Dear TIV Readers:
In this issue, I would like to share with you the following good news:
- By June 28th, we have received a total of 1576 submissions, with an average of 8.80 submissions per day (SPD). It is noteworthy that during the period from June 1st to 28th, 340 manuscripts were submitted to our periodical, averaging 12.14 SPD. This increase in submissions stands for more work to process and evaluate manuscripts, therefore IEEE TIV calls for the accelerated recruitment of competent and responsible Associate Editors and Reviewers. Your assistance in this task would be greatly appreciated.
- On June 28th, Clarivate released the 2022 Journal Citation Report. The impact factor (IF) of IEEE Transactions on Intelligent Vehicles (IEEE TIV) has hit an impressive 8.2. This represents 63.7% from the 5.009 IF received in 2021.
- In the Journal Citation Report Impact Factor List, IEEE TIV is ranked the 6th out of 40 publications in the subject category "Transportation Science & Technology", and the 27th of 275 in the category "Engineering, Electrical & Electronic", and the 24th of 145 publications in the category "Computer Science, Artificial Intelligence". Our periodical is classified as Q1 in all three subject categories.
The current issue comprises 3 letters and 22 regular papers. All letters are the outcome from our decentralized and hybrid workshops (DHW): the first letter results from our DHW on Scenario Engineering (SE), the second from Intelligent Vehicles for Education (IV4E), and the last from Ethics, Responsibility, and Sustainability (ERS).
Our editorial focuses on Scenario Engineering (SE) for Autonomous Vehicles (AVs), and we have already conducted 2 decentralized and hybrid symposia (DHS) and 4 DHWs on this project. After Scanning the Issue, we would like to address issues related to Trustworthy and Effective Artificial Intelligence for AVs based on the SE methodology
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Aims & Scope The IEEE Transactions on Intelligent Vehicles (TIV) publishes peer-reviewed articles that provide innovative research concepts and application results, report significant theoretical findings and application case studies, and raise aware-ness of pressing research and application challenges in areas of intelligent vehicles in a roadway environment, and in particular in automated vehicles. The TIV focuses on providing critical information to the intelligent vehicle community, serving as a dissemination vehicle for IEEE ITS Society members and the others to learn the state of the art development and progress on research and applications in the field of intelligent vehicles.
Topics Prospective authors are invited to submit original contributions or survey papers for review for publication in TIV. Topics of interest include (but are not limited to):
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According to the Journal CiteScore Ranking list published recently by Elsevier, the IEEE TIV has hit a CiteScore of 11.8 for 2022. In the Journal Cite-Score Ranking List, the IEEE TIV is currently ranked the 3rd out of all 121 publications in the subject category "Control and Optimization" subject, the 5th out of all 115 publications in the category "Automotive Engineering", and 36th out of all 301 publications in the category of "Artificial Intelligence" subject category.
Best Regards
Fei-Yue, EiC, IEEE TIV
Fei-Yue, EiC, IEEE TIV
Current Issue Catalog (No. 5, 2023)
Editorial
Scenarios Engineering: Enabling Trustworthy and Effective AI for Autonomous Vehicles
Abstract
The first journal impact factor (IF) of IEEE TIV is 5.009 with a CiteScore = 10.9 in 2022, signifying a promising start for our periodical. By April 2023, our real-time IF stands at 7.03 according to Web of Science, and CiteScore from Elsevier is 11.6. These numbers indicate that IEEE TIV is among the top-tier publications in the related fields.
Cite
X. Li and F. -Y. Wang, "Scenarios Engineering: Enabling Trustworthy and Effective AI for Autonomous Vehicles," in IEEE Transactions on Intelligent Vehicles, vol. 8, no. 5, pp. 3205-3210, May 2023, doi: 10.1109/TIV.2023.3269421.
