scholarly journals Autonomous vehicles and smart cities: future directions of ownership vs shared mobility

2020 ◽  
Author(s):  
Nacer-Eddine Bezai ◽  
◽  
Benachir Medjdoub ◽  
Fodil Fadli ◽  
Moulay Larby Chalal ◽  
...  
Keyword(s):  
Public Transportation ◽  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Travel Speed ◽  
On Demand ◽  
Shared Mobility ◽  
The Future ◽  
Self Driving Cars

Over the last decade, there has been increasing discussions about self-driving cars and how most auto-makers are racing to launch these products. However, this discourse is not limited to transportation only, but how such vehicles will affect other industries and specific aspects of our daily lives as future users such as the concept of work while being driven and productivity, entertainment, travel speed, and deliveries. Although these technologies are beneficial, access to these potentials depends on the behaviour of their users. There is a lack of a conceptual model that elucidate the acceptance of people to Self-driving cars. Service on-demand and shared mobility are the most critical factors that will ensure the successful adoption of these cars. This paper presents an analysis of public opinions in Nottingham, UK, through a questionnaire about the future of Autonomous vehicles' ownership and the extent to which they accept the idea of vehicle sharing. Besides, this paper tests two hypotheses. Firstly, (a) people who usually use Public transportation like (taxi, bus, tram, train, carpooling) are likely to share an Autonomous Vehicle in the future. Secondly, (b) people who use Private cars are expected to own an Autonomous Vehicle in the future. To achieve this aim, a combination of statistical methods such as logistic regression has been utilised. Unexpectedly, the study findings suggested that AVs ownership will increase contrary to what is expected, that Autonomous vehicles will reduce ownership. Besides, participants have shown low interest in sharing AVs. Therefore, it is likely that ownership of AVs will increase for several reasons as expressed by the participants such as safety, privacy, personal space, suitability to children and availability. Actions must be taken to promote shared mobility to avoid AVs possession growth. The ownership diminution, in turn, will reduce traffic congestion, energy and transport efficiency, better air quality. That is why analysing the factors that influence the mindset and attitude of people will enable us to understand how to shift from private cars to transport-on-demand, which is a priority rather than promoting the technology.

scholarly journals Three-Dimensional LiDAR Decoder Design for Autonomous Vehicles in Smart Cities

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 18
Author(s):  
Yu-Cheng Fan ◽  
Sheng-Bi Wang
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Vehicles ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Three Dimensional ◽  
Original Data ◽  
Depth Information ◽  
Learning Technology ◽  
Cloud Data ◽  
Self Driving Cars

With the advancement of artificial intelligence, deep learning technology is applied in many fields. The autonomous car system is one of the most important application areas of artificial intelligence. LiDAR (Light Detection and Ranging) is one of the most critical components of self-driving cars. LiDAR can quickly scan the environment to obtain a large amount of high-precision three-dimensional depth information. Self-driving cars use LiDAR to reconstruct the three-dimensional environment. The autonomous car system can identify various situations in the vicinity through the information provided by LiDAR and choose a safer route. This paper is based on Velodyne HDL-64 LiDAR to decode data packets of LiDAR. The decoder we designed converts the information of the original data packet into X, Y, and Z point cloud data so that the autonomous vehicle can use the decoded information to reconstruct the three-dimensional environment and perform object detection and object classification. In order to prove the performance of the proposed LiDAR decoder, we use the standard original packets used for the comparison of experimental data, which are all taken from the Map GMU (George Mason University). The average decoding time of a frame is 7.678 milliseconds. Compared to other methods, the proposed LiDAR decoder has higher decoding speed and efficiency.

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.

scholarly journals Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2143
Author(s):  
Sara Paiva ◽  
Mohd Abdul Ahad ◽  
Gautami Tripathi ◽  
Noushaba Feroz ◽  
Gabriella Casalino
Keyword(s):  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
City Planning ◽  
Smart Cities ◽  
The Sustainable Development ◽  
Current Trends ◽  
Enabling Technologies ◽  
Entry Points ◽  
Development Goals ◽  
Smart Mobility

