Szilárd Aradi

• PhD
• Professor (Associate) at Budapest University of Technology and Economics

Advanced driver assistance and autonomous systems require an enhanced perception system, fusing the data of multiple sensors. Many automotive sensors provide high-level data, such as tracked objects, i.e., tracks, usually fused in a track-to-track manner. The core of this fusion is the track-to-track association, intending to create assignments bet...

Although today's reinforcement learning-based control agents operate effectively in ideal situations, their performance can significantly decrease under changing conditions. This paper addresses this limitation by proposing an experience-based online adaptation framework. It is designed to enable agents to adjust their policies to differing domains...

The number of highly automated machines in the agricultural sector has increased rapidly in recent years. To reduce their fuel consumption, and thus their emission and operational cost, the performance of such machines must be optimized. The running gear–terrain interaction heavily affects the behavior of the vehicle; therefore, off-road traction c...

The development and testing of automotive perception systems require a large amount of labeled data. The ground truth of the measurement scene is usually generated by manual video annotation. However, besides requiring a lot of manual effort, it can be time-consuming. Moreover, since the annotation boxes must be transformed into the vehicle's BEV s...

With increasing realised traffic on transport networks, greenhouse gas emissions show a similar trend. Reducing them is a modern aspiration, creating a better place to live and moving towards sustainability. Expanding the infrastructure is often not an appropriate solution, as the system would only be fully utilised at peak times, while at less fre...

This paper focuses on the design of lateral controllers for autonomous vehicles. To enhance passenger comfort while concurrently maintaining minimal deviation from the desired trajectory, the developed controllers are tuned by a Genetic Algorithm, whose cost function is following the ISO 2631 Standard. Three model-based controllers, a Linear Quadra...

The gradually evolving automated driving and ADAS functions require more enhanced environment perception. The key to reliable environmental perception is large amounts of data that are hard to collect. Several simulators provide realistic, raw sensor data based on physical sensor models. However, besides their high price, they also require very hig...

In this paper, we study the problem of traffic signal control in general intersections by applying a recent reinforcement learning technique. Nowadays, traffic congestion and road usage are increasing significantly as more and more vehicles enter the same infrastructures. New solutions are needed to minimize travel times or maximize the network cap...

The rapid growth of urbanization and the constant demand for mobility have put a great strain on transportation systems in cities. One of the major challenges in these areas is traffic congestion, particularly at signalized intersections. This problem not only leads to longer travel times for commuters, but also results in a significant increase in...

Reinforcement learning applications are spreading among different domains, including autonomous vehicle control. The diverse situations that can happen during, for instance, at a highway commute are infinite, and with labeled data, the perfect coverage of all use-cases sounds ambitious. However, with the complex tasks and complicated scenarios face...

The railway timetables are designed in an optimal manner to maximize the capacity usage of the infrastructure concerning different objectives besides avoiding conflicts. The real-time railway traffic management problem occurs when the pre-planned timetable cannot be fulfilled due to various disturbances; therefore, the trains must be rerouted, reor...

Planning the optimal trajectory of emergency avoidance maneuvers for highly automated vehicles is a complex task with many challenges. The algorithm needs to decrease accident risk by reducing the severity and keeping the car in a controllable state. Optimal trajectory generation considering all aspects of vehicle and environment dynamics is numeri...

Advanced driver assistance systems and highly automated driving functions require an enhanced frontal perception system. The requirements of a frontal environment perception system cannot be satisfied by either of the existing automotive sensors. A commonly used sensor cluster for these functions consists of a mono-vision smart camera and automotiv...

The real-time railway rescheduling problem is a crucial challenge for human operators since many factors have to be considered during decision making, from the positions and velocities of the vehicles to the different regulations of the individual railway companies. Thanks to that, human operators cannot be expected to provide optimal decisions in...

Academic research in the field of autonomous vehicles has reached high popularity in recent years related to several topics as sensor technologies, V2X communications, safety, security, decision making, control, and even legal and standardization rules. Besides classic control design approaches, Artificial Intelligence and Machine Learning methods...

