Server Architecture Development for On-line Tracking of Large-sized Vehicle Fleet

This article describes the structure of a fleet management system and its system elements. First, the schematic structure (central server and on-board computers) are outlined. Therefore the details of the communication, operation and the emerged problems are given. Later the development and testing of the central server, its software, and the database server are described. Finally the advantage of the system and the development possibilities are summarized

The control of fleet management systems´ server model

Our article deals with the load controlling of server systems which can be represented as M/M/1 queuing models. It introduces the results of a service structure´s state space based control, which has also been realized in practice, the system model, and the control which guarantees the availability of the system

Measurement Based Validation of an Electro-Pneumatic Gearbox Actuator

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 exactly known, such as contraction coefficients and friction parameters, they can be estimated based on literature then they can be further tuned to minimize the error of the simulation. The developed nonlinear model is capable of describing the dynamic behavior of the gearbox actuator, thus it can be used to analyze the effects of constructional modifications and it can serve as Model in the Loop (MIL) environment for controller testing

Fast Prototype Framework for Deep Reinforcement Learning-based Trajectory Planner

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 separated into training with simulation, validation through vehicle dynamics software, and real-world tests. However, ensuring portability of the designed algorithms between these levels is difficult. A case study is also given to provide better insight into the development process, in which an online trajectory planner is trained and evaluated in both vehicle simulation and real-world environments

Lane Change Prediction Using Gaussian Classification, Support Vector Classification and Neural Network Classifiers

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 algorithms such as Gaussian-classifier, SVM, and LSTM neural networks. Real vehicle trajectories are extracted from the NGSIM US-101 and I-80 datasets. The input for the classifiers is derived from the trajectory by selecting a subset of the features: lateral and longitudinal position coordinates, longitudinal acceleration, and velocity. In such an environment, the vehicle movement is limited, so it has been tested that how sufficient if only the mean and the variance of the derivative of lateral coordinate was taken as input for the classification had been tested. Different strategies for labeling the input sequences were tested

Autonomous Drifting Using Reinforcement Learning

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 Learning methods are used. One of these motion planning problems is when the tires lose their grip on the road, an autonomous vehicle should handle this situation. Thus the paper provides an Autonomous Drifting algorithm using Reinforcement Learning. The algorithm is based on a model-free learning algorithm, Twin Delayed Deep Deterministic Policy Gradients (TD3). The model is trained on six different tracks in a simulator, which is developed specifically for autonomous driving systems; namely CARLA

Design of Lane Keeping Algorithm of Autonomous Vehicle

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, a double-loop control with feedforward load disturbance compensation and a nonlinear method. The control algorithms were designed and tuned in a Hardware-In-The-Loop framework. The nonlinear algorithm was implemented on two different hardware devices and validated in CarSim–Matlab software environment