Platoon Signal Priority in Connected-Autonomous Vehicle Environments: Algorithm Development and Testing

As traffic congestion increases day by day, it becomes necessary to improve the existing roadway facilities to maintain satisfactory operational and safety performances. New vehicle technologies, such as Connected and Autonomous Vehicles (CAV) have a potential to significantly improve transportation systems. Using the advantages of CAVs, this study developed signalized intersection control strategy algorithm that optimizes the operations of CAVs and allows signal priority for connected platoons. The algorithm was tested in VISSIM microsimulation using a real-world urban corridor. The tested scenarios include a 2040 Do-Nothing scenario, and CAV alternatives with 25%, 50%, 75% and 100% CAV penetration rate. The results show a significant reduction in intersection delays (26% - 38%) and travel times (6% - 20%), depending on the penetration rate, as well as significant improvements on the network-wide level. CAV penetration rates of 50% or more have a potential to significantly improve all operational measures of effectiveness.

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

Applied Sciences ◽

10.3390/app11041514 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1514 ◽

Cited By ~ 2

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.

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Autonomous Electric Vehicle Sharing System Design

Journal of Mechanical Design ◽

10.1115/1.4034471 ◽

2016 ◽

Vol 139 (1) ◽

Cited By ~ 24

Author(s):

Namwoo Kang ◽

Fred M. Feinberg ◽

Panos Y. Papalambros

Keyword(s):

System Design ◽

Electric Vehicle ◽

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Transportation Systems ◽

Fleet Size ◽

Gasoline Engines ◽

Ann Arbor ◽

Vehicle Technologies ◽

System Design Optimization

Car sharing services promise “green” transportation systems. Two vehicle technologies offer marketable, sustainable sharing: autonomous vehicles (AVs) eliminate customer requirements for car pick-up and return, and battery electric vehicles entail zero emissions. Designing an autonomous electric vehicle (AEV) fleet must account for the relationships among fleet operations, charging station (CS) operations, electric powertrain performance, and consumer demand. This paper presents a system design optimization framework integrating four subsystem problems: fleet size and assignment schedule; number and locations of charging stations; vehicle powertrain requirements; and service fees. We also compare an AEV service and autonomous vehicle (AV) service with gasoline engines. A case study for an autonomous fleet operating in Ann Arbor, MI, is used to examine AEV and AV sharing systems profitability and feasibility for a variety of market scenarios. The results provide practical insights for service system decision makers.

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An Open-Source Scale Model Platform for Teaching Autonomous Vehicle Technologies

Sensors ◽

10.3390/s21113850 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3850

Author(s):

Bastien Vincke ◽

Sergio Rodriguez Rodriguez Florez ◽

Pascal Aubert

Keyword(s):

Open Source ◽

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Constrained Systems ◽

Scale Model ◽

Technical Students ◽

Vehicle Technologies ◽

Model Platform ◽

High Level ◽

Time And Energy

Emerging technologies in the context of Autonomous Vehicles (AV) have drastically evolved the industry’s qualification requirements. AVs incorporate complex perception and control systems. Teaching the associated skills that are necessary for the analysis of such systems becomes a very difficult process and existing solutions do not facilitate learning. In this study, our efforts are devoted to proposingan open-source scale model vehicle platform that is designed for teaching the fundamental concepts of autonomous vehicles technologies that are adapted to undergraduate and technical students. The proposed platform is as realistic as possible in order to present and address all of the fundamental concepts that are associated with AV. It includes all on-board components of a stand-alone system, including low and high level functions. Such functionalities are detailed and a proof of concept prototype is presented. A set of experiments is carried out, and the results obtained using this prototype validate the usability of the model for the analysis of time- and energy-constrained systems, as well as distributed embedded perception systems.

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Novel Routing Algorithm for Autonomous Vehicles in Smart Transportation System

Journal of Ubiquitous Computing and Communication Technologies - December 2019 ◽

10.36548/jucct.2021.3.002 ◽

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.

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Towards a Severity Assessment Method for Potential Cyber Attacks to Connected and Autonomous Vehicles

Journal of Advanced Transportation ◽

10.1155/2020/6873273 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Qiyi He ◽

Xiaolin Meng ◽

Rong Qu

Keyword(s):

