To improve forecasting accuracy and capture intricate interactions within transportation networks, information fusion approaches are crucial for traffic predictions based on graph neural networks (GNNs). GNNs offer a potentially effective framework for capturing intricate patterns and interactions among diverse elements, such as road segments and crossings, by considering both temporal and geographical dependencies. Although GNN-based traffic forecasting has recently been investigated in many studies, there is a need for comprehensive reviews that examine information fusion approaches for GNN-based traffic predictions, including an analysis of their benefits and challenges. This study addresses this knowledge gap and offers future insights into the potential advancements and developing fields of research in GNN-based fusion techniques, as well as their implications in urban planning and smart cities. Existing research demonstrates that the accuracy of traffic forecasting is substantially enhanced by information fusion techniques based on GNNs in comparison to more conventional approaches. By integrating information fusion methods with GNNs, the model is capable of capturing intricate temporal and spatial relationships between various locations in a traffic network. Multi-source data integration benefits traffic forecasting models, including social events, weather conditions, real-time traffic sensor data, and historical traffic patterns. In addition, combining GNNs with other AI methods like evolutionary algorithms or reinforcement learning could be an efficient strategy. With the potential to combine the best features of several methods, hybrid models could improve overall performance and flexibility in challenging traffic situations. 

Enhancement of traffic forecasting through graph neural network-based information fusion techniques

Multi-vehicle perception fusion is an emerging advanced vehicular application providing Vehicle Users (VUs) with comprehensive driving assistance. This involves each VU's individual vehicular perception tasks and additional fusion tasks at the end. However, performing these perception fusion applications on some VUs may not be feasible due to the applications' high computing complexity. Vehicular Edge Computing (VEC) server, which is collocated with a Road-side Unit, together with VUs' Vehicular Local Computing (VLC) units can be used to support these perception fusion applications through task offloading. Achieving this edge-collaborated perception fusion with task offloading involves multiple challenges: (i) frequently varying uplink channel conditions, (ii) sequential and parallel task dependencies, and (iii) uncertain task composition induced by unknown object detection outputs in the dynamic driving environment. In this paper, a real-time mechanism is proposed to minimize the end-to-end delay of an edge-collaborated multi-vehicle perception fusion application by addressing these challenges. Based on the real-time channel conditions, VEC and VLC server capacities, and number of detected objects, this algorithm jointly determines the bandwidth allocation, task partitioning, offloading, and execution scheduling for all the involved tasks of the multi-vehicle perception fusion application. Numerical results show that the proposed approach can significantly reduce the end-to-end fusion application latency compared to existing techniques. 

Uncertainty-aware Task Offloading for Multi-vehicle Perception Fusion over Vehicular Edge Computing

Cooperative environmental perception is an effective way to provide connected and autonomous vehicles (CAVs) with the necessary environmental information. The research goal of this paper is to achieve efficient sharing of cooperative environmental perception information. Hence, a novel vehicular edge computing scheme is proposed. In this scheme, the environmental perception tasks are selected to be offloaded based on their shareability, and the edge server directly delivers the task results to the CAVs who need the perception information. The experimental results show that the proposed task offloading scheme can decrease the perception information delivery latency up to 20%. Therefore, it is an effective way to improve cooperative environmental perception efficiency by taking the shareability of the perception information into consideration.

Cooperative Environmental Perception Task Offloading for Connected and Autonomous Vehicles

To further enhance the reliability and throughput of the Vehicle-to-Everything (V2X) communications via New Radio (NR) sidelink, the flexible Modulation and Coding Scheme (MCS) was approved by 3GPP in Release 16. However, most available works neglect the variations in message data size and adopt fixed MCS, which unavoidably limits the NR sidelink spectral efficiency. Using the theory of stochastic geometry, we are able to present a mathematical framework and analytically investigate the impact of MCS selection on the achievable message delivery probability in NR sidelink, following the general message data size in 3GPP. Then, the optimal MCS is selected by enlarging the surplus between the Signal to Interference-plus-Noise Ratio (SINR) measured at the receiver and the SINR Threshold Required for Successful Delivery (TRSD). The numerical results prove that the optimal MCS is extremely beneficial for the V2X message transmission.

Toward Optimizing Delivery Probability in NR Sidelink by MCS Selection

This paper presents Bottom-up Residual vector quantization for learned Image Compression (BRIC). This novel deep learning-based image compression method quantizes latent representations beginning with the lowest resolution and proceeding with residual quantization to the highest resolution. BRIC outperforms the top-down approach from prior work, reducing average bit rates for a given image quality on three vehicular datasets. BRIC is designed with a shallower architecture than prior work, which enables faster encoding/decoding speeds and lower peak memory usage, both critical for practical application. The importance of BRIC is further demonstrated in vehicular networks, where reduced bit rates can double frame delivery rates under challenging channel conditions in a C-V2X network and significantly increase the probability of achieving target frame rates in 5G networks. This bottom-up quantization approach inherently captures a notion of feature importance, making it well-suited for future incorporation into semantic communication systems that utilize unequal error protection during transmission. Overall, BRIC represents an important step towards enabling efficient, reliable, and scalable visual data sharing on the roadway, significantly enhancing the performance of vehicular networks.

