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Laboratory introduction

In short about us

BME Automated Drive Lab is a laboratory at Budapest University of Technology and Economics, in Department of Automotive Technologies. The laboratory aims to do cutting-edge R&D activities in the whole spectrum of highly automated and autonomous road vehicles. As an academic research group, the lab intends to connect the academic and industrial competencies and education and research. The lab would like to catalyse the acceptance and penetration of self-driving vehicles and related technologies. It is emphasised that besides fundamental research in this field, the lab is also doing applied research, i.e. real automated vehicles and functionalities are developed continuously for credible demonstrations.We would like to develop more functionality in the future to create a safe, autonomous vehicle.Our mission is to combine academic and industrial competences to become a globally recognised education and research institution.

What we do?

We have 7 teams with different research fields where we work together with other Universities, Departments, and Industrial partners. The 5 main research layers including several subfields:

Our Team

R&D of BME Automated Drive Lab in general

The lab has 7 main research layers including several subfields:
Safety and Security Team
Our team is the core member of the national Hungarian working group, which is responsible for the establishment of the Automotive Cybersecurity Test and Certification Centre at ZalaZONE. Due to this membership, our Department has direct experiences from the state-of-the-art scientific methods and concepts in the field of Automotive Cybersecurity. We have traditionally strong cooperation with the actors of the Hungarian automotive ecosystem (especially considering the most important TIER1 market actors), which makes it possible to receive information about the most relevant industry-related issues from the automotive cybersecurity sector. The Safety and Security Research Group pays particular attention to the interaction and interrelationship of automotive safety and security co-engineering. Accordingly, we focus our research resources to develop integrated design and analysis methodologies that can consider the interaction of automotive safety and security of road domains together, especially considering the CAV related cybersecurity issues, such as IDS solutions, threat analysis, scenario-based testing, and specific domains of PKI management. Beyond this, our team has outstanding competencies in the field of automotive forensics researches. In this field, our primary research orientation related to CAVs focuses on the evaluation possibilities of accidents related to highly automated vehicles (including accident reconstruction methods, the investigation of the most relevant conflict types, and liability issues).
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Vehicle Dynamics and Control Team
The main activities of the team includes vehicle and vehicle sub-system modeling, parameter identification, validation and test measurements and vehicle control. Modeling is a useful and cost effective tool for analyzing, tune and develop vehicle systems and their interaction with the environment or with other sensing or control systems installed in the car. The definition and correct implementation is insufficient for the realistic behavior of numeric models, hence, a special emphasis should be put on the parameter identification and validation. For this purposes, a wide range of measurement equipment is needed. The teams work is supported by various sensors and measurement systems including but not limited to different kinds of dual antenna GNSS systems with RTK correction, a Correvit 2-axis optical sensor, IMU units, strain-gauge measurement system, etc. A direct access to the test platforms of the ZalaZONE Automotive Proving guarantees that a wide variety of test runs can be carried out such as highly dynamic test on the dynamic platform and the handling course as well as on different surface qualities on the braking platform. The most commonly used platform for custom model implementation is MATLAB/Simulink that ensures quick and flexible model development, evaluation and documentation. Commercial modeling softwares are also available for simulation purposes such as IPG CarMaker, PreScan, VTD Vires. With the coupling between the above simulators and MATLAB/Simulink a suitable customization and improved flexibility can also be achieved. Based on the knowledge regarding vehicle modeling the other main field of the team activity incorporates the function and control software development for automated and regular vehicles. Our activity is focused both on component level (e.g. steering control, brake control, etc.) as well as high level functions (e.g. driver assistance systems and automated driving). Implementation, testing and data acquisition are usually carried out with dSpace AutoBox rapid prototyping units that are available in various types. The teams competency includes the modification of vehicle to enable steer-by-wire which is essential for reproducible testing and the development of self-driving functions. Current the research activities are focused on vehicle motion control at handling limits and vehicle-drone interactions.
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Perception Team
The focus of the team relates first of all to problems related to environment perception and understanding, to the research and development of related methods and algorithms. Let us give a brief overview on the main topics of the laboratory. Development of Perception Algorithms: The main objective is to develop neural network architectures (first of all for 3D object detection and end-to-end models) capable of learning from multiple type of data such as LiDAR, RADAR, cameras. Such networks are able to learn the synergies present between different type of data and thus may provide more reliable results, more robust behavior than networks operating on single type of data only. Besides the developent of algorithms for raw sensor fusion, we are working on the development of a so called central perception system capable of providing object detections by a cluster of infrastructural and vehicular sensors, fusing them together in real time and thus providing a digital twin of the traffic in the area covered by these sensors. Measurement and Testing: Besides testing our algorithms on publicly available datasets we perform also experiments under real circumstances. For this purpose we have built a measurement vehicle equipped with LiDARS, RADAR, Cameras, dGPS and V2X communication unit which allows us to test our algorithms under real circumstances in real-time. Here the calibration of sensors and that of the whole system is of key importance. Verification of Models: The risks we face in many safety-critical applications such as assisted and highly automated driving are rapidly changing because the internal logic of the employed deep learning perception methods is not humanly interpretable. This gives rise to new kinds of mistakes and more worryingly, new vectors of attack exploiting these new vulnerabilities. In our research we explore ways to quantify the robustness and verify guaranteed properties of machine learning systems. The goal is to design robust perception systems that come with mathematically certified properties. As a prerequisite for this, we have to develop more efficient verification methods and also find novel ways of formulating humanly interpretable specifications in ultra-high dimensional input domains (e.g. sensory data).
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The LDDAS (Lane Departure Avoidance Driver Assistance System) was a  project starting in 1997 and funded by Hungary. During the research, BME and  Knorr-Bremse Fékrendszert Kft. Cooperated. The tests took place in Kiskunlacháza. The essence and result of which  is that a truck wants to deviate from the runway "intentionally",  however, the video-based system in the vehicle detects and prevents this with  longitudinal intrusion and then steers the vehicle back to the correct  trajectory.

