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Recent Advances in Autonomous Vehicles
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Recent Advances in Autonomous Vehicles

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

Deadline for manuscript submissions: 30 November 2025 | Viewed by 4071

Special Issue Editors

Dr. Soualmi Boussaad

E-Mail Website
Guest Editor
Institut de Recherche Technologique SystemX, Palaiseau, France
Interests: ADAS; autonomous vehicle; models and simulation; test and validation; certification
Dr. Duc-Thang Nguyen

E-Mail
Guest Editor
Institut de Recherche Technologique SystemX, Palaiseau, France
Interests: hybrid intelligence for autonomous vehicles and ADAS systems

Special Issue Information

Dear Colleagues,

Autonomous vehicles have attracted a wide array of attention from engineers and researchers in recent years. Many works have been conducted; as a consequence, we can now use a driverless car named Waymo in several cities in America. In addition, autonomous vehicles used to deliver goods are also in development. However, we have still not achieved commercial certificated autonomous vehicles that can be used in an open environment.

The main objective of driving is to keep traffic safe and fluid. The term “safe traffic” includes infrastructure, goods, and traffic users being safe, including passengers in the subject vehicle. Safe autonomous driving is a task that combines many well-trained capacities, such as the capacity of observing, analyzing, predicting, anticipating, deciding, and acting at any time in order to adapt the dynamics of the vehicle to the traffic situation in terms of cancelling disturbances from the environment. Thus, the development of autonomous vehicles requires many advances in technology and efforts in the development of driving strategies and the management of fleets of autonomous vehicles.

We expect that autonomous vehicles will be popular in the very near future, meaning that everybody can profit from this safe, stressless means of transport with all the required comfort of private cars.

This Special Issue focuses on recent advances in research and practice for the development of autonomous vehicles. Submissions to this Special Issue are welcome to focus on, but not be limited to, the following topics:

  • Advances in intelligent sensors: cameras, radars, and lidars;
  • Advances in data fusion for sensors to build an efficient perception system;
  • Advances in decision-making;
  • Integration of situation awareness and anticipation in the driving algorithm;
  • Integration of disturbance cancellation;
  • Integration of V2X, V2V, and X2V communication to increase driving capacity as well as the safety of traffic;
  • Advances in the management of fleets of autonomous vehicles;
  • Advances in itinerary creation for the autonomous vehicle;
  • Advances in trajectory calculation for autonomous control;
  • Methods of verification and validation of autonomous driving by simulation or from the data collection during the ride;
  • Scenarios for the test and validation of autonomous vehicles.

Dr. Soualmi Boussaad
Dr. Duc-Thang Nguyen
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

  • autonomous driving
  • actuator control scheme
  • sensor data processing
  • data fusion
  • V2V and V2X communication
  • collective perception
  • digital maps
  • localization
  • trajectory calculation
  • path planning
  • decisions
  • distributed decision making
  • central decision making
  • reasoning
  • problem solving
  • experience-oriented artificial intelligence
  • human-like intelligence
  • machine learning and deep learning
  • behavior verification and validation
  • fleet management

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Published Papers (5 papers)

