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Vehicle Safe Motion in Mixed Vehicle Technologies Environment
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Vehicle Safe Motion in Mixed Vehicle Technologies Environment

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: 28 February 2025 | Viewed by 5271

Special Issue Editors

Dr. Stergios Mavromatis

E-Mail Website
Guest Editor
Department of Transportation Planning and Engineering, School of Civil Engineering, National Technical University of Athens, 15780 Athens, Greece
Interests: highway vehicle interaction through vehicle dynamics modelling; highway design consistency; road safety
Prof. Dr. Yasser Hassan

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada
Interests: modelling of roadway alignments; interaction of highway infrastructure and connected autonomous vehicles; effects of driver perception and behavior; reducing collision risk; design consistency and traffic safety; probabilistic highway design
Prof. Dr. George Yannis

E-Mail Website
Guest Editor
Department of Transportation Planning and Engineering, School of Civil Engineering, National Technical University of Athens, 15780 Athens, Greece
Interests: road safety; planning and management of transportation systems

Special Issue Information

Dear Colleagues,

The penetration rate of connected and automated vehicles (CAVs) is increasing every year. There are many discussions regarding the enormous benefits resulting from a fully automated vehicle– human–road interaction, such as mitigating environmental impacts, facilitating mobility, reducing travel time, increasing road network capacity, and advancing further road safety levels.

While a fully connected vehicle–human–road scenario is expected to substantially contribute to improving road network operational and safety aspects, there is a plethora of related new challenges, which must be addressed with caution. Amongst them is the intermediate scheme, which, by including a heterogeneous mixture of human-driven, semi-autonomous, and fully autonomous vehicles, constitutes a critical and challenging task for implementing CAVs.

This Special Issue aims to address problems and suggest potential solutions for mitigating operational and safety aspects that arise during this intermediate vehicle–human–road interaction period. This Special Issue also provides an opportunity to present the latest scientific research on emerging detection technologies, Big Data analytics, and naturalistic driving experiments, among other things.

Dr. Stergios Mavromatis
Prof. Dr. Yasser Hassan
Prof. Dr. George Yannis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. World Electric Vehicle Journal is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • vehicle
  • road
  • safety
  • operational aspects
  • CAVs

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Published Papers (5 papers)