The increasing population across the globe makes it essential to link smart and sustainable city planning with the logistics of transporting people and goods, which will significantly contribute to how societies will face mobility in the coming years. The concept of smart mobility emerged with the popularity of smart cities and is aligned with the sustainable development goals defined by the United Nations. A reduction in traffic congestion and new route optimizations with reduced ecological footprint are some of the essential factors of smart mobility; however, other aspects must also be taken into account, such as the promotion of active mobility and inclusive mobility, encouraging the use of other types of environmentally friendly fuels and engagement with citizens. The Internet of Things (IoT), Artificial Intelligence (AI), Blockchain and Big Data technology will serve as the main entry points and fundamental pillars to promote the rise of new innovative solutions that will change the current paradigm for cities and their citizens. Mobility-as-a-service, traffic flow optimization, the optimization of logistics and autonomous vehicles are some of the services and applications that will encompass several changes in the coming years with the transition of existing cities into smart cities. This paper provides an extensive review of the current trends and solutions presented in the scope of smart mobility and enabling technologies that support it. An overview of how smart mobility fits into smart cities is provided by characterizing its main attributes and the key benefits of using smart mobility in a smart city ecosystem. Further, this paper highlights other various opportunities and challenges related to smart mobility. Lastly, the major services and applications that are expected to arise in the coming years within smart mobility are explored with the prospective future trends and scope.

Not So Fast: A Brief Plea for Muddling Through the Problems of Autonomous Vehicle Liability

2020 ◽  
Vol 13 (2) ◽  
pp. 189-195
Author(s):  
Adam F. Scales
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Know How ◽  
The Future ◽  
Auto Accidents

AbstractAutonomous Vehicles (AVs) are likely to change a great deal about the practical workings of the liability system for auto accidents. However, we cannot know how just yet. Attempts to anticipate the future and preemptively redesign the liability system around its imagined contours are likely to invite error and frustration. Discretion often being the better part of valor, I suggest we muddle through a bit first.

scholarly journals An Efficiency Enhancing Methodology for Multiple Autonomous Vehicles in an Urban Network Adopting Deep Reinforcement Learning

2021 ◽  
Vol 11 (4) ◽  
pp. 1514 ◽  
Author(s):  
Quang-Duy Tran ◽  
Sang-Hoon Bae
Keyword(s):  
Reinforcement Learning ◽  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Penetration Rate ◽  
Autonomous Vehicle ◽  
Effective Means ◽  
Urban Network ◽  
Learning Agents ◽  
Policy Optimization ◽  
The Impact

To reduce the impact of congestion, it is necessary to improve our overall understanding of the influence of the autonomous vehicle. Recently, deep reinforcement learning has become an effective means of solving complex control tasks. Accordingly, we show an advanced deep reinforcement learning that investigates how the leading autonomous vehicles affect the urban network under a mixed-traffic environment. We also suggest a set of hyperparameters for achieving better performance. Firstly, we feed a set of hyperparameters into our deep reinforcement learning agents. Secondly, we investigate the leading autonomous vehicle experiment in the urban network with different autonomous vehicle penetration rates. Thirdly, the advantage of leading autonomous vehicles is evaluated using entire manual vehicle and leading manual vehicle experiments. Finally, the proximal policy optimization with a clipped objective is compared to the proximal policy optimization with an adaptive Kullback–Leibler penalty to verify the superiority of the proposed hyperparameter. We demonstrate that full automation traffic increased the average speed 1.27 times greater compared with the entire manual vehicle experiment. Our proposed method becomes significantly more effective at a higher autonomous vehicle penetration rate. Furthermore, the leading autonomous vehicles could help to mitigate traffic congestion.

Pedestrians, Autonomous Vehicles, and Cities

2016 ◽  
Vol 38 (1) ◽  
pp. 6-12 ◽  
Author(s):  
Adam Millard-Ball
Keyword(s):  
Travel Behavior ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Urban Neighborhoods ◽  
Urban Traffic ◽  
Risk Averse ◽  
Strategic Interactions ◽  
Road Users ◽  
Self Driving Cars ◽  
Time Autonomous

Autonomous vehicles, popularly known as self-driving cars, have the potential to transform travel behavior. However, existing analyses have ignored strategic interactions with other road users. In this article, I use game theory to analyze the interactions between pedestrians and autonomous vehicles, with a focus on yielding at crosswalks. Because autonomous vehicles will be risk-averse, the model suggests that pedestrians will be able to behave with impunity, and autonomous vehicles may facilitate a shift toward pedestrian-oriented urban neighborhoods. At the same time, autonomous vehicle adoption may be hampered by their strategic disadvantage that slows them down in urban traffic.

scholarly journals Novel Routing Algorithm for Autonomous Vehicles in Smart Transportation System