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Research

25 pages, 5857 KiB  
Article
Evaluation of the Intersection Sight Distance at Stop-Controlled Intersections in a Mixed Vehicle Environment
by Jana Sarran and Sean Sarran
World Electr. Veh. J. 2025, 16(5), 245; https://doi.org/10.3390/wevj16050245 - 23 Apr 2025
Viewed by 386
Abstract
The introduction of autonomous vehicles (AVs) on roadways will result in a mixed vehicle environment consisting of these vehicles and manual vehicles (MVs). This vehicular environment will impact intersection sight distances (ISDs) due to differences in the driving behaviors of AVs and MVs. [...] Read more.
The introduction of autonomous vehicles (AVs) on roadways will result in a mixed vehicle environment consisting of these vehicles and manual vehicles (MVs). This vehicular environment will impact intersection sight distances (ISDs) due to differences in the driving behaviors of AVs and MVs. Currently, ISD design values for stop-controlled intersections are based on AASHTO’s guidelines, which account only for human driver behaviors. However, with AVs in the traffic stream, it is important to assess whether the existing MV-based ISDs are compliant when an AV is present at an intersecting roadway. Hence, this study utilizes the Monte Carlo Simulation method to compute the PNC of various object locations on the major and minor roadways for possible vehicle interaction types in a mixed vehicle environment at a stop-controlled intersection. Scenarios generated considered these variables and the major roadway speed limits and sight distance triangles (SDTs). ISD non-compliance was determined by examining the PNC metric, which occurred when the demand exceeded the supply. The results indicated that when AV–MV interaction was present at the intersection, the MV-based ISD design was non-compliant. However, it is possible to correct this non-compliance issue by reducing the AV speed limit. Full article
(This article belongs to the Special Issue Recent Advances in Autonomous Vehicles)
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21 pages, 21844 KiB  
Article
Multi-Agent Deep Reinforcement Learning Cooperative Control Model for Autonomous Vehicle Merging into Platoon in Highway
by Jiajia Chen, Bingqing Zhu, Mengyu Zhang, Xiang Ling, Xiaobo Ruan, Yifan Deng and Ning Guo
World Electr. Veh. J. 2025, 16(4), 225; https://doi.org/10.3390/wevj16040225 - 10 Apr 2025
Viewed by 457
Abstract
This study presents the first investigation into the problem of autonomous vehicle (AV) merging into existing platoons, proposing a multi-agent deep reinforcement learning (MA-DRL)-based cooperative control framework. The developed MA-DRL architecture enables coordinated learning among multiple autonomous agents to address the multi-objective coordination [...] Read more.
This study presents the first investigation into the problem of autonomous vehicle (AV) merging into existing platoons, proposing a multi-agent deep reinforcement learning (MA-DRL)-based cooperative control framework. The developed MA-DRL architecture enables coordinated learning among multiple autonomous agents to address the multi-objective coordination challenge through synchronized control of platoon longitudinal acceleration, AV steering and acceleration. To enhance training efficiency, we develop a dual-layer multi-agent maximum Q-value proximal policy optimization (MAMQPPO) method, which extends the multi-agent PPO algorithm (a policy gradient method ensuring stable policy updates) by incorporating maximum Q-value action selection for platoon gap control and discrete command generation. This method simplifies the training process by using maximum Q-value action policy optimization to learn platoon gap selection and discrete action commands. Furthermore, a partially decoupled reward function (PD-Reward) is designed to properly guide the behavioral actions of both AVs and platoons while accelerating network convergence. Comprehensive highway simulation experiments show the proposed method reduces merging time by 37.69% (12.4 s vs. 19.9 s) and energy consumption by 58% (3.56 kWh vs. 8.47 kWh) compared to existing methods (the quintic polynomial-based + PID (Proportional–Integral–Differential)). Full article
(This article belongs to the Special Issue Recent Advances in Autonomous Vehicles)
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14 pages, 1136 KiB  
Article
Generating Realistic Vehicle Trajectories Based on Vehicle–Vehicle and Vehicle–Map Interaction Pattern Learning
by Peng Li, Biao Yu, Jun Wang, Xiaojun Zhu, Hui Zhang, Chennian Yu and Chen Hua
World Electr. Veh. J. 2025, 16(3), 145; https://doi.org/10.3390/wevj16030145 - 4 Mar 2025
Viewed by 491
Abstract
Diversified and realistic traffic scenarios are a crucial foundation for evaluating the safety of autonomous driving systems in simulations. However, a considerable number of current methods generate scenarios that lack sufficient realism. To address this issue, this paper proposes a vehicle trajectory generation [...] Read more.
