Dear All,

I would like to share with you the following news:

• By August 30, IEEE TIV has received 5326 original manuscripts, surpassing last year's total submissions of 4726. Our current average number of submissions per day (SPD) is 22.10.

• The TIV Best Paper Award Committee has decided to add an additional research paper to its 2024 George N. Saridis Best Paper Award of the IEEE TRANSACTIONS ON INTELLIGENT VEHICLES. Therefore, in addition to the traditional one research paper and one survey article, a third research paper has been selected. I have concurred with the decision by the Award Committee, and might suggest a corresponding change in our rule for the future selection process. The key timeline for the Award this year is as follows:

• June 23: Start of the review process

• July 23: End of the review process

• July 30: Notification to the corresponding authors

• September 25: Award ceremony at IEEE ITSC 2024

The 2024 George N. Saridis Best Transactions Paper Awards are presented to the following articles:

Best Research Paper

M. Brossard, A. Barrau and S. Bonnabel, “AI-IMU Dead-Reckoning,” IEEE Transactions on Intelligent Vehicles, vol. 5, no. 4, pp. 585-595, Dec. 2020. (Google Scholar Citations: 261)

Best Survey Paper

Y. Huang, J. Du, Z. Yang, Z. Zhou, L. Zhang and H. Chen, “A Survey on Trajectory-Prediction Methods for Autonomous Driving,” IEEE Transactions on Intelligent Vehicles, vol. 7, no. 3, pp. 652-674, Sept. 2022. (Google Scholar Citations: 337)

Best Research Paper

D. Yang, H. Zhang, E. Yurtsever, K. A. Redmill and Ü. Özgüner, “Predicting Pedestrian Crossing Intention With Feature Fusion and Spatio-Temporal Attention,” IEEE Transactions on Intelligent Vehicles, vol. 7, no. 2, pp. 221-230, June 2022. (Google Scholar Citations: 110)

Congratulations to all authors!

This issue includes 1 perspective, 1 letter and 5 regular papers.

After scanning the issue, I would like to have a conversation on the autonomous right of way for intelligent vehicles (IVs).

Perspective

R. Zhou, et al., “Large Models Defining a New Era in Intelligence Testing: A Short Discussion and Exploration,” IEEE Transactions on Intelligent Vehicles, vol. 9, no. 8, pp. 5247–5249, Aug. 2024.

Communication and Letters

H. Huang, et al., “The The development of autonomousPotential of Low-Altitude Airspace: The Future of Urban Air Transportation,” IEEE Transactions on Intelligent Vehicles, vol. 9, no. 8, pp. 5250–5254, Aug. 2024.

Regular Papers

Im et al. [A1] propose a handcrafted method-based efficient feature extraction method for place recognition and point registration that aims to overcome these challenges while achieving high performance. The proposed method involves extracting robust feature points, generating powerful descriptors, and achieving performance levels similar to deep learning methods while ensuring algorithmic versatility. Validation results using the KITTI dataset are also included in the paper, demonstrating exceptional performance even in demanding scenarios involving rotated loops. The results show an average loop closure accuracy of 95% or better, and an average pose estimation accuracy of 0.1 meters for translation and 0.15 degrees for rotation.

Ha et al. [A2] propose an extremum-seeking-based algorithm using fuzzy logic for maximizing the braking friction force in anti-lock brake systems (ABSs) without relying on vehicle speed information and slip ratio dynamics. Many current ABSs algorithms utilize slip ratio as a control parameter. If the slip ratio is inaccurately measured, the braking performance of the ABSs may not be optimal. To address this, they propose a method to achieve maximum friction force by designing a reference generator that generates the control inputs for the ABSs without requiring slip ratio information. They design an extended state observer that can estimate the braking friction force and braking friction coefficient. Based on the estimated friction force, the desired wheel cylinder pressure (WCP), which is the control input of the hydraulic brake system, is generated to converge to the maximum friction force using the extremum seeking control algorithm.

Ma et al. [A3] propose a trigger-based layered compliance decision-making framework. This framework utilizes the decision intent at the highest level as a signal to activate an online violation monitor that identifies the type of violation committed by the vehicle. Then, a four-layer architecture for compliance decision-making is employed to generate compliant trajectories. Using this system, autonomous vehicles can detect and correct potential violations in real-time, thereby enhancing safety and building public confidence in autonomous driving technology. Finally, the proposed method is evaluated on the DJI AD4CHE highway dataset under four typical highway scenarios: speed limit, following distance, overtaking, and lane-changing.

