drivers are also prone to driving at lower speeds and have a higher steering reversal rate. They also give larger steering angle inputs when crossing the cones in a slalom manoeuvre and cause the car to experience larger lateral acceleration. These findings provide indications on how to design SFF and how driving behavior and experience change in remote driving.

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Existing models of vibration transmission through the seated human body are primarily two-dimensional, focusing on the mid-sagittal plane and in-plane excitation. However, these models have limitations when the human body is subjected to vibrations in the mid-coronal plane. Three-dimensional (3D) human models have been primarily developed for impact analysis. Recently, we showed that such a 3D active human model can also predict vibration transmission. However, existing 3D body models suffer from excessive computational time requirements due to their complexity. To effectively analyze motion comfort, this research presents a 3D computationally efficient human model (EHM), running faster than real-time, with scope for real-time vehicle and seat motion control to enhance comfort. The EHM is developed by considering various combinations of body segments and joint degrees of freedom, interacting with multibody (MB) and finite element (FE) seat compliance models. Postural stabilization parameters are estimated using an optimization process based on experimental frequency-dependent gain responses for different postures (erect/slouched) and backrest support (low/high) conditions. The model combines two postural control mechanisms: 1) joint angle control capturing reflexive and intrinsic stabilization for each degree of freedom with PID controllers, including integration to eliminate drift, and 2) head-in-space control minimizing 3D head rotation. Interaction with a compliant seat was modeled using deformable finite elements and multibody contact models. Results showed the importance of modeling both compressive and shear deformation of the seat and the human body. Traditional stick-slip multibody contact failed to reproduce seat-to-human vibration transmission. Combining efficient body modeling principles, innovative postural adaptation techniques, and advanced seat contact strategies, this study lays a robust foundation for predicting and optimizing motion comfort.

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Teleoperation is considered as a viable option to control fully automated vehicles (AVs) of Level 4 and 5 in special conditions. However, by bringing the remote drivers in the loop, their driving experience should be realistic to secure safe and comfortable remote control. Therefore, the remote control tower should be designed such that remote drivers receive high quality cues regarding the vehicle state and the driving environment. In this direction, the steering feedback could be manipulated to provide feedback to the remote drivers regarding how the vehicle reacts to their commands. However, until now, it is unclear how the remote drivers' steering feel could impact occupant's motion comfort. This paper focuses on exploring how the driver feel in remote (RD) and normal driving (ND) are related with occupants' motion comfort. More specifically, different types of steering feedback controllers are applied in (a) the steering system of a Research Concept Vehicle-model E (RCV-E) and (b) the steering system of a remote control tower. An experiment was performed to assess driver feel when the RCV-E is normally and remotely driven. Subjective assessment and objective metrics are employed to assess drivers' feel and occupants' motion comfort in both remote and normal driving scenarios. The results illustrate that motion sickness and ride comfort are dominated by steering velocity variations in remote driving, while throttle input variations dominate in normal driving. The results demonstrate that motion sickness and steering velocity increase both around 25% from normal to remote driving.

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In this paper, a path-tracking controller is developed for an autonomous vehicle with All-Wheel-Steering (AWS) capability. Based on nonlinear model predictive control, the proposed controller is formulated in a way that allows the manipulation of vehicle’s attitude during path-tracking. With high-fidelity vehicle dynamics simulation, the controller is examined at various velocities up to the limit handling condition. Comparison is carried out in the aspects of path-tracking, ride comfort and motion sickness, between the implementation with a constant yaw angle reference (referred to as crab steering) and the nominal steering behaviour for negotiating the same path. The ride comfort metric suggested by ISO-2631 is used to capture the benefits of the crab steering approach against the nominal case, and the simulation results reveal that crab steering is able to enhance the ride comfort for AWS vehicles in double lane-change and slalom manoeuvres.

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The adoption of automated vehicles will be a positive step towards road safety and environmental benefits. However, one major challenge that still exist is motion sickness. The move from drivers to passengers who will engage in non-driving related tasks as well as the potential change in the layout of the car interior that will come with automated vehicles are expected to result in a worsened experience of motion sickness. The previous workshop [18] highlighted the need for consensus on guidelines regarding study design for motion sickness research. Hence, this workshop will develop a guide for motion sickness research through reflection and discussions on the current methodologies used by experts in the field. Further it will build on the knowledge collected from the previous workshop and will thereby facilitate not only new research ideas and fruitful collaborations but also find a consensus in the field in regard to study design and methodologies.

