Electrical and Information Technology

Faculty of Engineering, LTH

Denna sida på svenska This page in English

Zahra Chamideh improved self-driving cars.


Zahra Chamideh

Title of thesis: Navigating the Future: Intersection of Safety, Efficiency, and Resilience in Autonomous Traffic Systems.

Link to thesis in Lund University Research Portal.

Defence: Tuesday March 12th, 09:15, room E:1406.
Zoom link.  Zoom ID: 66179207826.

Describe your research in a popular science way

Imagine a world where your morning commute is no longer a battle with endless red lights and traffic jams. Envision instead a scenario where your car passes through intersections, in seamless conversation with both its vehicular peers and the infrastructure that guides them. This isn't science fiction; it's the emerging reality of autonomous vehicles and intelligent traffic management systems. Across our cities, we're beginning to see glimpses of this future. Smart traffic management systems adjust based on real-time traffic flow, reducing congestion and idling. Autonomous vehicles, already on our roads, promise a future of hands-free, stress-free driving. These advancements are not just about convenience; they're about creating safer, cleaner, and more efficient urban environments.

But what happens when the technologies we rely on face glitches? Picture this: you're in a self-driving car, approaching an intersection. Suddenly, the car's positioning system malfunctions, or it loses the ability to communicate with other vehicles. In such a scenario, the harmonious flow of traffic could be disrupted, leading to confusion or even accidents. This is not just a theoretical concern. The reality is that our technology, as advanced as it is, isn't perfect. Communication breakdowns, GPS inaccuracies, or even cyber threats can pose significant risks in a system that relies heavily on precision and inter-connectivity.

This is where my work comes into play. Recognizing these challenges, my thesis focuses on developing a resilient Autonomous Intersection Management (AIM) system. This system is designed to withstand technological imperfections and unforeseen circumstances. By employing advanced algorithms and reinforcement learning techniques, the AIM system can adapt and respond to various disruptions, ensuring that traffic continues to flow smoothly and safely, even when individual components fail or behave unpredictably.

What made you want to pursue a PhD?

I saw a chance to really dive into how self-driving cars and intelligent traffic systems could change our daily commutes. I was curious about making these technologies not just cool but also really reliable and safe, especially when things go wrong.

What is the most fascinating or interesting with your thesis subject?

This research is not only fascinating due to its innovative use of cutting-edge technology but also because of its significant real-world implications. By prioritizing safety, reliability, and efficiency, my work attempts to address key challenges in autonomous vehicular network, offering solutions that could impact future urban mobility and smart city infrastructure.

Do you believe some results from your research will be applied in practice eventually? And if so, how / how?

Absolutely, my research on improving traffic management has a strong chance of being applied in the real world. It might not mean my system gets used exactly as it is, but I'm pretty excited about how it brings new ways of thinking, analyzing, and improving traffic systems to the table.  Think of it getting into smart city projects, influencing how self-driving cars are controlled. My research could be a big deal in making our streets smarter and safer.

What are your plans?

As my interest in the field, which already from the start was high, has increased further, I would like to continue in the field. I wish to get involved in future projects that aim to make vehicles as well as roads safe at the same time as the traffic flow is efficient.