In this article, the basic Reinforcement Learning (RL) concepts are discussed, continued with a brief explanation of Markov Decision Processes (MDPs). Reasoning for the application of RL in the autonomous vehicle control domain is accompanied with a developed basic environment for simulation-based training of agents. Furthermore, we look at the ava...

The transportation industry is one of the main contributors to global warming since it is responsible for a quarter of greenhouse gas emissions. Due to society’s crucial dependence on fossil fuels and the rapid increase in mobility demands, the reduction of global vehicle emissions evolved into a significant challenge. In the urban transportation a...

The traffic signal control problem is an extensively researched area providing different approaches, from classic methods to machine learning based ones. Different aspects can be considered to find an optima, from which this paper emphasises emission reduction. The core of our solution is a novel rewarding concept for deep reinforcement learning (D...

Several problems can be encountered in the design of autonomous vehicles. Their software is organized into three main layers: perception, planning, and actuation. The planning layer deals with the sort and long-term situation prediction, which are crucial for intelligent vehicles. Whatever method is used to make forecasts, vehicles’ dynamic environ...

Rail transportation helps to reach the global climate targets because it is characterized by low emission. The passenger and freight volumes on the railway increase yearly in line with EU targets. However, delays of passenger and freight trains decrease the punctuality and the reliability of the railway sector and the development of the infrastruct...

Autonomous vehicles or self-driving cars are prevalent nowadays, many vehicle manufacturers, and other tech companies are trying to develop autonomous vehicles. One major goal of the self-driving algorithms is to perform manoeuvres safely, even when some anomaly arises. To solve these kinds of complex issues, Artificial Intelligence and Machine Lea...

The paper presents the modeling and control design of a floating piston pneumatic gearbox actuator using a grid-based Linear Parameter Varying approach. First, the nonlinear model of the pneumatic actuator is presented, then it is transformed into a 6th order Linear Parameter Varying representation with endogenous scheduling parameters. The model i...

The perception and prediction of the surrounding vehicles’ trajectories play a significant role in designing safe and optimal control strategies for connected and automated vehicles. The compression of trajectory data and the drivers’ strategic behavior’s classification is essential to communicate in vehicular ad-hoc networks (VANETs). This paper p...

The paper presents a simple yet powerful and intelligent driver agent, designed to operate in a preset highway situation using Policy Gradient Reinforcement Learning (RL) agent. The goal is to navigate safely in dense highway traffic and proceed through the defined length with the shortest time possible. The algorithm uses a dense neural network as...

Electro-pneumatic actuators play an essential role in various areas of the industry, including heavy-duty vehicles. This article deals with the control problem of an Automatic Manual Transmission, where the actuator of the system is a double-acting floating-piston cylinder, with dedicated inner-position. During the control design of electro-pneumat...

Highly automated and autonomous vehicles become more and more widespread changing the classical way of testing and validation. Traditionally, the automotive industry has pursued testing rather in real-world or in pure virtual simulation environments. As a new possibility, mixed-reality testing has also appeared enabling an efficient combination of...

This work presents a powerful and intelligent driver agent, designed to operate in a preset highway situation using Policy Gradient Reinforcement Learning (RL) agent. Our goal is to create an agent that is capable of navigating safely in changing highway traffic and successfully accomplish to get through the defined section keeping the reference sp...

It is essential for a driver assistant system’s motion planning to take the vehicles moving in the surroundings into account. One of the most crucial driver intentions which should be predicted is lane changing. It has been investigated whether it is possible to reliably classify lane-changing maneuvers in a highway situation using learning algorit...

Reinforcement Learning, as one of the main approaches of machine learning, has been gaining high popularity in recent years, which also affects the vehicle industry and research focusing on automated driving. However, these techniques, due to their self-training approach, have high computational resource requirements. Their development can be separ...

Academic research in the field of autonomous vehicles has reached high popularity in recent years related to several topics as sensor technologies, V2X communications, safety, security, decision making, control, and even legal and standardization rules. Besides classic control design approaches, Artificial Intelligence and Machine Learning methods...

