Högskolan i Skövde

This paper presents an interview study aiming to understand the state of the art of how ergonomics designers work in the vehicle development process within the Swedish automotive industry. Ten ergonomic designers from seven different companies participated in the interview study. Results report the ergonomics designers' objectives, workflow, tools, challenges, and ideal work performance tool. We identify four main gaps and research directions that can enhance the current challenges: human behavior predictions, simulation tool usability, ergonomics evaluations, and integration between systems.

DHM tools have been widely used to analyze and improve vehicle occupant packaging and interior design in the automotive industry. However, these tools still present some limitations for this application. Accurately characterizing seated posture is crucial for ergonomic and safety evaluations. Current human posture and motion predictions in DHM tools are not accurate enough for the precise nature of vehicle interior design, typically requiring manual adjustments from DHM users to get more accurate driving and passenger simulations. Manual adjustment processes can be time-consuming, tedious, and subjective, easily causing non-repeatable simulation results. These limitations create the need to validate the simulation results with real-world studies, which increases the cost and time in the vehicle development process. Working with multiple Swedish automotive companies, we have begun to identify and specify the limitations of DHM tools relating to driver and passenger posture predictions given predefined vehicle geometry points/coordinates and specific human body parts relationships. Two general issues frame the core limitations. First, human kinematic models used in DHM tools are based on biomechanics models that do not provide definitions of these models in relation to vehicle geometries. Second, vehicle designers follow standards and regulations to obtain key human reference points in seated occupant locations. However, these reference points can fail to capture the range of human variability. This paper describes the relationship between a seated reference point and a biomechanical hip joint for driving simulations. The lack of standardized connection between occupant packaging guidelines and the biomechanical knowledge of humans creates a limitation for ergonomics designers and DHM users. We assess previous studies addressing hip joint estimation from different fields to establish the key aspects that might affect the relationship between standard vehicle geometry points and the hip joint. Then we suggest a procedure for standardizing points in human models within DHM tools. A better understanding of this problem may contribute to achieving closer to reality driving posture simulations and facilitating communication of ergonomics requirements to the design team within the product development process.

Occupant packaging design is usually done using computer-aided design (CAD) and digital human modelling (DHM) tools. These tools help engineers and designers explore and identify vehicle cabin configurations that meet accommodation targets. However, studies indicate that current working methods are complicated and iterative, leading to time-consuming design procedures and reduced investigations of the solution space, in turn meaning that successful design solutions may not be discovered. This paper investigates potential advantages and challenges in using an automated simulation-based multi-objective optimization (SBMOO) method combined with a DHM tool to improve the occupant packaging design process. Specifically, the paper studies how SBMOO using a genetic algorithm can address challenges introduced by human anthropometric and postural variability in occupant packaging design. The investigation focuses on a fabricated design scenario involving the spatial location of the seat and steering wheel, as well as seat angle, taking into account ergonomics objectives and constraints for various end-users. The study indicates that the SBMOO-based method can improve effectiveness and aid designers in considering human variability in the occupant packaging design process.

Accurately predicting driving postures and positions is crucial for occupant packaging design to accommodate diverse drivers. However, this is challenging due to individual variability and limited access to user data. Digital human modelling (DHM) tools enable posture prediction in virtual environments. This paper presents a case study comparing two driving prediction methods: a statistical prediction method (SPM) and an optimisation prediction method (OPM). Both were evaluated using data from two car models with different seat heights, involving 199 participants whose seat, eye-point, and steering wheel positions were measured. Results showed SPM was more accurate for vertical positioning, whereas OPM for fore-aft positioning. The effectiveness of each method varied by car model, with SPM aligning better in the higher-seated vehicle and OPM performing better in the lower-seated vehicle. These findings highlight the practical, context-specific performance of posture prediction methods. Methodological insights guide the improvement of DHM tool use in occupant packaging.

This paper presents a process for enhancing driver posture prediction in digital human modelling (DHM) tools by integrating real-world data from driver posture studies. The process leverages key data points such as seat position, steering wheel position, and eye point coordinates to position manikins and extract joint angles for ergonomic analysis. A test study involving 49 drivers was conducted, revealing significant variations in joint angles across different statures. These findings were used to develop stature-based strategies that demonstrated improved predictive accuracy for short and tall stature groups compared to the existing strategy in the DHM tool IPS IMMA. While the results highlight the potential benefits of this approach, limitations such as refined manikin body meshes, seat property considerations, and broader vehicle model validation are recommended. Overall, this method offers a promising solution for addressing incomplete datasets in occupant packaging studies, contributing to the development of more ergonomic and safer vehicle designs.

Predicting human upper extremity reaching motion in 3D space can support adaptive interactions with computer-controlled systems (robots and virtual avatars), and applications in ergonomics and rehabilitation. This study compares two predictive approaches: an optimization-based method (OPM) and a proposed heuristic method (SFM) that integrates steering dynamics path planning, an adaptive velocity model, and inverse kinematics. Both methods were validated against motion capture data from ten participants performing four reach tasks. Predictions and inter-subject variability were evaluated for path, velocity, and upper extremity joint configuration using root mean square error and dynamic time warping. Results show that SFM more accurately predicts spatial path and velocity, whereas OPM achieves greater precision in joint angle estimation. As input, OPM requires the initial and end posturesand the task duration, while SFM needs the initial posture, initial and target end-effector positions, and initial and estimated peak velocity. These results highlight trade-offs between accuracy and behavioral variability when selecting motion prediction methods.