scholarly journals Comparative Analysis of V2V and A2A Technologies

2018 ◽  
Vol 45 (1) ◽  
pp. 345-364 ◽  
Author(s):  
Agata Kołodziejska ◽  
Karolina Krzykowska ◽  
Mirosław Siergiejczyk
Keyword(s):  
Autonomous Vehicles ◽  
Road Traffic ◽  
Autonomous Vehicle ◽  
Legal Aspects ◽  
Transport Systems ◽  
Human Beings ◽  
Driver Assistance Systems ◽  
The Road ◽  
Safety And Reliability ◽  
On The Road

Abstract In recent years, around the world, there has been work underway on systems, which will increase not only the comfort of traveling but, above all, the safety and reliability of the road traffic. The systems in this field, designed to replace human beings in the future, thus eliminating their mistakes on the road, already have their prototypes. However, these prototypes are still being improved and require a lot of work so they could operate fully and reliably. The subject of the publication is a compilation of two new concepts in the field of Intelligent Transport Systems. These concepts are V2V (Vehicle - to - Vehicle) and A2A (Autonomous vehicle - to - Autonomous vehicle). Their comparison was carried out in terms of functionality, communication, vehicle equipment, legal aspects and the anticipated date of their entry into the market. Also examples of first tests and implementations of vehicles with driver assistance systems, and semi-autonomous vehicles were presented.

scholarly journals A Route Reservation Approach for an Autonomous Vehicles Routing Problem

2018 ◽  
Vol 220 ◽  
pp. 02004 ◽  
Author(s):  
Anton Agafonov ◽  
Aleksandr Borodinov
Keyword(s):  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Intelligent Transportation System ◽  
Road Traffic ◽  
Autonomous Vehicle ◽  
Routing Problem ◽  
Microscopic Traffic Simulation ◽  
The Road ◽  
On The Road ◽  
Road Segments

Autonomous vehicle development is one of many trends that will affect future transport demands and planning needs. Autonomous vehicles management in the context of an intelligent transportation system could significantly reduce the traffic congestion level and decrease the overall travel time in a network. In this work, we investigate a route reservation architecture to manage road traffic within an urban area. The routing architecture decomposes road segments into time and spatial slots and for every vehicle, it makes the reservation of the appropriate slots on the road segments in the selected route. This approach allows to predict the traffic in the network and to find the shortest path more precisely. We propose to use a rerouting procedure to improve the quality of the routing approach. Experimental study of the routing architecture is conducted using microscopic traffic simulation in SUMO package.

Pedestrians’ understanding of fully autonomous vehicles (FAV) intent to stop

2020 ◽  
Vol 64 (1) ◽  
pp. 1931-1932
Author(s):  
Michal Hochman ◽  
Tal Oron-Gilad
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Background Color ◽  
Urban Road ◽  
The Road ◽  
Message Type ◽  
On The Road ◽  
Future Work ◽  
Road Crossing ◽  
Distance Conditions

This study explored pedestrians’ understanding of Fully Autonomous Vehicle (FAV) intention and what influences their decision to cross. Twenty participants saw fixed simulated urban road crossing scenes with a FAV present on the road. The scenes differed from one another in the FAV’s messages: the external Human-Machine Interfaces (e-HMI) background color, message type and modality, the FAV’s distance from the crossing place, and its size. Eye-tracking data and objective measurements were collected. Results revealed that pedestrians looked at the e-HMI before making their decision; however, they did not always make the decision according to the e-HMIs’ color, instructions (in advice messages), or intention (in status messages). Moreover, when they acted according to the e-HMI proposition, for certain distance conditions, they tended to hesitate before making the decision. Findings suggest that pedestrians’ decision making to cross depends on a combination of the e-HMI implementation and the car distance. Future work should explore the robustness of the findings in dynamic and more complex crossing environments.

scholarly journals An Integrated Model for User State Detection of Subjective Discomfort in Autonomous Vehicles

Vehicles ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 764-777
Author(s):  
Dario Niermann ◽  
Alexander Trende ◽  
Klas Ihme ◽  
Uwe Drewitz ◽  
Cornelia Hollander ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Linear Models ◽  
Driving Simulator ◽  
Autonomous Vehicle ◽  
The Road ◽  
Novel Approach ◽  
Driving Scenario ◽  
On The Road ◽  
Using Data ◽  
Subjective Discomfort