2021 ◽  
Vol 3 (3) ◽  
pp. 164-179
Author(s):  
Haoxiang Wang
Keyword(s):  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Intelligent Transportation System ◽  
Autonomous Vehicle ◽  
Routing Algorithm ◽  
Transportation System ◽  
Traffic Condition ◽  
Smart Transportation ◽  
Centralized Management ◽  
The City

In recent times Automation is emerging every day and bloomed in every sector. Intelligent Transportation System (ITS) is one of the important branches of Automation. The major constrain in the transportation system is traffic congestion. This slurps the individual’s time and consequently pollutes the environment. A centralized management is required for optimizing the transportation system. The current traffic condition is predicted by evaluating the historical data and thereby it reduces the traffic congestion. The periodic update of traffic condition in each and every street of the city is obtained and the data is transferred to the autonomous vehicle. These data are obtained from the simulation results of transportation prediction tool SUMO. It is proved that our proposed work reduces the traffic congestion and maintains ease traffic flow and preserves the fleet management.

scholarly journals Self-Driving Cars and Criminal Liability

2020 ◽  
Vol 17 (3-4) ◽  
Author(s):  
Béla Csitei
Keyword(s):  
Autonomous Vehicles ◽  
Criminal Liability ◽  
Autonomous Vehicle ◽  
Essential Elements ◽  
Criminal Sanction ◽  
Criminal Offence ◽  
The Subject ◽  
Definition Of ◽  
Self Driving Cars

After clarifying the concepts of automated and autonomous vehicles, the purpose of the study is to investigate how reasonable the criminal sanction is arising from accidents caused by autonomous vehicles. The next question to be answered is that the definition of the crime according to the Hungarian law may be applied in case of traffic related criminal offences caused by automated and autonomous vehicles. During my research I paid special attention to two essential elements of criminal offence, namely the human act and guilt. Furthermore, I strived for finding solution for the next problem, as well: if the traffic related criminal offence is committed by driving an autonomous vehicle, how to define the subject of criminal liability.

Future Blockchain Technology for Autonomous Applications/Autonomous Vehicle

2022 ◽  
pp. 1027-1038
Author(s):  
Arnab Kumar Show ◽  
Abhishek Kumar ◽  
Achintya Singhal ◽  
Gayathri N. ◽  
K. Vengatesan
Keyword(s):  
Autonomous Vehicles ◽  
New Technology ◽  
Autonomous Vehicle ◽  
The Self ◽  
Third Party ◽  
Traffic Information ◽  
Online Transactions ◽  
The Third ◽  
Blockchain Technology ◽  
Self Driving Cars

The autonomous industry has rapidly grown for self-driving cars. The main purpose of autonomous industry is trying to give all types of security, privacy, secured traffic information to the self-driving cars. Blockchain is another newly established secured technology. The main aim of this technology is to provide more secured, convenient online transactions. By using this new technology, the autonomous industry can easily provide more suitable, safe, efficient transportation to the passengers and secured traffic information to the vehicles. This information can easily gather by the roadside units or by the passing vehicles. Also, the economical transactions can be possible more efficiently since blockchain technology allows peer-to-peer communications between nodes, and it also eliminates the need of the third party. This chapter proposes a concept of how the autonomous industry can provide more adequate, proper, and safe transportation with the help of blockchain. It also examines for the possibility that autonomous vehicles can become the future of transportation.

Current Developments in Autonomous Vehicle Policy in the United States: Federalism’s Influence in State and National Regulatory Law and Policy

Global Jurist ◽  
2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Christopher Salatiello ◽  
Troy B. Felver
Keyword(s):  
United States ◽  
Autonomous Vehicles ◽  
New Technologies ◽  
Autonomous Vehicle ◽  
The United States ◽  
Great Promise ◽  
Maximum Benefit ◽  
Regulatory Law ◽  
Regulatory Bodies ◽  
Self Driving Cars

Abstract With the advent of autonomous vehicles, especially self-driving cars, there is great promise for society. However, cars are not islands; they operate in a community of vehicles. Laws and regulations are crafted to allow the maximum benefit for the community while imposing the fewest costs. Unfortunately, a full accounting of these benefits and costs is not entirely clear at promulgation. Because the technologies and how they will be used are so uncertain, regulatory bodies have to try to build on what they have done in the past, sometimes successfully and sometimes unevenly. This paper will examine several regulatory attempts involving these new technologies in the United States, both on the federal and state levels. Also considered will be the interaction of these regulations under a federal system with defined and specific responsibilities for both sovereigns. A view on future developments is provided to gauge the directions additional regulation could take. Finally, generalizable lessons from this approached will be summarized.

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