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The goal of this paper is to contribute to the accurate prediction of human body motion by proposing a novel head-neck model for dynamic driving scenarios with complex vehicle motions. While automated vehicles are considered a potential solution to several transportation issues, there are still significant challenges that need to be addressed, including fundamental questions regarding motion comfort and postural stability. Existing standards fail to accurately describe motion comfort, and current head-neck models have limitations, such as their inability to accurately capture human head responses to dynamic perturbations and lack of adaptability to different perturbations, amplitudes, and individual characteristics. To address these challenges, the authors propose a 3D double inverted pendulum model (DIPM) with a total of 6 degrees of freedom (DoF) as an approximation of head-neck system. The proposed model uses Model Predictive Control (MPC) to derive optimal control inputs for head-neck stabilization. The study validates the proposed model against experimental data of anterior-posterior seat translation and rotation from the literature. The results indicate that the model fitted the experimental data with a variance accounted for 82.80 % in translation and 73.15 % in rotation (pitch). The proposed model paves the path for the accurate assessment of occupants’ postural stability in automated vehicles.

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Active muscles are crucial for maintaining postural stability when seated in a moving vehicle. Advanced active 3D non-linear full body models have been developed for impact and comfort simulation, including large numbers of individual muscle elements, and detailed non-linear models of the joint structures. While such models have an apparent potential to provide insight into postural stabilization, they are computationally demanding, making them less practical in particular for driving comfort where long time periods are to be studied. In vibrational comfort and in general biomechanical research, linearized models are effectively used. This paper evaluates the effectiveness of simplified 3D full-body human models to capture comfort provoked by whole-body vibrations. An efficient seated human body model is developed and validated using experimental data. We evaluate the required complexity in terms of joints and degrees of freedom for the spine, and explore how well linear spring-damper models can approximate reflexive postural stabilization. Results indicate that linear stiffness and damping models can well capture the human response. However, the results are improved by adding proportional integral derivative (PID) and head-in-space (HIS) controllers to maintain the defined initial body posture. The integrator is shown to be essential to prevent drift from the defined posture. The joint angular relative displacement is used as the input reference to each PID controller. With this model, a faster than real-time solution is obtained when used with a simple seat model. The paper also discusses the advantages and disadvantages of various models and provides insight into which models are more appropriate for motion comfort analysis. For designers and researchers in the automotive and seating industries, the findings given in this paper provide useful insights that will help them improve the comfort and safety of both vehicle occupants and seats.

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In the transition from partial to high automation, occupants will no longer be actively involved in driving. This will allow the use of travel time for work or leisure, where high comfort levels preventing motion sickness are required. In this paper, an optimal trajectory planning algorithm is presented in order to minimise motion sickness in automated vehicles. A predefined path is provided as an input to the algorithm, to generate an optimal path with limited lateral deviation and the corresponding optimal velocity profile, for the minimisation of motion sickness. An optimal control problem is formulated with a cost function combining both motion sickness and travel time. For a sickening curvy road, the algorithm reduced the motion sickness dose value (MSDV) up to 52% depending on the allowed lateral deviation and the weighting on travel time. The efficacy of the proposed algorithm has been evaluated via human-in-the-loop experiments using a moving-base driving simulator. Motion cueing parameters were selected to optimally transmit the sickening stimuli resulting in close to full vibration transmission above 0.2 Hz. During the experiment, the participants were asked to rate their experience based on the standard MIsery SCore ratings. According to these, sickness levels were reduced on average by 65% with reduced motion sickness in all 16 participants.

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Due to the complexity of the human body and its neuromuscular stabilization, it has been challenging to efficiently and accurately predict human motion and capture posture while being driven. Existing simple models of the seated human body are mostly two-dimensional and developed in the mid-sagittal plane exposed to in-plane excitation. Such models capture fore-aft and vertical motion but not the more complex 3D motions due to lateral loading. Advanced 3D full body active human models (AHMs), such as in MADYMO, can be used for comfort analysis and to investigate how vibrations influence the human body while being driven. However, such AHMs are very time-consuming due to their complexity. To effectively analyze motion comfort, a computationally efficient and accurate three dimensional (3D) human model, which runs faster than real time, is presented. The model's postural stabilization parameters are tuned using available 3D vibration data for head, trunk and pelvis translation and rotation. A comparison between AHM and EHM is conducted regarding human body kinematics. According to the results, the EHM model configuration with two neck joints, two torso bending joints, and a spinal compression joint accurately predicts body kinematics.

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The mass adoption of automated vehicles in the near future will benefit safety (of occupants and pedestrians), the environment (low emissions), and society (accessibility, on-demand travel). There are, however, still challenges that need to be addressed, with one of the most crucial being motion sickness. In automated vehicles, the interior could be transformed into a living room or a working space, allowing occupants to spend their time with non-driving activities. These changes are likely to provoke, and increase, motion sickness incidence. To that end, this workshop will explore the current state of motion sickness detection and mitigation methods from different angles (e.g., closed-loop detection, multimodal motion cues,etc.) through expert talks and reflections, followed by discussions. The workshop will develop an agenda for motion sickness research in automated vehicles, facilitate new research ideas and fruitful collaborations.

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