[2] Title: Eco-Vehicular Edge Networks for Connected Transportation: A Distributed Multi-Agent Reinforcement Learning Approach

Authors: Md Ferdous Pervej and Shih-Chun Lin

Publisher: 2020 IEEE 92nd Vehicular Technology Conference (VTC2020)

https://ieeexplore.ieee.org/document/9348507

Abstract—This paper introduces an energy-efficient, software defined vehicular edge network for the growing intelligent connected transportation system. A joint user-centric virtual cell formation and resource allocation problem is investigated to bring eco-solutions at the edge. This joint problem aims to combat against the power-hungry edge nodes while maintaining assured reliability and data rate. More specifically, by prioritizing the downlink communication of dynamic eco-routing, highly mobile autonomous vehicles are served with multiple low-powered access points (APs) simultaneously for ubiquitous connectivity and guaranteed reliability of the network. The formulated optimization is exceptionally troublesome to solve within a polynomial time, due to its complicated combinatorial structure. Hence, a distributed multi-agent reinforcement learning (D-MARL) algorithm is proposed for eco-vehicular edges, where multiple agents cooperatively learn to receive the best reward.

[4] Title: CAV Impacts on Traffic Intersection Capacity

Authors: Li Song, and Wei Fan

Publisher: NC-CAV, Report No. TCE2020-03-001, January 2021.

https://www.nccav.com/resources/reports

Abstract—This report provides a comprehensive review of the current state-of-the-art and state-of-the-practice on connected and autonomous vehicle’s technology and its impacts on traffic intersection capacity. This should give a clear picture of connected and automated vehicle (CAV) technology, deployment and market penetration rate prediction of CAVs, traffic flow controls of CAVs, control strategies for CAVs at intersections, intersection capacity analysis methods, and intersection modeling scenarios and parameters used in the existing evaluation studies on the impacts of CAVs at intersections. It summarizes a comprehensive review of the current state-of-the-art and\ state-of-the-practice on studies related to the connected vehicles, automated vehicles, and connected and automated vehicles. Deployment and market penetration rate prediction, traffic flow control strategy for CAVs, intersection control strategy for CAVs, intersection capacity analysis methods (empirical-based and simulation-based methods), and intersection modeling scenarios and parameters of CAVs are introduced and reviewed. This part is intended to provide a fundamental and solid reference to develop control strategies for CAVs at the intersection and conduct effective simulation strategies and impact analysis for future tasks.

[7] Title: A C-V2X Platform Using Transportation Data and Spectrum Aware Slidelink Access.

Authors: C.-H. Lin, S.-C. Lin, C.-Y. Wang, and T. Chase

Publisher: Proceeding of 2021 IEEE International Conference on Systems, Man, And Cybernetics (SMC). https://ieeexplore.ieee.org/document/9659109

Abstract— Intelligent transportation systems and autonomous vehicles are expected to bring new experiences with enhanced efficiency and safety to road users in the near future. However, an efficient and robust vehicular communication system should act as a strong backbone to offer the needed infrastructure connectivity. Deep learning (DL)-based algorithms are widely adopted recently in various vehicular communication applications due to their achieved low latency and fast reconfiguration properties. Yet, collecting actual and sufficient transportation data to train DL-based vehicular communication models is costly and complex. This paper introduces a cellular vehicle-to-everything (C-V2X) verification platform based on an actual traffic simulator and spectrum-aware access. This integrated platform can generate realistic transportation and communication data, benefiting the development and adaptivity of DL-based solutions. Accordingly, vehicular spectrum recognition and management are further investigated to demonstrate the potentials of dynamic slidelink access. Numerical results show that our platform can effectively train and realize DL-based C-V2X algorithms. The developed slidelink communication can adopt different operating bands with remarkable spectrum detection performance, validating its practicality in real-world vehicular environments.

[9] Title: XGBoost: a Tree-based Approach for Traffic Volume Prediction

Authors: Benjamin Lartey, Abdollah Homaifar, Abenezer Girma, Ali Karimoddini and Daniel Opoku

Publisher: Proceeding of 2021 IEEE International Conference on Systems, Man, And Cybernetics (SMC). https://ieeexplore.ieee.org/document/9658959

Abstract— The growth in the transportation sector has led to an enormous increase in the number of vehicles which resulted in serious transportation issues including road congestion. Hence, estimating the number of vehicles on a road enables traffic managers to take appropriate decisions to curb congestion. In this paper, we propose to use an extreme gradient boosting (XGBoost) algorithm to efficiently and accurately predict the hourly traffic volume. We investigated the effectiveness of the proposed method for different scenarios including how well it performs during extreme weather conditions and holidays. We further investigated the effect of ridge and LASSO regularization on the performance of XGBoost. We then propose a new approach for setting the LASSO regularization parameter in terms of the number of observations and predictors. The performance and computational efficiency of the proposed approach is evaluated on data collected from Interstate-94, Minnesota and the result is compared with existing methods. Results show that the proposed method provides a good balance between performance and computational efficiency.