Letters
Advanced Scenario Generation for Calibration and Verification of Autonomous Vehicles
Abstract
As driving scenarios and autonomous vehicles (AVs) become increasingly intricating, there is an increasing need for innovative frameworks that can enhance and test AV capabilities across diverse scenarios. At present, the design and validation for AVs predominantly rely on simulation software or real-road testing methodologies. However, these approaches possess inherent limitations, leading to inaccuracy, reduced efficiency, and potential hazards during the development process. This letter reports our first DHW (decentralized and hybrid workshop) on Scenarios Engineering (SE), that aims to calibrate and validate AV modules through advanced scenarios. Specifically, the DHW discusses combining SE with existing simulation software and real-road testing strategies, enabling developers to maximize the advantages of each approach while ensuring the safety and efficacy in the development of autonomous vehicles. Our findings demonstrate the benefits of enhancing realism of simulation scenarios in the aspects of both content and appearance. Furthermore, the use of scenarios engineering is explored in order to enhance the diversity and safety of real-road testing by means of integrating virtual and physical components.
Cite
X. Li, S. Teng, B. Liu, X. Dai, X. Na and F. -Y. Wang, "Advanced Scenario Generation for Calibration and Verification of Autonomous Vehicles," in IEEE Transactions on Intelligent Vehicles, vol. 8, no. 5, pp. 3211-3216, May 2023, doi: 10.1109/TIV.2023.3269428.
From Formula One to Autonomous One: History, Achievements, and Future Perspectives
Abstract
As driving scenarios and autonomous vehicles (AVs) become increasingly intricating, there is an increasing need for innovative frameworks that can enhance and test AV capabilities across diverse scenarios. At present, the design and validation for AVs predominantly rely on simulation software or real-road testing methodologies. However, these approaches possess inherent limitations, leading to inaccuracy, reduced efficiency, and potential hazards during the development process. This letter reports our first DHW (decentralized and hybrid workshop) on Scenarios Engineering (SE), that aims to calibrate and validate AV modules through advanced scenarios. Specifically, the DHW discusses combining SE with existing simulation software and real-road testing strategies, enabling developers to maximize the advantages of each approach while ensuring the safety and efficacy in the development of autonomous vehicles. Our findings demonstrate the benefits of enhancing realism of simulation scenarios in the aspects of both content and appearance. Furthermore, the use of scenarios engineering is explored in order to enhance the diversity and safety of real-road testing by means of integrating virtual and physical components.
Cite
B. Li et al., "From Formula One to Autonomous One: History, Achievements, and Future Perspectives," in IEEE Transactions on Intelligent Vehicles, vol. 8, no. 5, pp. 3217-3223, May 2023, doi: 10.1109/TIV.2023.3269207.
ACP-Based Energy-Efficient Schemes for Sustainable Intelligent Transportation Systems
Abstract
Reducing vehicle exhaust pollution and energy consumption is of great significance for improving the sustainability of social development. Currently, most energy-efficient and regenerative energy recovery methods are from a vehicle control perspective, ignoring the impact on the overall traffic environment. An important reason is that the transportation system's large scale, complexity and social nature restrict its energy-efficient development. Hence, this letter proposes energy-efficient and regenerative energy recovery schemes for sustainable intelligent transportation system using the Artificial societies, Computational experiments, Parallel execution (ACP) framework. The framework includes three parts: energy-efficient oriented intelligent road infrastructure design, transportation traffic flow and vehicle velocity profile planning co-design, and cloud-based vehicle engine parameter calibration. This letter is the second part of Distributed/Decentralized Hybrid Workshop on Sustainability for Transportation and Logistics (DHW-STL) and aims to enhance the sustainability of transportation system from the energy-efficient perspective.
Cite
J. Chen, Y. Zhang, S. Teng, Y. Chen, H. Zhang and F. -Y. Wang, "ACP-Based Energy-Efficient Schemes for Sustainable Intelligent Transportation Systems," in IEEE Transactions on Intelligent Vehicles, vol. 8, no. 5, pp. 3224-3227, May 2023, doi: 10.1109/TIV.2023.3269527.