Cyber Security ◽

Intelligent Transportation Systems ◽

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Assessment Method ◽

Transportation Systems ◽

Cyber Attacks ◽

Large Set ◽

Security Attacks ◽

Physical Damage

CAV (connected and autonomous vehicle) is a crucial part of intelligent transportation systems. CAVs utilize both sensors and communication components to make driving decisions. A large number of companies, research organizations, and governments have researched extensively on the development of CAVs. The increasing number of autonomous and connected functions however means that CAVs are exposed to more cyber security vulnerabilities. Unlike computer cyber security attacks, cyber attacks to CAVs could lead to not only information leakage but also physical damage. According to the UK CAV Cyber Security Principles, preventing CAVs from cyber security attacks need to be considered at the beginning of CAV development. In this paper, a large set of potential cyber attacks are collected and investigated from the aspects of target assets, risks, and consequences. Severity of each type of attacks is then analysed based on clearly defined new set of criteria. The levels of severity for the attacks can be categorized as critical, important, moderate, and minor. Mitigation methods including prevention, reduction, transference, acceptance, and contingency are then suggested. It is found that remote control, fake vision on cameras, hidden objects to LiDAR and Radar, spoofing attack to GNSS, and fake identity in cloud authority are the most dangerous and of the highest vulnerabilities in CAV cyber security.

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

Proceedings of the 56th ISOCARP World Planning Congress ◽

10.47472/pqrn8660 ◽

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.

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How virtue signalling makes us better: Moral preferences with respect to autonomous vehicle type choices

10.31234/osf.io/36vzk ◽

2019 ◽

Cited By ~ 1

Author(s):

Robin Kopecky ◽

Michaela Košová ◽

Daniel D. Novotný ◽

Jaroslav Flegr ◽

David Černý

Keyword(s):

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Biological Species ◽

Transportation Systems ◽

Personal Choice ◽

Moral Issues ◽

Web Based ◽

The Public ◽

Vehicle Type ◽

Research Questions

Autonomous vehicles (henceforth AVs) are expected to significantly benefit our transportation systems, their safety, efficiency, and impact on environment. However, many technical, social, legal, and moral questions and challenges concerning AVs and their introduction to the mass market still remain. One of the pressing moral issues has to do with the choice between AV types that differ in their built-in algorithms for dealing with situations of unavoidable lethal collision. In this paper we present the results of our study of moral preferences with respect to three types of AVs: (1) selfish AVs that protect the lives of passenger(s) over any number of bystanders; (2) altruistic AVs that minimize the number of casualties, even if this leads to death of passenger(s); and (3) conservative AVs that abstain from interfering in such situations even if it leads to the death of a higher number of subjects or death of passenger(s). We furthermore differentiate between scenarios in which participants are to make their decisions privately or publicly, and for themselves or for their offspring. We disregard gender, age, health, biological species and other characteristics of (potential) casualties that can affect the preferences and decisions of respondents in our scenarios. Our study is based on a sample of 2769 mostly Czech volunteers (1799 women, 970 men; age IQR: 25-32). The data come from our web-based questionnaire which was accessible from May 2017 to December 2017. We aim to answer the following two research questions: (1) Whether the public visibility of an AV type choice makes this choice more altruistic and (2) which type of situation is more problematic with regard to the altruistic choice: opting for society as a whole, for oneself, or for one’s offspring.Our results show that respondents exhibit a clear preference for an altruistic utilitarian strategy for AVs. This preference is reinforced if the AV signals its strategy to others. The altruistic preference is strongest when people choose software for everybody else, weaker in personal choice, and weakest when choosing for one’s own child. Based on the results we conclude that, in contrast to a private choice, a public choice is considerably more likely to pressure consumers in their personal choice to accept a non-selfish solution, making it a reasonable and relatively cheap way to shift car owners and users towards higher altruism. Also, a hypothetical voting in Parliament about a single available program is less selfish when the voting does not take place in secret.

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Impact of Autonomous-Vehicle-Only Lanes in Mixed Traffic Conditions

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119847475 ◽

2019 ◽

Vol 2673 (9) ◽

pp. 430-439 ◽

Cited By ~ 4

Author(s):

Hwapyeong Yu ◽

Sehyun Tak ◽

Minju Park ◽

Hwasoo Yeo

Keyword(s):

Autonomous Vehicles ◽

Road Traffic ◽

Positive Impact ◽

Penetration Rate ◽

Autonomous Vehicle ◽

Market Penetration ◽

Mixed Traffic ◽

Negative Effects ◽

Average Speed ◽

Time To Collision

The introduction of autonomous vehicles (AVs) in the near future will have a significant impact on road traffic. AVs may have advantages in efficiency and convenience, but safety can be compromised in mixed operations of manual vehicles and AVs. To deal with the issues associated with mixed traffic and to avoid its negative effects, a special purpose lane reserved for AVs can be proposed to segregate AVs from manual vehicles. In this research, we analyze the effect on efficiency and safety of AVs in mixed traffic and in a situation where an AV-only lane is deployed. In the analysis, we investigate the average speed, the throughput, and the inverse time-to-collision (ITTC). We differentiate the behaviors of manual vehicles and AVs through the reaction time, desired speed, and car-following models. As a result, we observe that the efficiency is improved when the market penetration rate of AVs increases, especially when the highway throughput increases by up to 84% in the case of mixed traffic. However, safety worsens when the market penetration of AVs is under 40%. In this case, the average speed can be improved and the frequency of dangerous situations (ITTC > 0.49) can be reduced drastically in the merging section by making the innermost lane AV-only. Accordingly, we conclude that AV-only lanes can have a significant positive impact on efficiency and safety when the market penetration rate of AVs is low.