BRIC: Bottom-Up Residual Vector Quantization for Learned Image Compression

The current Cellular Vehicle-to-Everything (C-V2X) Sidelink communication protocol provides a low latency interface for sharing short safety messages among Road-Side Units and vehicles. However, while its packets are broadcasted in the channel, the throughput is vulnerable to channel conditions and cannot meet the needs of the emerging connected and autonomous vehicles applications (e.g. vehicular fusion tasks), which require multimodal and multi-source sensor data sharing. In this work, we establish a C-V2X testbed on the campus of the University of California, San Diego, to study the feasibility of using C-V2X Sidelink communications for transmitting sensor data in real-time. We implement an end-to-end RGB sensor data (i.e. camera image frames) transmission mechanism on the C-V2X Sidelink testbed and explore the corresponding Quality of Service (QoS) characteristics under two configurable link-level parameters, the Modulation and Coding Scheme (MCS) and packet size. We then propose a cross-layer predictive and adaptive framework which adjusts, in real-time, the MCS and packet size settings based on side-channel information to optimize the QoS for image frame transmission. The real-world trace-driven emulation shows that the proposed policy improves the average frame goodput performance by 28% compared to fixed configuration policies that are used in current Sidelink communications.

Adaptive C-V2X Sidelink Communications for Vehicular Applications Beyond Safety Messages

Improvements in vehicular perception systems over the last decade have enabled new levels of safety and awareness in modern production vehicles. However, achievable performance of these perception systems is bounded by sensor limitations, such as range, and environmental factors, such as occlusion. Collaborative perception circumvents these limitations by incorporating sensor data from multiple sources to fill in perception gaps experienced by an individual sources’ sensors. This paper explores one important aspect of collaborative perception: simultaneously associating objects detected by multiple individual vehicles with each other. This task is crucial as the inability to perform such object association accurately results in duplicate or missed detections, which can lead to unsafe driving behavior. This work proposes a graph neural network model for this task that achieves an average precision (AP) of 0.882 in a challenging virtual environment consisting of 25 unique, simultaneous, and mobile viewpoints. A simpler real-world scenario with two static viewpoints is also evaluated where the model achieves an AP of 0.998, showing that this model can readily transfer to real-world scenarios as well.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09982615.pdf

Multi-Source Feature Fusion for Object Detection Association in Connected Vehicle Environments

Vehicular networking has seen continued evolution over decades with the recently emerging paradigm of Cellular Vehicle-to-Everything (C-V2X) communications beginning to pick up momentum for adoption on today's roadways. Initial iterations of C-V2X grew from the LTE Device-to-Device framework and targeted application use cases that required the exchange of small packets of information: where a vehicle is, what it is doing, etc. Many of the next generation of use cases require the transfer of sensory data from vehicles to the edge, e.g., tele-driving, cooperative perception, computation offloading, etc. This work evaluates whether today's commercially available vehicular networking solutions can support the higher data rates required to carry this sensory data from vehicles to a Roadside Unit using a C-V2X testbed based on C-V2X Mode 4, which operates autonomously in shared spectrum. It is experimentally shown that C-V2X is capable of carrying the most common form of vehicle sensor data, images, with a frame latency of approximately 50 ms; however, these transmissions are often unreliable due to C-V2X Mode 4’s lack of adaptation capability. To mitigate this, a Reinforcement Learning (RL) problem is proposed that can adapt the transmission parameters of image frames using readily available out of band information in order to achieve a 23.3% relative improvement in effective throughput. Extensions to this RL problem are developed that allow explicit control over a desired risk tolerance, such as the probability of transmitting an image but not receiving it. Using this extension, the developed RL solution learns an adaptive transmission policy that successfully delivers 87.1% of the image frames which are transmitted (a 33.6% absolute improvement) while still maintaining the throughput advantage. Ultimately this work finds that today's commercial vehicular networking solutions are capable of supporting applications that require sensor data sharing by using RL to overcome the limitations of C-V2X Mode 4.

Utilizing Reinforcement Learning for Adaptive Sensor Data Sharing over C-V2X Communications

Electric vehicle (EV) charging couples the operation of power and traffic networks. Specifically, the power network determines the charging price at various locations, while EVs on the traffic network optimize the charging power given the price, acting as price-takers. We model such decision-making processes by a bilevel program, with the power network at the upper-level and the traffic network at the lower-level. However, since the two networks are managed by separate entities and the charging expense term, calculated as the product of charging price and charging demand, is nonlinear. Solving the bilevel program is nontrivial. To overcome these challenges, we derive the charging demand function using multiparametric programming theory. This function establishes a piecewise linear relationship between the charging price and the optimal charging power, enabling the power network operator to manage EV charging power independently while accounting for the coupling between the two networks. With the derived function, we are also able to replace the nonlinear charging expense term with a piecewise quadratic one, thus guaranteeing solution optimality. Our numerical studies demonstrate that different traffic demands can have an impact on charging patterns and the power network can effectively incentivize charging at low-price nodes through price setting.

pdf/optimal-vehicle-charging-in-bilevel-power-traffic-networks-2est57fy.pdf

Optimal Vehicle Charging in Bilevel Power-Traffic Networks via Charging Demand Function

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