Gergely Bári

57203481450

Publications - 5

The Autonomous Software Stack of the FRED-003C: The Development that LED to Full-Scale Autonomous Racing

Gergely Bári Zalán Demeter Ármin Bogár-Németh Levente Puskás Martin Nádas Zsolt Farkas Balázs Tuba Balázs Kovács Ádám Matkovics

Publication Name: IEEE Intelligent Vehicles Symposium Proceedings

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: 1661-1667

Description:

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.

Open Access: Yes

DOI: 10.1109/IV64158.2025.11097721

A LiDAR-based approach to autonomous racing with model-free reinforcement learning

Gergely Bári Gergely Hajgató Máté Hell Ármin Bogár-Németh

Publication Name: IEEE Intelligent Vehicles Symposium Proceedings

Publication Date: 2024-01-01

Volume: Unknown

Issue: Unknown

Page Range: 258-263

Description:

This paper explores the use of reinforcement learning (RL) in the context of autonomous vehicle racing, specifically focusing on the F1TENTH simulation platform. While commercial autonomous driving often employs classic control algorithms, the state-of-the-art solutions, including those in the F1TENTH domain, increasingly rely on RL. Notably, RL-based approaches have shown superhuman performance in simulated environments, as seen in drone racing and the recent achievement by Sony in autonomous racing. In this paper we propose a novel LiDAR-only observation for learning vehicle dynamics, and test it with a widely accessible model-free RL method. The trained agent demonstrates the capability to transfer its driving skills to previously unseen tracks. Additionally, the paper provides recommendations for selecting hyperparameters, contributing valuable insights for newcomers to the field of autonomous racing.

Open Access: Yes

DOI: 10.1109/IV55156.2024.10588613

Scalable Supervisory Architecture for Autonomous Race Cars

Gergely Bári Péter Bogdán Zalán Demeter Ármin Bogár-Németh

Publication Name: IEEE Intelligent Vehicles Symposium Proceedings

Publication Date: 2024-01-01

Volume: Unknown

Issue: Unknown

Page Range: 264-271

Description:

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.

Open Access: Yes

DOI: 10.1109/IV55156.2024.10588615

Evaluation of Local Planner-Based Stanley Control in Autonomous RC Car Racing Series

Gergely Bári Máté Fazekas János Tóth Zalán Demeter Ármin Bogár-Németh

Publication Name: IEEE Intelligent Vehicles Symposium Proceedings

Publication Date: 2024-01-01

Volume: Unknown

Issue: Unknown

Page Range: 252-257

Description:

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.

Open Access: Yes

DOI: 10.1109/IV55156.2024.10588629

Vision Based Driving Agent for Race Car Simulation Environments

Gergely Bári László Palkovics

Publication Name: Lecture Notes in Mechanical Engineering

Publication Date: 2026-01-01

Volume: Unknown

Issue: Unknown

Page Range: 177-188

Description:

In recent years, autonomous driving has become a popular field of study. As control at tire grip limit is essential during emergency situations, algorithms developed for race cars are useful for road cars too. This paper examines the use of Deep Reinforcement Learning (DRL) to solve the problem of “grip limit driving” in a simulated environment. Proximal Policy Optimization (PPO) method is used to train an agent to control the steering wheel and pedals of the vehicle, using only visual inputs to achieve professional human lap times. The paper outlines the formulation of the task of time optimal driving on a race track as a deep reinforcement learning problem, and explains the chosen observations, actions, and reward functions. The results demonstrate human-like learning and driving behavior that utilize maximum tire grip potential.

Open Access: Yes

DOI: 10.1007/978-981-96-6452-8_15