Decision making at unsignalized inner city intersections using discrete events systems

2022 ◽  
Vol 0 (0) ◽  
Author(s):  
Hannes Weinreuter ◽  
Balázs Szigeti ◽  
Nadine-Rebecca Strelau ◽  
Barbara Deml ◽  
Michael Heizmann
Keyword(s):  
Inner City ◽  
Road Safety ◽  
Autonomous Vehicles ◽  
Discrete Event ◽  
Autonomous Driving ◽  
Simulation Environment ◽  
Discrete Events ◽  
Real World Data ◽  
Event System ◽  
Discrete Events Systems

Abstract Autonomous driving is a promising technology to, among many aspects, improve road safety. There are however several scenarios that are challenging for autonomous vehicles. One of these are unsignalized junctions. There exist scenarios in which there is no clear regulation as to is allowed to drive first. Instead, communication and cooperation are necessary to solve such scenarios. This is especially challenging when interacting with human drivers. In this work we focus on unsignalized T-intersections. For that scenario we propose a discrete event system (DES) that is able to solve the cooperation with human drivers at a T-intersection with limited visibility and no direct communication. The algorithm is validated in a simulation environment, and the parameters for the algorithm are based on an analysis of typical human behavior at intersections using real-world data.

Petri Nets and Discrete Events Systems

2013 ◽  
pp. 231-240 ◽  
Author(s):  
Juan L. G. Guirao ◽  
Fernando L. Pelayo
Keyword(s):  
Dynamical Systems ◽  
Petri Nets ◽  
Discrete Event Systems ◽  
Discrete Event ◽  
Discrete Dynamical Systems ◽  
Discrete Events ◽  
Key Factors ◽  
Discrete Events Systems ◽  
The One ◽  
The Relationship

This paper provides an overview over the relationship between Petri Nets and Discrete Event Systems as they have been proved as key factors in the cognitive processes of perception and memorization. In this sense, different aspects of encoding Petri Nets as Discrete Dynamical Systems that try to advance not only in the problem of reachability but also in the one of describing the periodicity of markings and their similarity, are revised. It is also provided a metric for the case of Non-bounded Petri Nets.

Petri Nets and Discrete Events Systems

2011 ◽  
Vol 3 (3) ◽  
pp. 13-22
Author(s):  
Juan L. G. Guirao ◽  
Fernando L. Pelayo
Keyword(s):  
Petri Nets ◽  
Cognitive Processes ◽  
Discrete Event Systems ◽  
Discrete Event ◽  
Discrete Dynamical Systems ◽  
Discrete Events ◽  
Key Factors ◽  
Discrete Events Systems ◽  
The One ◽  
The Relationship

This paper provides an overview over the relationship between Petri Nets and Discrete Event Systems as they have been proved as key factors in the cognitive processes of perception and memorization. In this sense, different aspects of encoding Petri Nets as Discrete Dynamical Systems that try to advance not only in the problem of reachability but also in the one of describing the periodicity of markings and their similarity, are revised. It is also provided a metric for the case of Non-bounded Petri Nets.

scholarly journals Supervision and control of heterarchical discrete event systems

2006 ◽  
Vol 17 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Marcos R. Da Silveira ◽  
Michel Combacau
Keyword(s):  
Discrete Event ◽  
Centralized Control ◽  
Discrete Events ◽  
Distributed Models ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Discrete Events Systems ◽  
And Control ◽  
Main Model ◽  
Modelling Process

This paper presents a method for distributing centralized control models of discrete events systems (DES). The contribution of our approach is to offer a systematic way to decompose centralised models in order to obtain a modular representation of the production process. We observe as an advantage of this method that the main model properties are preserved and that the knowledge acquired during the modelling process is still valuable for the new structure. We emphasise that redundant information can be introduced to the system to increase local autonomy and to help the local fault detection and identification functions. Another important aspect is that the distributed models (sub-models) can be modified in order to increase their flexibility and to reach a more realistic behaviour representation. A set of terminologies and definitions related to the Control and Supervision domain are introduced in this paper to assist the understanding of our work.

scholarly journals From Traditional to Autonomous Vehicles: A Systematic Review of Data Availability

2021 ◽  
pp. 036119812110575
Author(s):  
Leandro Masello ◽  
Barry Sheehan ◽  
Finbarr Murphy ◽  
German Castignani ◽  
Kevin McDonnell ◽  
...  
Keyword(s):  
Systematic Review ◽  
Risk Assessment ◽  
Transportation Planning ◽  
Road Safety ◽  
Autonomous Vehicles ◽  
Gap Analysis ◽  
Autonomous Driving ◽  
Data Availability ◽  
Principal Characteristics ◽  
Risk Assessment Models

