Leveraging Collaborative Expertise to Enhance Terrain-Tyre Interaction Modelling and Develop Cutting-Edge Solutions.

The Challenge

In collaboration with a leading South African research institution, we undertook a technically demanding project aimed at enhancing off-road vehicle simulation. Over an intensive 18-month engagement, we helped the client tackle one of the most intricate engineering challenges in autonomy—accurately modelling the dynamic interaction between tyres and varying terrain surfaces.

This wasn’t just about crunching numbers—it was about embedding cutting-edge modelling tools and expert processes into a complex research environment to unlock new frontiers in vehicle autonomy and hybridisation.

The project originated from a broader research initiative to investigate terrain-tyre interactions across diverse soil types—an essential precursor to the development of autonomous ground vehicles. However, the team faced two major hurdles:

• Lack of Simulation Capability: Accurately simulating variable terrain and tyre behaviour was outside their existing toolchain and expertise.
• Limited Resources: Internal constraints slowed progress and threatened long-term research goals.

As the project evolved, the mandate expanded to include development of a digital twin of a fully parameterised vehicle, with two future-facing goals: enabling autonomous control and retrofitting an Internal Combustion Engine (ICE) with a hybrid powertrain.

Adding to the complexity was the early-stage ambiguity of the research application. It became clear that solving the problem required deep technical integration, significant upskilling, and a rethinking of how simulation tools could support their broader strategy.

The Solution

We embedded a high-impact team to work closely alongside the researchers, leveraging deep domain expertise and MathWorks tools to bridge the technology gap.

Key Elements of Our Approach:

• Digital Twin Development: We built a highly accurate, modular digital twin of the vehicle using MATLAB, Simulink, and the Vehicle Dynamics Blockset. This enabled real-time scenario testing and paved the way for autonomous behaviour simulation.
• Collaborative Modelling Workflow: By aligning closely with the client’s engineers, we transformed fragmented processes into a shared, iterative workflow, using whiteboard design sessions, code reviews, and transparent documentation.
• Parameterisation and Data Gathering: We collaborated with third parties to extract detailed vehicle specs and used this data to calibrate the model, ensuring a high-fidelity response to terrain conditions.
• Resource Augmentation: With our experts onboard, the client could rapidly scale their research capacity, take on additional contracts, and meet their commercial mandates more efficiently.
• Toolchain Modernisation: We modernised their simulation environment by integrating legacy code with Simscape-based subsystems and efficient data pipelines—enabling faster, more insightful experimentation.

The Outcome

The collaboration resulted in significant benefits for the Research Institution, both technically and strategically enhancing their research capabilities:

• Accelerated Research Delivery: With our resources bolstering the team, they were able to produce high-quality work in less time. This efficiency contributed to winning additional contracts and securing funding to expand their research initiatives.
• Robust Vehicle Dynamics Solutions: The digital twin model developed through our efforts provided a foundational platform for simulating self-driving vehicle capabilities, allowing them to differentiate themselves in the market. This capability enhances their potential to innovate in autonomous vehicle research.
• Streamlined Integration of Research Outputs: The project facilitated the development of robust solutions that met the South African Research Institution’s mandated commercial goals via improved internal processes and accelerated output delivery.
• Enhanced Market Competitiveness: By establishing a framework for simulating complex vehicle dynamics, they not only advanced their research objectives but also reinforced their reputation as a leader in the field of autonomous vehicle technology.

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In summary, our collaboration successfully addressed the challenges of simulating terrain-tyre interactions while fostering innovation in vehicle dynamics. This case exemplifies how targeted resource integration and technical collaboration can lead to substantial improvements in research delivery and market relevance in increasingly complex technological landscapes.