The quickly rising development of autonomous vehicle technology and increase of (semi-) autonomous vehicles on the road leads to an increased demand for more sophisticated human–machine-cooperation approaches to improve trust and acceptance of these new systems. In this work, we investigate the feeling of discomfort of human passengers while driving autonomously and the automatic detection of this discomfort with several model approaches, using the combination of different data sources. Based on a driving simulator study, we analyzed the discomfort reports of 50 participants for autonomous inner city driving. We found that perceived discomfort depends on the driving scenario (with discomfort generally peaking in complex situations) and on the passenger (resulting in interindividual differences in reported discomfort extend and duration). Further, we describe three different model approaches on how to predict the passenger discomfort using data from the vehicle’s sensors as well as physiological and behavioral data from the passenger. The model’s precision varies greatly across the approaches, the best approach having a precision of up to 80%. All of our presented model approaches use combinations of linear models and are thus fast, transparent, and safe. Lastly, we analyzed these models using the SHAP method, which enables explaining the models’ discomfort predictions. These explanations are used to infer the importance of our collected features and to create a scenario-based discomfort analysis. Our work demonstrates a novel approach on passenger state modelling with simple, safe, and transparent models and with explainable model predictions, which can be used to adapt the vehicles’ actions to the needs of the passenger.

scholarly journals REVIEW ON THE ROAD TRAFFIC NOISE ASSESSMENT

2016 ◽  
Vol 22 (1) ◽  
Author(s):  
PETROVICI ALINA ◽  
TOMOZEI CLAUDIA ◽  
NEDEFF FLORIN ◽  
IRIMIA OANA ◽  
PANAINTE-LEHADUS MIRELA
Keyword(s):  
Urban Areas ◽  
Road Traffic ◽  
Traffic Noise ◽  
Legal Aspects ◽  
Urban Traffic ◽  
Road Traffic Noise ◽  
Action Plans ◽  
Noise Mapping ◽  
The Road ◽  
On The Road

<p>This paper presents a synthesis of current state of the assessment of road traffic noise in urban areas considering economic, social and legal aspects. Therefore, there were described several prediction methods of the urban traffic noise. These methods are useful in calculating the exposure of the population at noise levels which exceed the permissible limits. Mapping is one of the most common methods used for the assessment of noise. Whether it is industrial, airport, rail or road traffic noise, noise mapping provides accurate data needed later in developing action plans against noise. The road traffic noise assessments are performed periodically, and a representative picture of the noise in the analysed areas is obtained. Then, the action plans can be developed in order to reduce road traffic noise, where it is necessary.</p>

Regaining Sight of Humanity on The Roadway towards Automation

2020 ◽  
Vol 2020 (16) ◽  
pp. 88-1-88-5
Author(s):  
Mónica López-González
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Machine Learning Algorithms ◽  
Technological Advancement ◽  
Cognitive Intelligence ◽  
Safety Standards ◽  
The Road ◽  
Technological Advances ◽  
On The Road ◽  
Problem Solve

A primary goal of the auto industry is to revolutionize transportation with autonomous vehicles. Given the mammoth nature of such a target, success depends on a clearly defined balance between technological advances, machine learning algorithms, physical and network infrastructure, safety, standards and regulations, and end-user education. Unfortunately, technological advancement is outpacing the regulatory space and competition is driving deployment. Moreover, hope is being built around algorithms that are far from reaching human-like capacities on the road. Since human behaviors and idiosyncrasies and natural phenomena are not going anywhere anytime soon and so-called edge cases are the roadway norm, the industry stands at a historic crossroads. Why? Because human factors such as cognitive and behavioral insights into how we think, feel, act, plan, make decisions, and problem-solve have been ignored. Human cognitive intelligence is foundational to driving the industry’s ambition forward. In this paper I discuss the role of the human in bridging the gaps between autonomous vehicle technology, design, implementation, and beyond.

scholarly journals Autonomous Drifting Using Reinforcement Learning

2021 ◽  
Author(s):  
László Orgován ◽  
Tamás Bécsi ◽  
Szilárd Aradi
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Learning Algorithm ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
The Road ◽  
Model Free ◽  
On The Road ◽  
Self Driving Cars ◽  
Planning Problems

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.

scholarly journals REVIEW ON THE ROAD TRAFFIC NOISE ASSESSMENT

2016 ◽  
Vol 22 (1) ◽  
pp. 81-89
Author(s):  
ALINA PETROVICI ◽  
CLAUDIA TOMOZEI ◽  
FLORIN NEDEFF ◽  
OANA IRIMIA ◽  
MIRELA PANAINTE-LEHADUS
Keyword(s):  
Urban Areas ◽  
Road Traffic ◽  
Traffic Noise ◽  
Legal Aspects ◽  
Urban Traffic ◽  
Road Traffic Noise ◽  
Action Plans ◽  
Noise Mapping ◽  
The Road ◽  
On The Road

This paper presents a synthesis of current state of the assessment of road traffic noise in urban areas considering economic, social and legal aspects. Therefore, there were described several prediction methods of the urban traffic noise. These methods are useful in calculating the exposure of the population at noise levels which exceed the permissible limits. Mapping is one of the most common methods used for the assessment of noise. Whether it is industrial, airport, rail or road traffic noise, noise mapping provides accurate data needed later in developing action plans against noise. The road traffic noise assessments are performed periodically, and a representative picture of the noise in the analysed areas is obtained. Then, the action plans can be developed in order to reduce road traffic noise, where it is necessary.

