Modeling & Simulation Framework for Human-Aware Design and Manufacturing

About the Project

This research proposes a design framework that quantifies obstruction zones caused by automotive A-pillars—vertical structural elements that connect the windshield and side windows to the roof and provide critical structural integrity and occupant protection. Although essential for safety and aerodynamic performance, increased A-pillar thickness reduces drivers’ forward Field-of View (FoV), enlarges blind-spot regions, and limits the detection of traffic objects, thereby increasing accident risk.

This project proposes an early-stage design framework that integrates Generative Design and Digital Human Modelling to evaluate A-pillar obstruction under realistic traffic conditions. The framework is demonstrated through a traffic simulation study comparing concept pillar designs with see-through cutout sections to conventional solid pillars, assessing reductions in obstruction zones and improvements in driver visibility.

This work is guided by the principle of “see and be seen,” emphasizing mutual visibility between drivers and vulnerable road users—an interaction constrained by conventional pillar designs but enabled through see-through structural concepts. Beyond vehicle design, the proposed approach supports urban planning, infrastructure design, and policy development by enabling stakeholders (e.g., city planners, architects, cyclists) to visualize and experience design concepts using immersive methods (VR/AR) before committing to large-scale, resource-intensive interventions. Key contributions expected:

• Establish the design and modelling foundations: Investigate computational approaches (e.g., surface modelling, CAD, generative design) to model A-pillar concepts
• Develop the visualization framework: Use DHM and image processing techniques to quantify A-pillar obstruction, driver FoV, and design trade-offs in early-stage exploration.
• Fidelity check: Build validation studies that synthesizes three— (1) human (driver anthropometry and behaviour), (2) environment (vehicle and road context), and (3) simulation (DHM, ray-casting, and image-based methods) towards coupled human-vehicle-environment conditions.
• Validate and translate into manufacturing: Combine computational simulations (digital manikins, image processing) with empirical studies (human-in-the-loop experiments) to evaluate visibility improvements, and link results to manufacturing pathways (e.g., hydroforming) for feasible A-pillar designs.

Funding Notes

This PhD project is funded by the John Anderson Research Studentship Scheme (JARSS). It covers UK home tuition fees and an annual tax-free stipend. Additional funding may be available to cover travel to conferences and academic events, software, and equipment costs.