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SUSTAINABILITY EFFECTS OF NEXT-GENERATION INTERSECTION CONTROL FOR AUTONOMOUS VEHICLES

Transport ◽

10.3846/16484142.2015.1080760 ◽

2015 ◽

Vol 30 (3) ◽

pp. 342-352 ◽

Cited By ~ 15

Author(s):

Zhixia (Richard) Li ◽

Madhav V. Chitturi ◽

Lang Yu ◽

Andrea R. Bill ◽

David A. Noyce

Keyword(s):

Energy Consumption ◽

Autonomous Vehicles ◽

High Volume ◽

Ambient Air ◽

Next Generation ◽

Pm 2.5 ◽

Time Space ◽

Moderate Volume ◽

Vehicle Technologies ◽

Intersection Control

Transportation sustainability is adversely affected by recurring traffic congestions, especially at urban intersections. Frequent vehicle deceleration and acceleration caused by stop-and-go behaviours at intersections due to congestion adversely impacts energy consumption and ambient air quality. Availability of the maturing vehicle technologies such as autonomous vehicles and Vehicle-To-Vehicle (V2V) / Vehicle-To-Infrastructure (V2I) communications provides technical feasibility to develop solutions that can reduce vehicle stops at intersections, hence enhance the sustainability of intersections. This paper presents a next-generation intersection control system for autonomous vehicles, which is named ACUTA. ACUTA employs an enhanced reservation-based control algorithm that controls autonomous vehicles’ passing sequence at an intersection. Particularly, the intersection is divided into n-by-n tiles. An intersection controller reserves certain time-space for each vehicle, and assures no conflict exists between reservations. The algorithm was modelled in microscopic traffic simulation platform VISSIM. ACUTA algorithm modelling as well as enhancement strategies to minimize vehicle intersection stops and eventually emission and energy consumption were discussed in the paper. Sustainability benefits offered by this next-generation intersection were evaluated and compared with traditional intersection control strategies. The evaluation reveals that multi-tile ACUTA reduces carbon monoxide (CO) and Particulate Matter (PM) 2.5 emissions by about 5% under low to moderate volume conditions and by about 3% under high volume condition. Meanwhile, energy consumption is reduced by about 4% under low to moderate volume conditions and by about 12% under high volume condition. Compared with four-way stop control, single-tile ACUTA reduces CO and PM 2.5 emissions as well as energy consumption by about 15% under any prevailing volume conditions. These findings validated the sustainability benefits of employing next-generation vehicle technologies in intersection traffic control. In addition, extending the ACUTA to corridor level was explored in the paper.

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The potential effects of autonomous vehicles on walking

Global Health Promotion ◽

10.1177/17579759211019219 ◽

2021 ◽

pp. 175797592110192

Author(s):

Simone Pettigrew

Keyword(s):

Traffic Congestion ◽

Trade Unions ◽

Policy Development ◽

Autonomous Vehicles ◽

Transportation Systems ◽

Central Feature ◽

The European Union ◽

Vehicle Ownership ◽

Walking Activity ◽

Wide Range

Vehicle automation is progressing rapidly and autonomous vehicles (AVs) are forecast to become a central feature of transportation systems globally. This development has the potential to result in profound changes in walking behaviors. The present study examined this issue from the perspective of relevant experts for the purpose of informing health policy. Interviews were conducted with 44 key stakeholders in Australia ( n = 34), the European Union ( n = 5), the UK ( n = 4), and the US ( n = 1). The stakeholders represented a wide range of sectors including government, AV manufacturing/servicing companies, transport policy consortiums, technology firms, insurers (public and private), trade unions, consumer representation organizations, and academia. Two potential scenarios were evident in interviewees’ discussions of the ways AVs are likely to be introduced and the implications for walking behaviors. The most beneficial scenario, but the least likely to eventuate, was considered to be the situation where people relinquish private vehicle ownership and rely on a combination of walking, public transport, and on-demand transport. The alternative scenario involved greater private AV ownership, traffic congestion, and urban sprawl, resulting in less walking activity. The convergence of the stakeholders’ views around the opposing identified scenarios highlights the need for proactive policy development to ensure the emerging transport transformation does not result in substantial increases in sedentarism.

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