Motion planning plays an essential role in designing self-driving functions for connected and autonomous vehicles. The methods need to provide a feasible trajectory for the vehicle to follow, fulfilling different requirements, such as safety, efficiency, and passenger comfort. In this area, algorithms must also meet strict real-time expectations, s...

This paper presents a real-time optimal motion planner algorithm for road vehicles. The method is based on a cubic spline trajectory planner which is able to plan a set of vehicle motions driving from a given initial state to a required final state. Maximal dynamical feasibility and passenger comfort are ensured by minimizing the lateral accelerati...

Environment perception and situation awareness are keystones for autonomous road vehicles. The problem of maneuver classification for road vehicles in the context of multi-model state estimation under model uncertainty is addressed in this paper. The conventional approach is to define different motion models that match the desired type of movements...

The paper presents a small-scale electric vehicle framework for vehicle control education and research. The main goal of the project is to serve as a good experimental platform for the students on any level of vehicle mechatronics education. It offers wide range of possibilities for embedded system, control design and machine learning applications....

The objective of the research is to analyze the behavior of the developed electro-pneumatic actuator model and compare it to the behavior of the real system. The actuator achieves the requested gear changes by moving the two pistons inside the cylinder and it is operated by three-way two-position solenoid valves. Since not all model parameters are...

Robust object tracking and maneuver estimation methods play significant role in the design of advanced driver assistant systems and self-driving cars. As an input to situation understanding and awareness, the performance of such algorithms influences the overall effectiveness of motion planning and plays high role in safety. The paper examines the...

Over the last decade, many different algorithms were developed for the motion planning of road vehicles due to the increasing interest in the automation of road transportation. To be able to ensure dynamical feasibility of the planned trajectories, nonholonomic dynamics of wheeled vehicles must be considered. Nonlinear optimization based trajectory...

This paper presents two state estimator algorithms, a Kalman and an Extended Kalman filter in order to determine the chamber pressures of a pneumatic actuator without the application of pressure sensors. The presented state estimators were validated against laboratory measurements, then it was shown, that in applications with high computational res...

Cooperative multi-model state estimation is considered in road traffic scenario. Our goal is to detect and track a maneuvering object based on vehicular radar measurements. To handle the motion model switching of the navigating object we used the interacting Multiple Model (IMM) estimator with the Bernoulli filter (BF). We modified a recently deriv...

The paper presents a microscopic highway simulation model, built as an environment for the development of different machine learning based autonomous vehicle controllers. The environment is based on the popular OpenAI Gym framework, hence it can be easily integrated into multiple projects. The traffic flow is operated by classic microscopic models,...

The paper presents a reinforcement learning based solution for the control design problem of a gearbox actuator. The system is operated by an electro-pneumatic, three-state, floating piston cylinder. Besides the primary goals of positioning the piston, the nonlinear system's quality objectives are to minimize switching time and overshoot. The contr...

The Bernoulli filter (BF) in the interacting multiple model (IMM) framework is proposed for detecting and tracking a maneuvering target. The BF is implemented as a particle filter and embedded in the IMM structure. The communication between the IMM and the BF is achieved through a Gaussian layer. Particles are drawn from the mixture densities at ea...

The aim of this paper is to present a local trajectory planning method based on nonlinear optimization that is able to generate a dynamically feasible, comfortable and customizable trajectory for highly automated road vehicles. The presented algorithm is able to consider the nonholonomic dynamics of wheeled vehicles and ensures the dynamical feasib...

Multi-sensor object detection and tracking on a highway scene with radar measurements is presented. The estimation algorithm is the random finite set based Bernoulli filter, working in the Bayesian framework. The recursion for calculating the Bayes estimation is implemented as a particle filter. A method is presented for calculating the likelihoods...

The paper presents a vehicle framework for the development, implementation and testing of autonomous vehicle functions. The base vehicle is a Smart Fortwo which was strongly modified in order to be suitable for autonomous driving demonstration. The Sensor framework was expanded with environment sensors, such as lidar and radar, while full vehicle c...