[11] Title: A Computationally Effective Pedestrian Detection using Constrained Fusion with Body Parts for Autonomous Driving

Authors: Muhammad Mobaidul Islam, Abdullah Al Redwan Newaz, Renran Tian, Abdollah Homaifar, Ali Karimoddini

Publisher: Proceeding of 2021 IEEE International Conference on Robotic Computing.

https://ieeexplore.ieee.org/abstract/document/9699953

Abstract— In this work, we address the problem of detecting body parts of pedestrians using the Single Shot Detection (SSD) method. In particular, we consider the occluded pedestrian detection problem in autonomous driving scenarios where the balance performance between accuracy and speed is crucial. While SSD performs reasonably well for the full-body pedestrian detection problem, its performance is not satisfactory for handling occlusion. To achieve a real-time performance along with robust detection, we introduce a simple yet effective body parts based single shot pedestrian detection architecture (BP-SSD). Augmenting the decision layer of BP-SSD with our proposed constraint optimization technique, we demonstrate that BP-SSD can accurately detect pedestrians. We evaluate our method using an autonomous driving dataset. Experimental results show that the proposed BP-SSD along with the proposed constraint optimization technique outperforms the generic SSD based pedestrian detection method.

[13] Title: TULVCAN: Terahertz Ultra-broadband Learning Vehicular Channel-Aware Networking

Authors: C.-H. Lin, S.-C. Lin, and E. Blasch

Publisher: Proc. of IEEE INFOCOM Workshop, Virtual Conference, May 2021.

https://ieeexplore.ieee.org/document/9484613

Abstract— Due to spectrum scarcity and increasing wireless capacity demands, terahertz (THz) communications at 0.1-10THz and the corresponding spectrum characterization have emerged to meet diverse service requirements in future 5G and 6G wireless systems. However, conventional compressed sensing techniques to

reconstruct the original wideband spectrum with under-sampled measurements become inefficient as local spectral correlation is deliberately omitted. Recent works extend communication methods with deep learning-based algorithms but lack strong ties to THz channel properties. This paper introduces novel THz channel-aware spectrum learning solutions that fully disclose the uniqueness of THz channels when performing such ultrabroadband sensing in vehicular environments. Specifically, a joint design of spectrum compression and reconstruction is proposed through a structured sensing matrix and two-phase reconstruction based on high spreading loss and molecular absorption at THz frequencies. An end-to-end learning framework, namely compression and reconstruction network (CRNet), is further developed with the mean-square-error loss function to improve sensing accuracy while significantly reducing computational complexity. Numerical results show that the CRNet solutions outperform the latest generative adversarial network (GAN) realization with a much higher cosine and structure similarity measures, smaller learning errors, and 56n% less required training overheads. This THz Ultra-broadband Learning Vehicular Channel-Aware

Networking (TULVCAN) work successfully achieves effective THz spectrum learning and hence allows frequency-agile access.

[15] Title: GCN-CNVPS: Novel Method for Cooperative Neighboring Vehicle Positioning System Based on Graph Convolution Network

Authors: Chia-Hung Lin, Yo-Hui Fang, Hsin-Yuan Chang, Yu-Chien Lin, Wei-Ho Chung, Shih-Chun Lin, and Ta-Sung Lee

Publisher: IEEE Access, vol. 9, pp. 153429-153441, 2021, doi: 10.1109/ACCESS.2021.3127914.

https://ieeexplore.ieee.org/document/9614197

Abstract— Due to spectrum scarcity and increasing wireless capacity demands, terahertz (THz) communications at 0.1-10THz and the corresponding spectrum characterization have emerged to meet diverse service requirements in future 5G and 6G wireless systems. However, conventional compressed sensing techniques to

reconstruct the original wideband spectrum with under-sampled measurements become inefficient as local spectral correlation is deliberately omitted. Recent works extend communication methods with deep learning-based algorithms but lack strong ties to THz channel properties. This paper introduces novel THz channel-aware spectrum learning solutions that fully disclose the uniqueness of THz channels when performing such ultrabroadband sensing in vehicular environments. Specifically, a joint design of spectrum compression and reconstruction is proposed through a structured sensing matrix and two-phase reconstruction based on high spreading loss and molecular absorption at THz frequencies. An end-to-end learning framework, namely compression and reconstruction network (CRNet), is further developed with the mean-square-error loss function to improve sensing accuracy while significantly reducing computational complexity. Numerical results show that the CRNet solutions outperform the latest generative adversarial network (GAN) realization with a much higher cosine and structure similarity measures, smaller learning errors, and 56n% less required training overheads. This THz Ultra-broadband Learning Vehicular Channel-Aware

Networking (TULVCAN) work successfully achieves effective THz spectrum learning and hence allows frequency-agile access.