Chauffeur II

As a continuation of the CHAUFFEUR I project of the Telematics  Application Programme, CHAUFFEUR II has two general aims. Firstly, the  tow-bar technology demonstrated in CHAUFFEUR I will be further developed into  a system that can be transformed into a saleable product quite soon. At the  end of CHAUFFEUR II, there will be a "CHAUFFEUR Assistant", that  supports the driver and allows him/her to follow another vehicle, not only CHAUFFEUR  equipped trucks, at a safe distance. Secondly, CHAUFFEUR II looks into the  future. A fully operable truck platoon will be realised. Typical Platoon  manoeuvres will be presented in a test track environment. CHAUFFEUR II does  not only have technical goals. An important part of the project will be  system evaluation on a theoretical and a practical level. Especially  cost/benefit analysis and user trials and workshops need to be mentioned.



The proposal is to set up intelligent technologies for power trains to  generate nearly collision free vehicle. Such a vehicle will not only  reactively cope with dangerous situations, it will also be in the position to  predict such situation and thus prevent accidents. These possibilities are  limited in today's power trains, e.g. because of lack of a x-by-wire steering  and lack of failure tolerant system architecture. A secure failure tolerant  power train system has not been considered yet in any automobile accident  prevention system. To achieve the necessary requirement, an intelligent  automatic control of the vehicle kinematics and safety features is needed. A  fully electronically power train provides the technological basis.


eSafety of road and air transports Virtual co-pilot to reduce road deaths Vehicles that can foresee dangers and respond automatically to changing road conditions, traffic and driver mistakes could start rolling along Europe’s roads in the near future.
Though still prototypes, the vehicles developed by researchers working in the SPARC project sit on the cusp of a new generation of cars and trucks that promise to improve road safety dramatically.
Using a combination of sensors, automated decision-support systems and innovative control mechanisms, the smart vehicles help counteract the single biggest cause of traffic accidents: driver error.