The paper presents the performance evaluation of a multi-sensor object detection algorithm applied in traffic situation. The chosen data fusion and estimation procedure is the Bernoulli particle filter, which is ideal for cooperative object detection, as it can handle the varying number of sensor measurement and object appearance-disappearance too....

For future’s highly automated road vehicles, dynamically feasible, comfortable, and customizable trajectories must be planned in order to ensure the maximum level of road safety and passenger satisfaction. To fulfil these requirements, a constrained nonlinear optimization based trajectory planning method was developed, which is generating the traje...

The reduction of energy consumption has gained significance in the past years as energy prices have been continuously increasing. Advances in railway telematics and the large amount of data obtained from train services enable the development of methods that are capable of further improving energy efficiency through the evaluation, control or predic...

The paper presents the development of an experimental vehicle framework for testing autonomous vehicle functions. The development of system serves primarily the education and the scientific work of the students. It is a good experience for the students on any level of mechatronic education because of its diverse possibilities for mechanical enginee...

The paper presents the design and realization of lane keeping function of an autonomous electric go-cart. The requirement towards the system concerning this paper is navigating the vehicle on a closed track with road markings, based on information from an optical camera with lane detection capabilities. To achieve this task, two solutions were used...

One of the basis of the development in future intelligent transportation systems is the vehicle itself, whose provided features and communication possibilities are continuously expanding. One of the main areas in development of the vehicle of the future is mechatronics. Teaching this topic is a complex challenge because of the multidisciplinary nat...

This paper presents the possibilities for using currently installed interconnection cables for extended intra-train communication purposes and proposes a CAN-based communication solution for the problem. The implementation of such a communication technique raises feasibility issues due to the non-standard physical media and the non-fixed network to...

The Connected Revolution has reached the automotive industry and the Internet penetrates into the modern vehicles. Formerly acquiring data from a vehicle was the tool of Fleet Management Systems handling commercial vehicles. In the recent years connectivity began to appear in the passenger vehicles also. The first features were infotainment and nav...

Advances in railway telematics and the large amount of data obtained from train services enable the development of methods that are capable of further improving energy efficiency through the evaluation, control or prediction of energy utilisation of the railways. The paper proposes a method for determining the longitudinal running resistance of ele...

The paper proposes a novel system architecture with a communication mechanism to provide the driver with information about the vehicle conditions on a mobile device such as a mobile phone. The system uses a cost efficient and reliable connection between LIN/CAN networks and the mobile phone. A prototype of the system has been developed and tested w...

In the following pages the authors would like to provide an introduction into the design, the architecture and functionality of today’s highly automated vehicle control systems. Thanks to the developments in electronics technology integrated into the automotive industry there has been a revolution in road vehicle technology in the past 20 years. In...

The paper presents the development of an experimental vehicle framework for testing autonomous vehicle functions. The development of such systems serves two purposes: research and education. On the one hand, such system can be used to aid the development of many driver assistance functionalities. On the other hand it is a good experience for the st...

Teaching mechatronics is a great challenge since the subject is a multidisciplinary area of electrical, mechanical and IT knowledge. Students must deepen their knowledge in these slightly related fields of engineering. Tasks and solving problems in mechatronics require cognitive and operational knowledge and practical experience about systems desig...

The paper proposes a new approach to the design of speed profile for trains concerning time table and energy efficient operation. The method takes into consideration the inclinations of the railway tracks, the speed limits, the nonlinear traction curve and resistances to achieve the energy-efficient speed. The paper demonstrates that by using a pre...

Rising energy prices motivate all participants in transportation to pay attention to the possibilities of reducing the amount of energy used. The paper deals with the reduction of energy consumption of trains. The main goal is to generate a speed profile that is more energy efficient than a given reference journey taking the slopes of the track int...

Organizing a group of vehicles into a vehicle platoon in a way that, except for the leading vehicle, each platoon member can be autonomously driven has been a research goal for decades. Among other benefits this results in a decrease of fuel consumption and also in the driver's workload and an increase in a better use of road capacity. The recent d...