scholarly journals Review of Autonomous Intelligent Vehicles for Urban Driving and Parking

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1021
Author(s):  
Teck Kai Chan ◽  
Cheng Siong Chin
Keyword(s):  
Traffic Safety ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Intelligent Vehicles ◽  
Smart Device ◽  
Enabling Technology ◽  
Research Gaps ◽  
The Road ◽  
Driving Behaviors ◽  
On The Road

With the concept of Internet-of-Things, autonomous vehicles can provide higher driving efficiency, traffic safety, and freedom for the driver to perform other tasks. This paper first covers enabling technology involving a vehicle moving out of parking, traveling on the road, and parking at the destination. The development of autonomous vehicles relies on the data collected for deployment in actual road conditions. Research gaps and recommendations for autonomous intelligent vehicles are included. For example, a sudden obstacle while the autonomous vehicle executes the parking trajectory on the road is discussed. Several aspects of social problems, such as the liability of an accident affecting the autonomous vehicle, are described. A smart device to detect abnormal driving behaviors to prevent possible accidents is briefly discussed.

A Groundwork Based Novel Routing Protocol for Vehicular Ad hoc Networks

2017 ◽  
Vol 7 (11) ◽  
pp. 121
Author(s):  
Amolkirat Singh ◽  
Guneet Saini
Keyword(s):  
Routing Protocol ◽  
Packet Loss ◽  
Secure Communication ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Road Traffic ◽  
Traditional Approach ◽  
Unexpected Events ◽  
The Road ◽  
On The Road

Many people lose their life and/or are injured due to accidents or unexpected events taking place on road networks. Besides traffic jams, these accidents generate a tremendous waste of time and fuel. Undoubtedly, if the vehicles are provided with timely and dynamic information related to road traffic conditions, any unexpected events or accidents, the safety and efficiency of the transportation system with respect to time, distance, fuel consumption and environmentally destructive emissions can be improved. In the field of computer and information science, Vehicular Ad hoc Network (VANET) have recently emerged as an effective tool for improving road safety through propagation of warning messages among the vehicles in the network about potential obstacles on the road ahead. VANET is a research area which is in more demand among the researchers, the automobile industries and scientists to discover about the loopholes and advantages of the vehicular networks so that efficient routing algorithms can be developed which can provide reliable and secure communication among the mobile nodes.In this paper, we propose a Groundwork Based Ad hoc On Demand Distance Vector Routing Protocol (GAODV) focus on how the Road Side Units (RSU’s) utilized in the architecture plays an important role for making the communication reliable. In the interval of finding the suitable path from source to destination the packet loss may occur and the delay also is counted if the required packet does not reach the specified destination on time. So to overcome delay, packet loss and to increase throughput GAODV approach is followed. The performance parameters in the GAODV comes out to be much better than computed in the traditional approach.

The human-like trajectory planning for autonomous vehicles based on optimal control in a test track environment

2021 ◽  
pp. 095440702110090
Author(s):  
Xing Xu ◽  
Minglei Li ◽  
Feng Wang ◽  
Ju Xie ◽  
Xiaohan Wu ◽  
...  
Keyword(s):  
Optimal Control ◽  
Autonomous Vehicles ◽  
Optimal Trajectory ◽  
Control Method ◽  
Autonomous Vehicle ◽  
Trajectory Data ◽  
Test Track ◽  
Optimal Control Method ◽  
The Future ◽  
On The Road

A human-like trajectory could give a safe and comfortable feeling for the occupants in an autonomous vehicle especially in corners. The research of this paper focuses on planning a human-like trajectory along a section road on a test track using optimal control method that could reflect natural driving behaviour considering the sense of natural and comfortable for the passengers, which could improve the acceptability of driverless vehicles in the future. A mass point vehicle dynamic model is modelled in the curvilinear coordinate system, then an optimal trajectory is generated by using an optimal control method. The optimal control problem is formulated and then solved by using the Matlab tool GPOPS-II. Trials are carried out on a test track, and the tested data are collected and processed, then the trajectory data in different corners are obtained. Different TLCs calculations are derived and applied to different track sections. After that, the human driver’s trajectories and the optimal line are compared to see the correlation using TLC methods. The results show that the optimal trajectory shows a similar trend with human’s trajectories to some extent when driving through a corner although it is not so perfectly aligned with the tested trajectories, which could conform with people’s driving intuition and improve the occupants’ comfort when driving in a corner. This could improve the acceptability of AVs in the automotive market in the future. The driver tends to move to the outside of the lane gradually after passing the apex when driving in corners on the road with hard-lines on both sides.

Sign in / Sign up

Export Citation Format

Share Document