Yan et al. [A4] focus on a binocular-vision-based motion planning issue for an AUV. First, they develop an intelligent AUV system that mainly comprises binocular cameras for patrolling the target, a localization unit for acquiring the position information, and an acoustic modem for communicating with buoys. Accordingly, the parallax angles from the AUV to the target are used to construct an optimal motion planning problem. To solve the aforementioned problem, they develop a deep reinforcement learning method called the improved twin delayed deep deterministic (TD3) policy gradient algorithm in order to minimize the reward function, such that the AUV can perpendicularly patrol the target with a fixed distance. The advantages of our solution are as follows: 1) the binocular-vision-based motion planning method can achieve a trade-off between motion stability and observation effectiveness; 2) the improved TD3 algorithm can accelerate the convergence compared to other algorithms, while it can simultaneously overcome the dependency on the model parameters of the AUV. Finally, simulation and experimental studies are conducted to verify the effectiveness.

Piperigkos et al. [A5] propose two novel and distributed Cooperative Localization or Awareness algorithms, based on linear least-squares minimization and the celebrated Kalman Filter. They both aim to improve ego vehicle's 4D situational awareness, so as to be fully location aware of its surroundings and not just its own position. For that purpose, the ego vehicle forms a star-like topology with its neighbors and fuses four types of multi-modal inter-vehicular measurements (position, distance, azimuth, and inclination angle) via the linear Graph Laplacian operator and geometry capturing differential coordinates. Moreover, a data association strategy has been integrated to the algorithms as part of the identification process, which is shown to be much more beneficial than traditional Hungarian algorithm. An extensive experimental study has been conducted in CARLA, SUMO, and Artery simulators, highlighting the benefits of the proposed methods in a variety of experimental scenarios, and verifying increased situational awareness ability.

The rapid evolution of Intelligent Vehicles (IVs) and their integration into modern traffic systems necessitates a rethinking of traditional traffic governance, particularly in the allocation of the right of way. Historically, the concept of right of way has been a cornerstone of traffic management, ensuring orderly and safe passage on roads [1] [2]. However, the emergence of IVs, equipped with advanced sensing, communication, and motion planning capabilities, presents both challenges and opportunities in redefining this concept. I would like to explore the development of autonomous right-of-way systems as part of broader smart governance for smart mobility, focusing on the role of IVs in this transformation.

Evolution of Right of Way

Traditionally, the right of way was determined by a combination of legal rules, cultural norms, and physical infrastructure, such as road signs and signals [3]. These systems were designed for human drivers, who relied on visual cues and standardized laws to navigate intersections, merges, and pedestrian crossings. Over time, as traffic volumes increased and urban environments became more complex, the need for more sophisticated and adaptable traffic management systems became evident. The introduction of traffic signals, stop signs, and roundabouts marked significant advances in managing the flow of vehicles and ensuring safety.

Despite these advancements, several challenges remain in modern traffic systems. These include inconsistent enforcement of traffic laws, cultural differences in driving behavior, and the growing complexity of urban infrastructure [4]. Additionally, the advent of IVs introduces new variables into the equation, necessitating a reevaluation of how the right-of-way is allocated.

IVs, with their ability to communicate with each other and with traffic infrastructure, have the potential to revolutionize the allocation of right-of-way. Unlike traditional vehicles, which rely on human judgment and fixed signals, IVs can dynamically negotiate right-of-way based on real-time traffic conditions, the presence of pedestrians, and the operational status of nearby vehicles [5].

One of the key advancements in this area is Vehicle-to-Everything (V2X) communication, which enables IVs to exchange information with traffic signals, road signs, and other vehicles. This technology allows for more fluid and adaptive traffic management, where right-of-way can be allocated dynamically, rather than through fixed signals or pre-established rules. For example, at intersections, IVs can negotiate with each other to determine the optimal sequence for crossing, reducing delays and improving safety.

Moreover, the integration of artificial intelligence and machine learning into traffic management systems enables predictive analysis of traffic flow, which can further optimize the allocation of right-of-way. These systems can anticipate potential congestion or hazards and adjust traffic signals or vehicle behavior accordingly, enhancing overall traffic efficiency and safety.