Diversified and realistic traffic scenarios are a crucial foundation for evaluating the safety of autonomous driving systems in simulations. However, a considerable number of current methods generate scenarios that lack sufficient realism. To address this issue, this paper proposes a vehicle trajectory generation method based on vehicle–vehicle and vehicle–map interaction pattern learning. By leveraging a multihead self-attention mechanism, the model efficiently captures complex dependencies among vehicles, enhancing its ability to learn realistic traffic dynamics. Moreover, the multihead cross-attention mechanism is also used to learn the interaction features between the vehicles and the map, addressing the challenge of trajectory generation’s difficulty in perceiving static environments. This proposed method enhances the model’s ability to learn natural traffic sequences, enable the generation of more realistic traffic flow, and provide strong support for the testing and optimization of autonomous driving systems. Experimental results show that compared to the Trafficgen baseline model, the proposed method achieves a 26% improvement in ADE and a 20% improvement in FDE. Full article
(This article belongs to the Special Issue Recent Advances in Autonomous Vehicles)
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15 pages, 4464 KiB  
Article
Effect of Driver Energy Saving Awareness on Energy Consumption in a Microscopic Traffic Model
by Zawar Hussain Khan, Faryal Ali, Thomas Aaron Gulliver, Ahmed B. Altamimi, Mohammad Alsaffar and Wilayat Khan
World Electr. Veh. J. 2024, 15(12), 551; https://doi.org/10.3390/wevj15120551 - 26 Nov 2024
Viewed by 1064
Abstract
Road traffic significantly impacts global energy consumption and emissions, both of which contribute to climate change. Thus, energy conservation and emission reduction in road transportation are critical concerns, and traffic flow modeling is key to evaluating and improving these metrics. Therefore, this paper [...] Read more.
Road traffic significantly impacts global energy consumption and emissions, both of which contribute to climate change. Thus, energy conservation and emission reduction in road transportation are critical concerns, and traffic flow modeling is key to evaluating and improving these metrics. Therefore, this paper develops a microscopic traffic model to characterize energy consumption reflecting driver energy saving awareness. The well-known Intelligent Driver (ID) model cannot predict traffic dynamics within an energy saving driving environment because it is based on a fixed acceleration exponent. Simulation results are presented which demonstrate that the energy consumption in the proposed model decreases as driver energy saving awareness increases. Furthermore, traffic, in the proposed model, experiences smaller variations in flow, speed, acceleration, and density, higher speeds, and less congestion compared to the ID model. Thus, the proposed model can be employed to conserve energy and reduce emissions, thereby decreasing the overall carbon footprint of traffic and contributing to a more sustainable environment. Full article
(This article belongs to the Special Issue Recent Advances in Autonomous Vehicles)
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18 pages, 4079 KiB  
Article
A Study on Consumers’ Willingness to Purchase Autonomous Vehicles from a Multi-Party Interaction Perspective: A Tripartite Evolutionary Game Model Involving the Government, Automobile Manufacturers, and Consumers
by Chengcheng Mo, Fujian Chen and Zeyu Wang
World Electr. Veh. J. 2024, 15(11), 498; https://doi.org/10.3390/wevj15110498 - 30 Oct 2024
Viewed by 1062
Abstract
With the rapid development of autonomous driving technology, the advent of the autonomous driving era has become inevitable. An in-depth study of consumers’ willingness to purchase autonomous vehicles is critical to accelerating the adoption and commercialization of autonomous vehicles. By constructing a tripartite [...] Read more.
With the rapid development of autonomous driving technology, the advent of the autonomous driving era has become inevitable. An in-depth study of consumers’ willingness to purchase autonomous vehicles is critical to accelerating the adoption and commercialization of autonomous vehicles. By constructing a tripartite evolutionary game model of governments, automobile manufacturers, and consumers, we analyze the stable choice of unilateral strategy and equilibrium strategy of autonomous vehicle purchase intention. The MATLAB2022b tool was used for data simulation analysis to verify the validity of the conclusion and the influence of related factors on the purchase intention toward autonomous vehicles. The results show the following: (1) The combination of government support, active R&D, and consumer purchasing is the evolutionary stability strategy (ESS) of the model. (2) With an increase in government support, the probability of automobile enterprises taking the initiative to participate in R&D also increases. However, the negative impact of risk can significantly reduce the incentive for firms to conduct R&D and reduce the effectiveness of government support. (3) Government subsidies to consumers and purchase incentives offered by automotive companies can significantly increase the likelihood that consumers will purchase an autonomous vehicle. Based on these findings, recommendations are made to strengthen government support, establish risk mitigation mechanisms, and strengthen market promotion efforts to promote the commercialization of autonomous vehicles. The study provides a new perspective for understanding multi-party interactions in the rollout of autonomous vehicles and provides valuable insights for policymakers and industry stakeholders. Full article
(This article belongs to the Special Issue Recent Advances in Autonomous Vehicles)
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