scholarly journals How Connected Autonomous Vehicles Would Affect Our World? —— A Literature Review on the Impacts of CAV on Road Capacity, Environment and Public Attitude

2019 ◽  
Vol 296 ◽  
pp. 01007 ◽  
Author(s):  
Shuya Zong
Keyword(s):  
Stochastic Models ◽  
Autonomous Vehicles ◽  
Penetration Rate ◽  
Rapid Development ◽  
Autonomous Vehicle ◽  
Public Attitude ◽  
Gas Emission ◽  
The Public ◽  
Road Capacity ◽  
Future Condition

With the rapid development of technology, connected autonomous vehicle (CAV) is getting close to the reality. The application of CAV causes changes to road capacity, gas emission, public attitude and other realms. Lots of efforts have been spent in quantifying the potential changes and this paper is an attempt to review the relevant researches. There will be three sections, presenting review of the impacts on road capacity, environment and public attitude respectively. There is a large amount of papers making models to predict future road capacity with various penetration rate of CAV and they obtain quite different interesting results. To predict the future condition more properly, more stochastic models should be proposed. In terms of influence on environment, it may be hard to conclude whether CAV will exacerbate or relieve global warming by looking at current researches. It would be valuable to conduct a quantitative analysis on this issue. For the public attitude, this paper mainly focus on whether people are willing to use CAV and future efforts that may help with the promotion of CAV.

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.

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

2019 ◽  
Vol 65 (4) ◽  
pp. 1-9
Author(s):  
Milan Zlatkovic ◽  
Andalib Shams
Keyword(s):  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Penetration Rate ◽  
Autonomous Vehicle ◽  
Transportation Systems ◽  
Vehicle Technologies ◽  
Measures Of Effectiveness ◽  
Urban Corridor ◽  
Intersection Control ◽  
Day By Day

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.

scholarly journals The City-Wide Impacts of the Interactions between Shared Autonomous Vehicle-Based Mobility Services and the Public Transportation System

2021 ◽  
Vol 13 (12) ◽  
pp. 6725
Author(s):  
Sehyun Tak ◽  
Soomin Woo ◽  
Sungjin Park ◽  
Sunghoon Kim
Keyword(s):  
Public Transportation ◽  
Autonomous Vehicles ◽  
Impact Analysis ◽  
Autonomous Vehicle ◽  
The Public ◽  
Private Vehicle ◽  
Simulation Based ◽  
Mode Share

When attempts are made to incorporate shared autonomous vehicles (SAVs) into urban mobility services, public transportation (PT) systems are affected by the changes in mode share. In light of that, a simulation-based method is presented herein for analyzing the manner in which mode choices of local travelers change between PT and SAVs. The data used in this study were the modal split ratios measured based on trip generation in the major cities of South Korea. Subsequently, using the simulated results, a city-wide impact analysis method is proposed that can reflect the differences between the two mode types with different travel behaviors. As the supply–demand ratio of SAVs increased in type 1 cities, which rely heavily on PT, use of SAVs gradually increased, whereas use of PT and private vehicles decreased. Private vehicle numbers significantly reduced only when SAVs and PT systems were complementary. In type 2 cities, which rely relatively less on PT, use of SAVs gradually increased, and use of private vehicles decreased; however, no significant impact on PT was observed. Private vehicle numbers were observed to reduce when SAVs were operated, and the reduction was a minimum of thrice that in type 1 cities when SAVs and PT systems interacted. Our results can therefore aid in the development of strategies for future SAV–PT operations.

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

2019 ◽  
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.

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

2019 ◽  
Vol 2673 (9) ◽  
pp. 430-439 ◽  
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.

scholarly journals Self-driving cars and liability in traffic - reality or fiction?

2018 ◽  
pp. 769-776
Author(s):  
Natasa Tomic-Petrovic
Keyword(s):  
Autonomous Vehicles ◽  
Technological Development ◽  
Autonomous Vehicle ◽  
Legal Framework ◽  
The Self ◽  
Public And Private ◽  
The Public ◽  
Private Transportation ◽  
Self Driving Cars ◽  
The City

Self-driving vehicles are considered to be the technology that will change the city, public and private transportation, as well as the concept of mobility in general. The great obstacle to self-driving vehicles are legal conditions, although the situation in this area is slowly changing. It is indeed true that producers need to gain more experience in testing vehicles without a driver on public roads before this technology is offered to the general public. The expansion of autonomous vehicles will depend on the public belief that the self-driving cars are considerably safer than those manually-controlled. Lawmakers intercede in favor of a balance between security and technological development. There should not be place for unsafe technologies on the roads, but the solution is not to prevent the easier way for vehicles that improve safety to reach consumers. If a man is not driving the vehicle, that appoints responsibility to the manufacturer of the self-driving operating system of the car in the event of a collision. Clarification of the blame for the accident will sometimes entail complex issues of allocating responsibility of man as the driver and those who provide technology of autonomous vehicle. The issue of privacy of the owner is also one of the current ones, because these data could be misused. Protection of privacy of the passenger should be in balance with the gain that the utilization of data brings. Self-driving cars may have to wait if the existing legal framework does not offer sufficient legal certainty.