Smart Governance for Smart Mobility

Smart governance for smart mobility represents an integrated approach to managing urban transportation systems by leveraging advanced technologies, data analytics, and collaborative motion planning [6]. Governance extends beyond the traditional roles of traffic management to include the coordination of various mobility services, the engagement of multiple stakeholders, and the alignment of transportation policies with broader urban development goals.

The concept of smart governance is rooted in the principles of transparency, inclusivity, and sustainability[7]. By utilizing real-time data from IVs, traffic sensors, and public transportation networks, smart governance frameworks can facilitate more informed and responsive decision-making. This allows cities to better manage traffic flows, reduce congestion, and improve the overall efficiency of the transportation network.

One of the central tenets of smart governance is the seamless integration of various mobility services, including public transportation, ride-sharing, and autonomous vehicles. This integration ensures that all forms of transportation work together harmoniously, providing users with a cohesive and efficient mobility experience. For example, smart governance can enable dynamic pricing and routing of public transit options based on real-time demand, thus optimizing the use of infrastructure and resources.

Furthermore, smart governance promotes the involvement of citizens in the decision-making process, ensuring that transportation policies reflect the needs and preferences of the public [8]. This participatory approach not only enhances the legitimacy of governance decisions but also fosters greater trust between citizens and government authorities.

In the context of autonomous right-of-way systems, smart governance plays a crucial role in setting the rules and standards that guide the behavior of IVs on the road. It ensures that these systems are designed to prioritize safety, equity, and environmental sustainability. For instance, smart governance can mandate that IVs give precedence to vulnerable road users, such as pedestrians and cyclists, or that they adhere to specific emissions standards to minimize environmental impact.

Challenges and Considerations

While the potential benefits of autonomous right-of-way systems are significant, several challenges must be addressed to realize their full potential. First, the transition from traditional traffic management to autonomous systems will require significant changes in infrastructure, including the deployment of V2X communication networks and the upgrading of traffic signals to support real-time data exchange with IVs [9].

Additionally, the legal and regulatory framework for traffic management will need to be updated to accommodate the unique characteristics of IVs. This includes redefining right-of-way laws to account for the capabilities of IVs and ensuring that these vehicles can operate safely and effectively in mixed-traffic environments, where human-driven and autonomous vehicles coexist.

There are also concerns related to privacy and data security, as autonomous right-of-way systems will rely heavily on the collection and sharing of real-time traffic data. Ensuring that this data is protected from unauthorized access and that the privacy of road users is maintained will be critical to the successful implementation of these systems [10].

Furthermore, the social implications of autonomous right-of-way systems must be considered. The shift from human-controlled traffic to machine-controlled systems may lead to changes in the perceived autonomy of drivers and pedestrians. Ensuring that these systems are transparent and that road users can trust them will be essential for widespread adoption.

Conclusion

The development of autonomous right-of-way systems, facilitated by IVs and advanced communication technologies, represents a significant step forward in the evolution of traffic management. By enabling dynamic and adaptive allocation of right-of-way, these systems promise to improve traffic flow, enhance safety, and reduce congestion in increasingly complex urban environments. However, realizing these benefits will require careful consideration of the technical, legal, and social challenges associated with this transformation. As we move towards a future of smart mobility, the integration of autonomous right-of-way systems into a broader smart governance framework will be key to ensuring that our roads remain safe, efficient, and accessible for all users. Smart governance, with its focus on data-driven decision-making and stakeholder engagement, will be the backbone of this transformation, guiding the evolution of our transportation systems toward greater sustainability, inclusivity, and efficiency.

The above content was produced with the assistance of Retrieval-Augmented Generation (RAG), through three steps: Prompting, Prescription, and Alignment. We believe that with the further development of Artificial Intelligence and Large Language Models, three new types of engineers will emerge and be needed in the future: Prompting Engineers, Prescription Engineers, and Alignment Engineers. We would like to start to try them first here at IEEE TIV.

At IEEE TIV we will continue to organize DHW/DHS on various issues in ITS and IVs.