Regular Paper
Heuristics for Multi-Vehicle Routing Problem Considering Human-Robot Interactions
Abstract
Autonomous mobile robots (AMRs) are being used extensively in civilian and military applications for applications such as underground mining, nuclear plant operations, planetary exploration, intelligence, surveillance and reconnaissance (ISR) missions and manned-unmanned teaming. We consider a multi-objective, multiple-vehicle routing problem in which teams of manned ground vehicles (MGVs) and AMRs are deployed respectively in a leader-follower framework to execute missions with differing requirements for MGVs and AMRs while considering human-robot interactions (HRI). HRI studies highlight the costs of managing a team of follower AMRs by a leader MGV. This paper aims to compute feasible visit sequences, replenishments, team compositions and number of MGV-AMR teams deployed such that the requirements for MGVs and AMRs for the missions are met and the routing, replenishment, HRI and team deployment costs are at minimum. The problem is first modeled as a a mixed-integer linear program (MILP) that can be solved to optimality by off-the-shelf commercial solvers for small-sized instances. For larger instances, a variable neighborhood search algorithm is offered to compute near optimal solutions and address the challenges that arise when solving the combinatorial multi-objective routing optimization problem. Finally, computational experiments that corroborate the effectiveness of the proposed algorithms are presented.
Cite
V. S. Chirala, K. Sundar, S. Venkatachalam, J. M. Smereka and S. Kassoumeh, "Heuristics for Multi-Vehicle Routing Problem Considering Human-Robot Interactions," in IEEE Transactions on Intelligent Vehicles, vol. 8, no. 5, pp. 3228-3238, May 2023, doi: 10.1109/TIV.2023.3261274.
Railway Virtual Coupling: A Survey of Emerging Control Techniques
Abstract
This paper provides a systematic review of emerging control techniques used for railway virtual coupling (VC) studies. Train motion models are first reviewed, including model formulations and force elements involved. Control objectives and typical design constraints are then elaborated. Next, existing VC control techniques are surveyed and classified into five groups: consensus-based control, model prediction control, sliding mode control, machine learning-based control, and constraints-following control. Their advantages and disadvantages for VC applications are also discussed in detail. Furthermore, several future directions for achieving better controller development and better controller implementation are envisioned, respectively. The purposes of this survey are to help researchers to achieve a comprehensive understanding regarding VC control, to spark more research into VC, and to further speed-up the realization of this emerging technology in railway and other relevant fields such as road vehicles.
Cite
Q. Wu, X. Ge, Q. -L. Han and Y. Liu, "Railway Virtual Coupling: A Survey of Emerging Control Techniques," in IEEE Transactions on Intelligent Vehicles, vol. 8, no. 5, pp. 3239-3255, May 2023, doi: 10.1109/TIV.2023.3260851.
Shared Steering Control With Predictive Risk Field Enabled by Digital Twin
Abstract
A core issue of safe human-machine cooperative driving lies in dynamic assessment of the rapidly evolving driving risks. Given that, this paper proposes a shared controller for safe and human-friendly cooperative driving based on predictive risk assessment enabled by Digital Twin technologies. In the digital world, we create a fine-grained digital replica of the driving scene comprising historical motions of vehicles, as well as roadway geometries and topologies. On the basis of that, spatial-temporal interactive features are obtained with a deep learning-based model and subsequently decoded to predict future trajectories of each target vehicle in the neighborhood of the ego-vehicle. In the physical world, the predicted trajectories of neighboring vehicles are integrated into the risk distribution to construct predictive risk fields. A novel shared controller in the framework of multi-objective MPC is designed to minimize the driving risk while matching driver's commands, so that safe cooperative driving is achieved in a smooth and minimal-intervention manner. The results of driver-in-the-loop simulation experiments demonstrate the enabling role of the Digital Twin in improving the assessment of risk in highly dynamic scenes through taking the motion trends of dynamic agents into account. The results also show the superiority of the Digital Twin-based shared controller in terms of implementing cooperation in time while honoring the driver's commands whenever possible.
Cite
Y. Liang, Z. Yin, L. Nie and Y. Ba, "Shared Steering Control With Predictive Risk Field Enabled by Digital Twin," in IEEE Transactions on Intelligent Vehicles, vol. 8, no. 5, pp. 3256-3269, May 2023, doi: 10.1109/TIV.2023.3259970.
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