scholarly journals From Trolleys to Risk: Models for Ethical Autonomous Driving

2020 ◽  
Author(s):  
Noah J. Goodall
Keyword(s):  
Public Health ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Public Health Issue ◽  
Ethical Challenges ◽  
Risk Models ◽  
Health Implications ◽  
The Public ◽  
Crash Rates

The article by Fleetwood in this issue of AJPH provides an overview of the public health implications of highly automated vehicles, with a focus on the ethics of a vehicle’s behavior when a crash is unavoidable, that is, its “ethical crashing algorithms.” Although autonomous vehicles are widely expected to reduce crash rates, those benefits may not be distributed equitably, and some users may receive more benefit than others. Just as airbags save many, they also kill a few that would otherwise not have died. This creates a smaller but persistent public health issue, and the authors provide a helpful exploration of the unique ethical challenges created by the (hopefully) rare autonomous vehicle crashes.

Evaluation Methodology of Leader-Follower Autonomous Vehicle System for Work Zone Maintenance

2021 ◽  
pp. 036119812098523
Author(s):  
Qing Tang ◽  
Yanqiu Cheng ◽  
Xianbiao Hu ◽  
Chenxi Chen ◽  
Yang Song ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Academic Research ◽  
Field Testing ◽  
Evaluation Methodology ◽  
The Public ◽  
Testing Procedures ◽  
Analysis Methodology ◽  
Highway Transportation ◽  
Promising Solution

Mobile and slow-moving operations, such as striping, sweeping, bridge flushing, and pothole patching, are critical for efficient and safe operation of a highway transportation system. However, reducing hazards for roadway workers and achieving a safer environment for both roadway maintenance operators and the public is a challenging problem. In 2017 alone, a total of 158,000 vehicle crashes occurred in work zones in the U.S.A., accounting for 61,000 injuries. The autonomous truck-mounted attenuator (ATMA) vehicle, sometimes referred to as an autonomous impact protection vehicle (AIPV), offers a promising solution to eliminate injuries to roadway maintenance workers and the public. This paper presents the evaluation methodology for the ATMA system, as well as the outcomes of field testing in Sedalia, Missouri. To the best of the authors’ knowledge, this is the first academic research to focus on ATMA. The ATMA system is first reviewed, followed by an introduction to the field testing procedures that includes descriptions of test cases and data collected, and their format. An analysis methodology is then proposed to quantitatively evaluate the system’s performance, and statistical models and hypothesis testing procedures are developed and presented. The numerical analysis results from real-world field testing under a controlled environment are presented, and the ATMA system’s performance is summarized. This paper can serve as a reference for transportation agencies that are interested in deploying similar technologies or for academic researchers to assess characteristics of autonomous vehicles and to apply knowledge gained in transportation modeling and simulation practices.

The public and the private: reconfiguring the interrelationship during a civil act

2019 ◽  
pp. 53-69
Author(s):  
Natalia Kostenko
Keyword(s):  
Public Sphere ◽  
Public Space ◽  
Rapid Development ◽  
Online Communication ◽  
Decisive Role ◽  
Physical Interaction ◽  
Bare Life ◽  
Public And Private ◽  
The Public ◽  
The Public Sphere

The subject matter of research interest here is the movement of sociological reflection concerning the interplay of public and private realms in social, political and individual life. The focus is on the boundary constructs embodying publicity, which are, first of all, classical models of the space of appearance for free citizens of the polis (H. Arendt) and the public sphere organised by communicative rationality (Ju. Habermas). Alternative patterns are present in modern ideas pertaining to the significance of biological component in public space in the context of biopolitics (M. Foucault), “inclusive exclusion of bare life” (G. Agamben), as well as performativity of corporeal and linguistic experience related to the right to participate in civil acts such as popular assembly (J. Butler), where the established distinctions between the public and the private are levelled, and the interrelationship of these two realms becomes reconfigured. Once the new media have come into play, both the structure and nature of the public sphere becomes modified. What assumes a decisive role is people’s physical interaction with online communication gadgets, which instantly connect information networks along various trajectories. However, the rapid development of information technology produces particular risks related to the control of communications industry, leaving both public and private realms unprotected and deforming them. This also urges us to rethink the issue of congruence of the two ideas such as transparency of societies and security.

Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards

2019 ◽  
Vol 2673 (12) ◽  
pp. 494-507 ◽  
Author(s):  
Mhafuzul Islam ◽  
Mashrur Chowdhury ◽  
Hongda Li ◽  
Hongxin Hu
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Semantic Segmentation ◽  
Steering Wheel ◽  
Potential Hazard ◽  
Driving System ◽  
Hazardous Object ◽  
Vision Based Navigation ◽  
Navigational System

Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.

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