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Research

17 pages, 3232 KiB  
Article
Impact of Mixed-Vehicle Environment on Speed Disparity as a Measure of Safety on Horizontal Curves
by Tahmina Sultana and Yasser Hassan
World Electr. Veh. J. 2024, 15(10), 456; https://doi.org/10.3390/wevj15100456 - 9 Oct 2024
Viewed by 653
Abstract
Due to the transition of vehicle fleets from conventional driver-operated vehicles (DVs) to connected vehicles (CVs) and/or automated vehicles (AVs), vehicles with different technologies will soon operate on the same roads in a mixed-vehicle environment. Although a major goal of vehicle connectivity and [...] Read more.
Due to the transition of vehicle fleets from conventional driver-operated vehicles (DVs) to connected vehicles (CVs) and/or automated vehicles (AVs), vehicles with different technologies will soon operate on the same roads in a mixed-vehicle environment. Although a major goal of vehicle connectivity and automation is to improve traffic safety, negative safety impacts may persist in the mixed-vehicle environment. Speed disparity measures have been shown in the literature to be related to safety performance. Therefore, speed disparity measures are derived from the expected speed distributions of different vehicle technologies and are used as surrogate measures to assess the safety of mixed-vehicle environments and identify the efficacy of prospective countermeasures. This paper builds on speed models in the literature to predict the speed behavior of CVs, AVs, and DVs on horizontal curves on freeways and major arterials. The paper first proposes a methodology to determine speed disparity measures on horizontal curves without any control in terms of speed limit. The impact of speed limit or advisory speed, as a safety countermeasure, is modeled and assessed using different strategies to set the speed limit. The results indicated that the standard deviation of the speeds of all vehicles (σc) in a mixed environment would increase on arterial roads under no control compared to the case of DV-only traffic. This speed disparity can be reduced using an advisory speed as a safety countermeasure to decrease the adverse safety impacts in this environment. Moreover, it was shown that compared to the practice of a constant speed limit based on road classification, the advisory speed is more effective when it is based on the speed behavior of various vehicle types. Full article
(This article belongs to the Special Issue Vehicle Safe Motion in Mixed Vehicle Technologies Environment)
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11 pages, 1723 KiB  
Article
Influence of an Automated Vehicle with Predictive Longitudinal Control on Mixed Urban Traffic Using SUMO
by Paul Heckelmann and Stephan Rinderknecht
World Electr. Veh. J. 2024, 15(10), 448; https://doi.org/10.3390/wevj15100448 - 30 Sep 2024
Viewed by 662
Abstract
In this paper, an approach to quantify the area of influence of an intelligent longitudinally controlled autonomous vehicle in an urban, mixed-traffic environment is proposed. The intelligent vehicle is executed with a predictive longitudinal control, which anticipates the future traffic scenario in order [...] Read more.
In this paper, an approach to quantify the area of influence of an intelligent longitudinally controlled autonomous vehicle in an urban, mixed-traffic environment is proposed. The intelligent vehicle is executed with a predictive longitudinal control, which anticipates the future traffic scenario in order to reduce unnecessary acceleration. The shown investigations are conducted within a simulated traffic environment of the city center of Darmstadt, Germany, which is carried out in the traffic simulation software “Simulation of Urban Mobility” (SUMO). The longitudinal dynamics of the not automated vehicles are considered with the Extended Intelligent Driver Model, which is an approach to simulate real human driver behavior. The results show that, in addition to the energy saving caused by a predictive longitudinal control of the ego vehicle, this system can also reduce the consumption of surrounding traffic participants significantly. The area of influence can be quantified to four vehicles and up to 250 m behind. Full article
(This article belongs to the Special Issue Vehicle Safe Motion in Mixed Vehicle Technologies Environment)
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26 pages, 11862 KiB  
Article
Comparative Assessment of Expected Safety Performance of Freeway Automated Vehicle Managed Lanes
by Jana McLean Sarran and Yasser Hassan
World Electr. Veh. J. 2024, 15(10), 447; https://doi.org/10.3390/wevj15100447 - 29 Sep 2024
Viewed by 574
Abstract
The use of dedicated lanes, known as managed lanes (MLs), on freeways is an established traffic management strategy to reduce congestion. Allowing automated vehicles (AVs) in existing MLs or dedicating MLs for AVs, referred to as AVMLs, has been suggested in the literature [...] Read more.
