Scientific development often takes place in the context of research projects carried out by dedicated students during their time at university. In the field of self-driving software research, the Formula Student Driverless competitions are an excellent platform to promote research and attract young engineers. This article presents the software stack developed by BME Formula Racing Team, that formed the foundation of the development that ultimately led us to full-scale autonomous racing. The experience we gained here contributes greatly to our successful participation in the Abu Dhabi Autonomous Racing League. We therefore think it is important to share the system we used, providing a valuable starting point for other ambitious students. We provide a detailed description of the software pipeline we used, including a brief description of the hardware-software architecture. Furthermore, we introduce the methods that we developed for the modules that implement perception; localisation and mapping, planning, and control tasks.

The advancement of AI technologies and the increasing processing power of computers have made high-speed autonomous racing possible. Different leagues, such as the Abu Dhabi Autonomous Racing League (A2RL) and the Indy Autonomous Challenge (IAC), are organizing races in simulation and with real race cars. In this paper we will describe our experience with the inaugural A2RL event and a SIM race organized by IAC. With respect to A2RL, we will give an overview of the physical parameters of the race car, the sensors we worked with, and our software solution including how we created trajectories for different test scenarios.

In recent years, the number and importance of autonomous racing leagues, and consequently the number of studies on them, has been growing. The seamless integration between different series has gained attention due to the scene's diversity. However, the high cost of full scale racing makes it a more accessible development model, to research at smaller form factors and scale up the achieved results. This paper presents a scalable architecture designed for autonomous racing that emphasizes modularity, adaptability to diverse configurations, and the ability to supervise parallel execution of pipelines that allows the use of different dynamic strategies. The system showcased consistent racing performance across different environments, demonstrated through successful participation in two relevant competitions. The results confirm the architecture's scalability and versatility, providing a robust foundation for the development of competitive autonomous racing systems. The successful application in real-world scenarios validates its practical effectiveness and highlights its potential for future advancements in autonomous racing technology.

This paper proposes a control technique for autonomous RC car racing. The presented method does not require any map-building phase beforehand since it operates only local path planning on the actual LiDAR point cloud. Racing control algorithms must have the capability to be optimized to the actual track layout for minimization of lap time. In the examined one, it is guaranteed with the improvement of the Stanley controller with additive control components to stabilize the movement in both low and high-speed ranges, and with the integration of an adaptive lookahead point to induce sharp and dynamic cornering for traveled distance reduction. The developed method is tested on a 1/10-sized RC car, and the tuning procedure from a base solution to the optimal setting in a real F1Tenth race is presented. Furthermore, the proposed method is evaluated with a comparison to a more simple reactive method, and in parallel to a more complex optimization-based technique that involves offline map building the global optimal trajectory calculation. The performance of the proposed method compared to the latter, referring to the lap time, is that the proposed one has only 8% lower average speed. This demonstrates that with appropriate tuning, a local planning-based method can be comparable with a more complex optimization-based one. Thus, the performance gap is lower than 10% from the state-of-the-art method. Moreover, the proposed technique has significantly higher similarity to real scenarios, therefore the results can be interesting in the context of automotive industry.