Nigerian Engineer, Yusuf Develops GPU Powered Lane Tracking Technology for Safer Autonomous Vehicles

By Esther Oluku

In the global effort to improve vehicle safety, practical engineering advances continue to shape the future of autonomous and assisted driving systems.

One such contribution is emerging from Nigerian engineer Ayomide Adeyemi Yusuf, whose research focuses on real time lane detection and lane tracking designed to operate efficiently on embedded automotive hardware.

Lane tracking is a core component of Advanced Driver Assistance Systems, supporting functions such as lane keeping, lane centering and lane departure warnings.

Unlike static image analysis, lane tracking requires continuous processing of camera frames as a vehicle moves, with each frame updating the vehicle’s position relative to lane boundaries.

Engineers note that even minor delays can significantly affect steering corrections and safety alerts.

Yusuf explained that his work was driven by the need to meet real world constraints faced by production vehicles.

He said, “Lane tracking systems must operate in real time on embedded platforms with limited computing power and strict energy budgets, rather than on high performance desktop systems that are impractical for deployment in cars.”

To address this challenge, Yusuf designed and evaluated a lane tracking pipeline on two platforms.

One implementation ran on a conventional CPU system, while the other was deployed on an embedded GPU platform optimized for low power operation.

Performance measurements showed that the GPU based system achieved processing speeds up to twenty times faster than the CPU baseline.

The CPU platform used an Intel i5 6330 processor running at 2.5 gigahertz, while the embedded GPU system operated at an estimated power range of about 7.5 watts.

According to Yusuf, the results demonstrated the feasibility of achieving high speed performance within realistic automotive power constraints.

He noted that performance gains without consideration for energy efficiency offer limited value for real vehicles.

Industry experts say the evaluation approach strengthens the relevance of the work. Raveen Mustala, a senior Advanced Driver Assistance Systems engineer with more than fifteen years of experience in embedded perception systems, said the emphasis on testing under realistic hardware and power conditions sets the research apart.

He noted that many academic studies focus on algorithmic accuracy without addressing embedded feasibility, which is critical for production systems.

“Yusuf’s lane tracking pipeline relied on explainable computer vision techniques commonly trusted in safety critical applications. These included grayscale conversion, noise reduction, edge detection, region of interest selection and line estimation.

Such transparency allows engineers to inspect system behavior, validate performance and diagnose failures, which is essential for safety certification.

“The research also accounted for real world driving conditions such as faded lane markings, shadows, variable lighting and construction zones. Rather than treating lane detection as a single frame problem, Yusuf approached it as a continuous estimation task, prioritizing stability over time alongside speed.

“Yusuf’s work aligns with industry expectations for deployable systems, which are typically assessed on whether they run fast enough, remain stable and can operate on embedded hardware. Experts say many research efforts fall short on the third requirement.”

As vehicles become increasingly software driven, contributions that balance performance, reliability and hardware constraints are gaining attention.

Yusuf said his goal was not to produce impressive benchmarks, but to build systems that can function reliably where drivers depend on them.

Analysts believe this type of research highlights how progress in autonomous and assisted driving often comes through measured, constraint aware engineering rather than headline grabbing demonstrations.