The Electronic Vehicle and Vehicle Control Knowledge Center (EJJT) was  established in 2005 at the Budapest University of Technology and Economics on  the basis of a tender from the Regional University Knowledge Center of the  National Office for Research and Technology. The state-defined goal of  establishing knowledge centers is to concentrate, consolidate, further  develop and make previously available university, research institute and  corporate knowledge in each selected field into a salable product. The efficient  operation of knowledge centers presupposes a well-defined cooperation between these three spheres.


ICT for Intelligent Vehicles and Mobility Services The groundbreaking  HAVEit proposal aims at the long-term vision of highly automated driving.  Within this proposal important intermediate steps will be developed,  validated and demonstrated. These intermediate results on the one hand offer  high potential for exploitation within 3-5 years after HAVEit and on the  other hand form the ideal basis to integrate further next generation ADAS  (highly automated functionalities) by adding software modules. HAVEit will  significantly contribute to higher traffic safety and efficiency for both passenger cars and trucks, thereby strongly promoting safe and intelligent  mobility of both people and goods.



The main objective of the TRUCKDAS project is to support R&D  programs which can provide innovative technological solutions for the wide  range of users while taking into consideration economicalness as well. Avoiding incidents  resulting from loss of stability and lane departure can supported by such  technological solutions which inform the driver about the danger and if  needed, automatically take control over the vehicle. These solutions are  drive-assist systems and are referred to as DAS in the literature in the  field.


Development of technologies for passenger safety, driving assistance,  reliability, energy efficiency and environmental awareness.



The past decade has seen significant progress on active pedestrian  safety, as a result of advances in video and radar technology. In the  intelligent vehicle domain, this has recently culminated in the market  introduction of first-generation active pedestrian safety systems, which can  perform autonomous emergency braking (AEB-PED) in case of critical traffic  situations. PROSPECT will significantly improve the effectiveness of active  VRU safety systems compared to those currently on the market.

BME FIKP-MI/FM Higher Education Excellence Program of the Ministry of Human Capacities

The aim of the FIKP is to improve the research conditions of higher  education institutions in order to increase Hungary's research capacities,  strengthen the focus of research development and innovation in the operation  of higher education institutions, improve the conditions of scientific and  researcher supply, increase scientific productivity and strengthen research,  technological development and the role and importance of innovation among  higher education institutions.


EFOP-3.6.2-16-2017-00002: Research of autonomous vehicle systems  related to the ZalaZONE autonomous test track

The research professional goal of the project is to research the  testing processes of future key vehicle technologies (autonomous vehicles,  e-mobility) and the technical support of the test track based in  Zalaegerszeg, the direct goal is to research the testing procedures of  autonomous vehicles and electric vehicles. (simulation technologies,  laboratory tests, testing of limited road and road tests that can be  performed on a test track), both at the level of components and systems,  providing an appropriate basis for the future functionality of the test track  currently under construction in Zalaegerszeg.

EFOP-3.6.3-VEKOP-16-2017-00001: Talent management in autonomous  vehicle control technologies

Nowadays, transport is undergoing a significant change, the main  reason for which is that the most modern solutions of informatics and infocommunication  technology are gaining momentum in this field as well. An examination of  international developments shows that in the future, autonomous vehicle  functions will continuously take over the leading role, as a result of which  extensive research activity can be observed in autonomous vehicle  developments. The aim of the competition is to support researchers in their  developments.

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Top publications

Critical Scenario Identification Concept: The Role of the Scenario-in-the-Loop Approach in Future Automotive Testing

Zsolt Szalay
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Do Automated Vehicles Reduce the Risk of Crashes–Dream or Reality?

Árpád Török
IEEE Transactions on Intelligent Transportation Systems
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LTV-MPC Approach for Automated Vehicle Path Following at the Limit of Handling

Ádám Domina; Viktor Tihanyi
Sensors 22
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