The increasing accessibility of mobility datasets has enabled research in green mobility, road safety, vehicular automation, and transportation planning and optimization. Many stakeholders have leveraged vehicular datasets to study conventional driving characteristics and self-driving tasks. Notably, many of these datasets have been made publicly available, fostering collaboration, scientific comparability, and replication. As these datasets encompass several study domains and contain distinctive characteristics, selecting the appropriate dataset to investigate driving aspects might be challenging. To the best of the authors’ knowledge, this is the first paper that performs a systematic review of a substantial number of vehicular datasets covering various automation levels. In total, 103 datasets have been reviewed, 35 of which focused on naturalistic driving, and 68 on self-driving tasks. The paper gives researchers the possibility of analyzing the datasets’ principal characteristics and their study domains. Most naturalistic datasets have been centered on road safety and driver behavior, although transportation planning and eco-driving have also been studied. Furthermore, datasets for autonomous driving have been analyzed according to their target self-driving tasks. A particular focus has been placed on data-driven risk assessment for the vehicular ecosystem. It is observed that there exists a lack of relevant publicly available datasets that challenge the creation of new risk assessment models for semi- and fully automated vehicles. Therefore, this paper conducts a gap analysis to identify possible approaches using existing datasets and, additionally, a set of relevant vehicular data fields that could be incorporated in future data collection campaigns to address the challenge.

scholarly journals Autonomous Driving in Roundabout Maneuvers Using Reinforcement Learning with Q-Learning

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1536 ◽  
Author(s):  
Laura García Cuenca ◽  
Enrique Puertas ◽  
Javier Fernandez Andrés ◽  
Nourdine Aliane
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Learning Algorithm ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Simulation Environment ◽  
Q Learning ◽  
Driving Scenario

Navigating roundabouts is a complex driving scenario for both manual and autonomous vehicles. This paper proposes an approach based on the use of the Q-learning algorithm to train an autonomous vehicle agent to learn how to appropriately navigate roundabouts. The proposed learning algorithm is implemented using the CARLA simulation environment. Several simulations are performed to train the algorithm in two scenarios: navigating a roundabout with and without surrounding traffic. The results illustrate that the Q-learning-algorithm-based vehicle agent is able to learn smooth and efficient driving to perform maneuvers within roundabouts.

scholarly journals Simulation of Electrical Grid with Omnet++ Open Source Discrete Event System Simulator

2016 ◽  
Vol 44 (2) ◽  
pp. 85-91
Author(s):  
Milán Sőrés ◽  
Attila Fodor
Keyword(s):  
Communication Networks ◽  
Discrete Event ◽  
Operating Conditions ◽  
Simulation Environment ◽  
Electrical Networks ◽  
Electrical Grid ◽  
Software Products ◽  
Event System ◽  
Electrical Grids ◽  
Electrical Components

Abstract The simulation of electrical networks is very important before development and servicing of electrical networks and grids can occur. There are software that can simulate the behaviour of electrical grids under different operating conditions, but these simulation environments cannot be used in a single cloud-based project, because they are not GNU-licensed software products. In this paper, an integrated framework was proposed that models and simulates communication networks. The design and operation of the simulation environment are investigated and a model of electrical components is proposed. After simulation, the simulation results were compared to manual computed results.

scholarly journals Multi-Sensor Fault Detection, Identification, Isolation and Health Forecasting for Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2547
Author(s):  
Saeid Safavi ◽  
Mohammad Amin Safavi ◽  
Hossein Hamid ◽  
Saber Fallah
Keyword(s):  
Fault Detection ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Machine Learning Techniques ◽  
Health Index ◽  
Sensor Fault ◽  
Real World Data ◽  
Sensor Fault Detection ◽  
Primary Focus ◽  
Index Measure

The primary focus of autonomous driving research is to improve driving accuracy and reliability. While great progress has been made, state-of-the-art algorithms still fail at times and some of these failures are due to the faults in sensors. Such failures may have fatal consequences. It therefore is important that automated cars foresee problems ahead as early as possible. By using real-world data and artificial injection of different types of sensor faults to the healthy signals, data models can be trained using machine learning techniques. This paper proposes a novel fault detection, isolation, identification and prediction (based on detection) architecture for multi-fault in multi-sensor systems, such as autonomous vehicles.Our detection, identification and isolation platform uses two distinct and efficient deep neural network architectures and obtained very impressive performance. Utilizing the sensor fault detection system’s output, we then introduce our health index measure and use it to train the health index forecasting network.

F1_Tenth_Course_Report_Group_C

2018 ◽  
Author(s):  
Yi Chen ◽  
Sagar Manglani ◽  
Roberto Merco ◽  
Drew Bolduc
Keyword(s):  
Operating System ◽  
Autonomous Driving ◽  
Software Tools ◽  
Simulation Environment ◽  
Robot Operating System ◽  
Available Information

In this paper, we discuss several of major robot/vehicle platforms available and demonstrate the implementation of autonomous techniques on one such platform, the F1/10. Robot Operating System was chosen for its existing collection of software tools, libraries, and simulation environment. We build on the available information for the F1/10 vehicle and illustrate key tools that will help achieve properly functioning hardware. We provide methods to build algorithms and give examples of deploying these algorithms to complete autonomous driving tasks and build 2D maps using SLAM. Finally, we discuss the results of our findings and how they can be improved.

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