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The Potential Diffusion and Impacts of Autonomous Electric Vehicles: Evidence and Modelling Approaches

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 9319

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Special Issue Editor

Dr. Chengxiang Zhuge

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Guest Editor

Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Interests: transportation electrification; autonomous vehicle; complex urban system; spatial planning; agent-based modelling; big data analysis

Special Issue Information

Dear Colleagues,

Electric vehicles (EVs) and autonomous vehicles (AVs) are two disruptive and sustainable innovations in the transport sector, and their diffusion could potentially impact connected urban systems, such as transportation, land use, energy, environment, economy and population systems. AVs would be likely introduced in the market in the near future when the penetration rate of EVs is high, and thus the future AVs are likely to be electric. As a combination of EV and AV, autonomous electric vehicle (AEV) tends to be more promising, as it would take advantages of both EVs and AVs (Zhuge and Wang, 2021).

Both AVs and EVs have received considerable attention in academia. However, previous studies have tended to look at AVs and EVs separately, resulting in a limited understanding of the potential diffusion, use and impacts of AEVs. Therefore, this Special Issue aims to be a collection of work on AEVs. Specifically, the topics of this Special Issue include but are not limited to: adoption and use of AEVs (e.g., preferences towards AEVs, travel behavior of AEV users, in-vehicle activities, and AEVs’ interactions with other transport modes), and the impacts of AEVs on transportation systems (e.g., travel demand, traffic conditions, accessibility and transport infrastructures), land use (e.g., urban form and residential location choice), energy systems (e.g., energy efficiency and transition), environment systems (e.g., GHG emissions and traffic noise), economy system (e.g., employment), and population systems (e.g., inequities) (Zhuge and Wang, 2021). The work to be published in this Special Issue is expected to contain the state-of-the-art empirical evidence and/or modelling approaches for the adoption, use or impacts of AEVs.

You may choose our Joint Special Issue in Future Transportation.

Dr. Chengxiang Zhuge
Guest Editor

Manuscript Submission Information

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Keywords

autonomous vehicle
electric vehicle
adoption behavior
use behavior
impact assessment
empirical evidence
modelling approaches

Published Papers (3 papers)

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Research

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20 pages, 2788 KiB

Open AccessArticle

Performance Evaluation of Lane Detection and Tracking Algorithm Based on Learning-Based Approach for Autonomous Vehicle

by Swapnil Waykole, Nirajan Shiwakoti and Peter Stasinopoulos

Sustainability 2022, 14(19), 12100; https://doi.org/10.3390/su141912100 - 24 Sep 2022

Cited by 4 | Viewed by 2135

Abstract

Disruptive technology, especially autonomous vehicles, is predicted to provide higher safety and reduce road traffic emissions. Lane detection and tracking are critical building blocks for developing autonomous or intelligent vehicles. This study presents a lane detecting algorithm for autonomous vehicles on different road pavements (structured and unstructured roads) to overcome challenges such as the low detection accuracy of lane detection and tracking. First, datasets for performance evaluation were created using an interpolation method. Second, a learning-based approach was used to create an algorithm using the steering angle, yaw angle, and sideslip angle as inputs for the adaptive controller. Finally, simulation tests for the lane recognition method were carried out by utilising a road driving video in Melbourne, Australia, and the BDD100K dataset created by the Berkeley DeepDrive Industrial Consortium. The mean detection accuracy ranges from 97% to 99%, and the detection time ranges from 20 to 22 ms under various road conditions with our proposed algorithm. This lane detection algorithm outperformed conventional techniques in terms of accuracy and processing time, as well as efficiency in lane detection and overcoming road interferences. The proposed algorithm will contribute to advancing the lane detection and tracking of intelligent-vehicle driving assistance and help further improve intelligent vehicle driving safety. Full article

(This article belongs to the Special Issue The Potential Diffusion and Impacts of Autonomous Electric Vehicles: Evidence and Modelling Approaches)

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20 pages, 2090 KiB

Open AccessEditor’s ChoiceArticle

Efficiency of Governmental Policy and Programs to Stimulate the Use of Low-Emission and Electric Vehicles: The Case of Romania

by Ioana C. Sechel and Florin Mariasiu

Sustainability 2022, 14(1), 45; https://doi.org/10.3390/su14010045 - 21 Dec 2021

Cited by 9 | Viewed by 3120

Abstract

The contemporary demands for massive reductions in industrial pollution caused by the transport sector, especially in large urban agglomerations, compel local and national authorities to propose, develop, and implement programs and policies that have the ultimate goal of significantly reducing (or eliminating) pollution. The aim of this article is to provide a primary analysis of the effectiveness of Romanian government policies in terms of reducing pollution (CO₂ emissions) caused by transportation (due to the “Rabla Plus” (RP) program, through which financial subsidies are granted for the purchase of a new plug-in hybrid electric vehicles (PHEVs) or battery electric vehicle (BEVs)). After analyzing the justification for the use of low-emission and electric vehicles in traffic (as a major solution to eliminate pollution), a comparative analysis of energy-efficient transport for Romania and Europe is presented in order to identify the directions in which it is necessary to develop and implement government policies specifically in Romania, considering a series of indicators chosen and considered by the authors to be important, including CO₂ emissions compared with the size of the road infrastructure, the number of registered vehicles, the number of passengers transported, and the quantity of goods transported. With the identification of the ability of government programs to encourage the acquisition and use of low-emission and electric vehicles in traffic, the efficiency achieved is calculated in terms of the net CO₂ emissions eliminated (average values of 1949.23 CO₂ tons/year and 1.71 CO₂ tons/vehicle). Furthermore, this aspect is also beneficial for analyses in terms of the economic costs involved (the associated costs are estimated to be 7034.17 EUR/ton of CO₂ eliminated from the transportation sector), identifying new directions of action that are more cost-effective and sustainable and on which government policies should focus in the future. Full article

(This article belongs to the Special Issue The Potential Diffusion and Impacts of Autonomous Electric Vehicles: Evidence and Modelling Approaches)

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Review

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29 pages, 558 KiB

Open AccessEditor’s ChoiceReview

State-of-the-Art of Factors Affecting the Adoption of Automated Vehicles

by Yilun Chen, Nirajan Shiwakoti, Peter Stasinopoulos and Shah Khalid Khan

Sustainability 2022, 14(11), 6697; https://doi.org/10.3390/su14116697 - 30 May 2022

Cited by 12 | Viewed by 3411

Abstract

Around 90% of accidents stem from human error. Disruptive technology, especially automated vehicles (AVs), can respond to the problems by, for instance, eradicating human error when driving, thus increasing energy efficiency due to the platoon effect, and potentially giving more space to human activities by decreasing parking space; hence, with the introduction of the autonomous vehicle, the public attitude towards its adoption needs to be understood to develop appropriate strategies and policies to leverage the potential benefits. There is a lack of a systematic and comprehensive literature review on adoption attitudes toward AVs that considers various interlinked factors such as road traffic environment changes, AV transition, and policy impacts. This study aims to synthesize past research regarding public acceptance attitude toward AVs. More specifically, the study investigates driverless technology and uncertainty, road traffic environment changes, policy impact, and findings from AV adoption modelling approaches, to understand public attitudes towards AVs. The study points out critical problems and future directions for analysis of AV impacts, such as the uncertainty on AVs adoption experiment, policy implementation and action plans, the uncertainty of AV-related infrastructure, and demand modelling. Full article

(This article belongs to the Special Issue The Potential Diffusion and Impacts of Autonomous Electric Vehicles: Evidence and Modelling Approaches)

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