The use of dedicated lanes, known as managed lanes (MLs), on freeways is an established traffic management strategy to reduce congestion. Allowing automated vehicles (AVs) in existing MLs or dedicating MLs for AVs, referred to as AVMLs, has been suggested in the literature as a tool to improve traffic operation and safety performance as AVs and driver-operated vehicles (DVs) coexist in a mixed-vehicle environment. This paper focuses on investigating the safety impacts of deploying AVMLs on freeways by repurposing general-purpose lanes (GPLs). Four ML strategies considering different lane positions and access controls were implemented in a traffic microsimulation under different AV market adoption rates (MARs) and traffic demand levels, and trajectories were used to extract rear-end and lane change conflicts. The time-to-collision (TTC) surrogate safety measure was used to identify critical conflicts using a time threshold dependent on the type of following vehicle. Rates of conflicts involving different vehicle types for all ML strategies were compared to the case of heterogeneous traffic. The results indicated that the rates of rear-end conflicts involving the same vehicle type as the lead and following vehicle, namely DV-DV and AV-AV conflicts, increased with ML implementation as more vehicles of the same type traveled in the same lane(s). By comparing the aggregated conflict rates, the design options that were deemed to negatively impact traffic efficiency and capacity were also found to negatively impact traffic safety. However, other ML options were found to be feasible in terms of traffic operation and safety performance, especially at traffic demand levels below capacity. Specifically, one left-side AVML with continuous access was found to have lower or comparable aggregated conflict rates compared to heterogenous traffic at 25% and 50% MARs, and, thus, it is expected to have positive or neutral safety impacts. Full article
(This article belongs to the Special Issue Vehicle Safe Motion in Mixed Vehicle Technologies Environment)
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19 pages, 6138 KiB  
Article
Visual Detection of Traffic Incident through Automatic Monitoring of Vehicle Activities
by Abdul Karim, Muhammad Amir Raza, Yahya Z. Alharthi, Ghulam Abbas, Salwa Othmen, Md. Shouquat Hossain, Afroza Nahar and Paolo Mercorelli
World Electr. Veh. J. 2024, 15(9), 382; https://doi.org/10.3390/wevj15090382 - 23 Aug 2024
Cited by 1 | Viewed by 1317
Abstract
Intelligent transportation systems (ITSs) derive significant advantages from advanced models like YOLOv8, which excel in predicting traffic incidents in dynamic urban environments. Roboflow plays a crucial role in organizing and preparing image data essential for computer vision models. Initially, a dataset of 1000 [...] Read more.
Intelligent transportation systems (ITSs) derive significant advantages from advanced models like YOLOv8, which excel in predicting traffic incidents in dynamic urban environments. Roboflow plays a crucial role in organizing and preparing image data essential for computer vision models. Initially, a dataset of 1000 images is utilized for training, with an additional 500 images reserved for validation purposes. Subsequently, the Deep Simple Online and Real-time Tracking (Deep-SORT) algorithm enhances scene analyses over time, offering continuous monitoring of vehicle behavior. Following this, the YOLOv8 model is deployed to detect specific traffic incidents effectively. By combining YOLOv8 with Deep SORT, urban traffic patterns are accurately detected and analyzed with high precision. The findings demonstrate that YOLOv8 achieves an accuracy of 98.4%, significantly surpassing alternative methodologies. Moreover, the proposed approach exhibits outstanding performance in the recall (97.2%), precision (98.5%), and F1 score (95.7%), underscoring its superior capability in accurate prediction and analyses of traffic incidents with high precision and efficiency. Full article
(This article belongs to the Special Issue Vehicle Safe Motion in Mixed Vehicle Technologies Environment)
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13 pages, 1699 KiB  
Article
Game-Based Vehicle Strategy Equalization Algorithm for Unsignalized Intersections
by Guangbing Xiao, Kang Liu, Ning Sun and Yong Zhang
World Electr. Veh. J. 2024, 15(4), 146; https://doi.org/10.3390/wevj15040146 - 2 Apr 2024
Viewed by 1234
Abstract
To address the coordination issue of connected autonomous vehicles (CAVs) at unsignalized intersections, this paper proposes a game-theory-based distributed strategy equalization algorithm. To begin, the vehicles present in the scene are conceptualized as participants in a game theory. The decision-payoff function takes into [...] Read more.
To address the coordination issue of connected autonomous vehicles (CAVs) at unsignalized intersections, this paper proposes a game-theory-based distributed strategy equalization algorithm. To begin, the vehicles present in the scene are conceptualized as participants in a game theory. The decision-payoff function takes into account three critical performance indicators: driving safety, driving comfort, and driving efficiency. Then, virtual logic lines connect the front and rear extremities of vehicles with odd and even numbers at the intersection to create a virtual logic ring. By dividing the virtual logic ring into numerous overlapping game groups, CAVs can engage in negotiation and interaction within their respective game groups. This enables the revision of action strategies and facilitates interaction between the overlapping game groups. A further application of the genetic algorithm (GA) is the search for the optimal set of strategies in constrained multi-objective optimization problems. The proposed decision algorithm is ultimately assessed and certified through a collaborative simulation utilizing Python and SUMO. In comparison to the first-come, first-served algorithm and the cooperative driving model based on cooperative games, the average passing delay is decreased by 40.7% and 6.17%, respectively, resulting in an overall improvement in the traffic system’s passing efficiency. Full article
(This article belongs to the Special Issue Vehicle Safe Motion in Mixed Vehicle Technologies Environment)
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