In automotive design, the A-pillar is critical for structural integrity and aerodynamics but creates forward blind spots that obstruct the driver’s field of view (FoV), increasing safety risks. Existing methods for assessing A-pillar obstruction lack scalability and are poorly suited for early-stage design exploration. This project addresses this gap by combining DHM with computational design methods (such as generative design) to inform manufacturing decisions that improve safety and sustainability, including reduced material use. The project will also create opportunities for collaboration with manufacturing research centres focused on advanced design and manufacturing applications, strengthening the translation of design concepts into practice.

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 (such as city planners, architects, cyclists) to visualise and experience design concepts using immersive methods (VR/AR) before committing to large-scale, resource-intensive interventions.

Key contributions expected are:

• establish the design and modelling foundations: Investigate computational approaches (such as 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
  • human (driver anthropometry and behaviour)
  • environment (vehicle and road context)
  • 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 (such as hydroforming) for feasible A-pillar designs