Welcome to participate in our investigations online or offline. Our discussions will be summarized and reported as perspectives, letters, or regular papers at IEEE TIV. The following DHWs and their chairs have been organized so far:

1. Verification and Validation for IVs (V&V4IV) Chair: Javier Ibanez-Guzman, Renault

2. Autonomous Mining (AM) Chair: Long Chen, Waytous

3. Ethics, Responsibility, and Sustainability (ERS) Chair: Hui Zhang, Beihang University

4. Intelligent Vehicles for Education (IV4E) Chair: Bai Li, Hunan University

5. Data Science for Intelligent Vehicles (DSiV) Chair: Junping Zhang, Fudan University

6. Vehicle 5.0 (V5) Chair: Ljubo Vlacic, Griffith University

7. Scenarios Engineering for Smart Mobility (SE4SM) Chair: Xuan Li, Pengcheng Laboratory

8. CrowdSensing Intelligence (CSI) Chair: Bin Chen, National University of Defense Technology

9. Sustainability for Transportation and Logistics (STL) Chair: David Cebon, Cambridge University

10. Autonomous Services (AS) Chair: Lefei Li, Tsinghua University

11. Foundation/Infrastruce Intelligence (FII) Chair: Jiaqi Ma, University of California, Los Angeles

12. HomePorts and SLAM (HPS) Chair: Xiaoxiang Na, Cambridge University

13. ITS for Sustainability Industry (ITS4SI) Chair: David Gao, University of Denver

14. Federated Intelligence for Intelligent Vehicles (FIIV) Chair: Weishan Zhang, China University of Petroleum

15. Intelligent Vehicles for Social Transportation (IVST) Chair: Xiaofeng Jia, University of Maryland

Any suggestions or proposals for future topics of DHW/DHS are greatly appreciated. Looking forward to having you in IEEE TIV DHW/DHS.

Our review guideline for EIC/SE/AE is “3323”, specified as below:

• 3 weeks for the first decision

• 3 rounds of revision in maximum

• 2 weeks for minor revisions

• 3 weeks for major revisions

Under this guideline, we expect a maximum total 15-week review process for a submission.

1. If the Associate Editor (AE) cannot find suitable reviewers within 3 weeks, the manuscript will be rejected through EIR and returned to the corresponding author, allowing the manuscript to be submitted to other more suitable journals.

In the case of resubmission to TIV, please provide the previous submission ID, and we will attempt to restart the process with a different AE.

1. If the AE cannot obtain two review reports within 6 weeks, the manuscript will be rejected through EIR and returned to the corresponding author, allowing the manuscript to be submitted to other more suitable journals.

In the case of resubmission to TIV, please provide the previous submission ID and a detailed revision report (if any); we will attempt to restart the process with the same AE/Reviewer and additional new reviewers.

Appendix

Related Articles

1. J. -U. Im, S. -W. Ki and J. -H. Won, “Omni point: 3D LiDAR-based feature extraction method for place recognition and point registration,” IEEE Trans. Intell. Vehicles, vol. 9, no. 8, pp. 5255–5271, Aug. 2024, doi: 10.1109/TIV.2023.3348525.

2. J. Ha, S. You, Y. -j. Ko and W. Kim, “Extremum seeking-based braking friction force maximization algorithm using fuzzy logic without slip ratio for ABSs,” IEEE Trans. Intell. Vehicles, vol. 9, no. 8, pp. 5272–5283, Aug. 2024, doi: 10.1109/TIV.2024.3430816.

3. X. Ma et al., “Legal decision-making for highway automated driving,” IEEE Trans. Intell. Vehicles, vol. 9, no. 8, pp. 5284–5298, Aug. 2024, doi: 10.1109/TIV.2023.3318214.

4. J. Yan, K. You, W. Cao, X. Yang and X. Guan, “Binocular vision-based motion planning of an AUV: A deep reinforcement learning approach,” IEEE Trans. Intell. Vehicles, vol. 9, no. 8, pp. 5299–5315, Aug. 2024, doi: 10.1109/TIV.2023.3321884.

5. N. Piperigkos, C. Anagnostopoulos, A. S. Lalos and K. Berberidis, “Extending online 4D situational awareness in connected and automated vehicles,” IEEE Trans. Intell. Vehicles, vol. 9, no. 8, pp. 5316–5335, Aug. 2024, doi: 10.1109/TIV.2023.3335605.