kamran_rzayev’s Site

The final briefing is 2 weeks away. For the initial briefing I have changed the topic from Formula 1 winner prediction to Advanced Vehicular Telemetry System since for this one I could answer more Heilmeier’s question than in any other project of my choise. My main mistake was that I didn't point out answers for Heilmeier’s questions explicitly as question - answer therefore it confused the audience and complicated the structure of the passage. By final briefing I'll have it fixed. The project that I have chosen for now is more complicated because it has some additional hardware involved, and the amount of work is pretty decent for a single person. For now it seems to me that it worths the risk because in the end I'll get the solution that I am excited about and I will most probably use it myself. After the next lecture I plan to consult with professors to see their opinion on how complex it is and whether it needs to be simlified in the scope of this MS thesis. Either way I want to make it modular and extendable for future changes.

As I mentioned before, it is quite challenging to come up with a real-world problem as it requires some expertise in the area of research and some critical thinking. In the following posts, I will try to come up with some ideas that could solve problems in the areas of my interest. As well as seek data that I could exploit for training or assessing. 

Here is one of them:

Optimal strategy selection (or race outcome prediction given the strategies) for Baku City Circuit Formula 1

One of the major challenges Baku City Circuit has to offer is its wind and this is something that almost none of the drivers were experiencing before they came to race to Baku. Other than that, it has the longest straight of 2.2 kilometers (1.36 miles) in the whole history of F1 races so far, which allows achieving speeds over 350 km/h with active DRS. But overall, F1 is not only about being fast, it's technology, planning, strategy and precision race and beyond.

So I'd like to develop a system that could identify the best strategy for the race if driver positions and weather conditions are known. And by strategy, the combination of tyre compound choice and pit time is usually considered. Another variation of this problem would be: knowing the strategies, to predict the outcome of the race up to a certain percentage.

It's been 5 years since Baku became host to one of the races of seasons in F1, so there have been only 5 races so far. Therefore, the challenge is that there might not be enough data to work with. But on an extended scale, data can be taken from previous years and applied in the same manner.

Data collection

For data collection, there is a great source called Ergast API. It contains almost all the data about F1 races starting from the 1950s till today. The documentation can be found here. So to check it out I used the postman to send requests to the API and searched for information about the latest race of 2021 that was held in Baku. To do that I have to send get requests to the following path: http://ergast.com/api/f1/2021/6 which gave me this output below

For more detailed information for example lets add extra variable like pitstop time:

In this case it would output the list with all pitstops and necessary information like lap number, time, duration of stop etc.

But another challenge in this case is that since 1950, many regulations in races have changed, and so did the vehicle specs and tyre compounds. I guess therefore they are missing in the data sources, however they play very important role. Starting from 2017, F1 moved to new compounds array which for the sake of simplicity they called hard, medium and soft. I belive for the last several years that information could be obtained from F1 original sources like F1 official webpage by webcraping or from livestreams.

Another remaining challenge is weather which also plays critical role on a way to victory. Unfortunately, the API doesnt provide the weather details of the race but knowing the exact date and location it could be looked up in other services via API or etc.

So here we are. Selecting a topic for our research that we are about to present at the end as the master thesis project. Doing a research seems a difficult and complicated process, at least for now. And one of the hardest parts of it is selecting an area and topic to work on and getting started. There is a huge choise in topics where computer science and AI field can be implemented. The main dilemma here is between our passion, creativity and excitement vs time and resources that we have for it. So the topic must be chellenging and interesting enought to fulfill the expectations but simple enough to be doable.

Professor Kaisler, ex DARPA employee, inroduced George Heilmeier’s Catechism: the way DARPA picked up projects for their research. It consists of 8 questions that the concept of the project should address in order to be approved for development and funding.

•What are you trying to do? • •How is it done today, and what are the limits of current practice? • •What’s new in your approach and why will it be successful? • •Who cares? If you are successful, what difference will it make? • •What are the risks and the payoffs? • •How much will it cost? How long will it take? • •What are the midterm and final exams to check for success? • •Why now?

As they believed in DARPA, if a project, idea, solution or concept can answer these basic questions, then it might have purpose behind it. I'll be trying to run all my ideas through this questions filter in order to have a better understanding of whether I am going in the right direction.

Either way, opportunities are plenty and worth exploring even if they fail.

Greetings! I am Kamran Rzayev - a Computer Science master student in a joint program at GWU and ADA University. This post is to introduce myself.
I was born in Baku, Azerbaijan. I got my bachelor degree in Computer Engineering at the National Aviation Academy of Azerbaijan. During that period of education, I tried to expose myself to a wide variety of computer science and engineering areas, including cybersecurity, IoT, embedded systems, distributed systems and Web development. After graduation, I've been working as a software developer for a company mainly focusing on back-end web development, but my interest is not limited to it.
Problem domains of my interest include natural and environmental sciences. As a kid, I used to be into physics, space and everything that had an engine or could potentially explode 🙂
Another thing of my interest outside of computer science would be video games although it's somehow related to it. I was a console gamer most of the time (Sony platform) playing popular AAA games.

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