On-board Unit Research Articles

Path Loss and Auxiliary Communication Analysis of VANET in Tunnel Environments

Vehicular ad hoc network (VANET) communications face severe fading problems due to the signal reflections and diffractions within tunnels. Unlike the open road, the space of a tunnel is very limited, so VANET communication performance in a tunnel is seriously affected. In the process of signal transmission, the reflected signal is symmetrical with the incident signal after it is reflected by the road and the wall. In this paper, we establish a mathematical model of path loss for V2V (Vehicle-to-Vehicle) communication based on the principle of signal reflection symmetry in tunnels and considering several factors, such as the tunnel surface and the color of the tunnel wall. In addition, we use cooperative communication to form a virtual multiple-input multiple-output (V-MIMO) system, to improve the communication quality in tunnels. In the proposed system, the OBU (On-Board-unit) and RSU (Road-Side-Unit) share each other’s antennas, so that wireless cooperative communication can be employed, without increasing the number of antennas in a one-way tunnel. Therefore, this multipath fading internal electromagnetic wave propagation model can be used to improve performance. A deep reinforcement learning algorithm was used to solve the pairing problem to obtain a more accurate OBU and RSU pair, to form a V-MIMO system. Here, the RSU is regarded as an agent and interacts with the OBU in the tunnel. The optimal strategy was learned in a real-time changing simulation environment, and the experiment verified the convergence of the algorithm. The simulation results showed that, compared with the Q-learning based scheme, the optimal matching algorithm based on V-MIMO and a DQN (Deep Q-network) could effectively reduce the probability of transmission outages and improve the communication efficiency in tunnels.

Extended 2-D Magnetic Field Modeling of Linear Motor to Investigate the Magnetic Force Parameters of High-Speed Superconducting Maglev

A 2D numerical finite element model of a linear synchronous motor (LSM) is extended based on the magnetic field and moving mesh properties to investigate the magnetic flux and magnetic force characteristics of the high-speed EML Maglev system, by using HTS coils rather than lower current carrying copper coils on the onboard unit and PMs on the ground. In this 2D LSM model, the transient, time dependent solver is used to obtain the magnetic flux densities. Both the propulsion and levitation forces increased with increasing supercurrent J <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c0</sub> values indicating the advantage of using the superconducting windings in PM-LSM of Maglev vehicles as compared to the copper wires. It was also determined that although the propulsion force is obtained on a comparable level with the studies in literature, the lower levitation force issue than the EDS, despite the better propulsion to levitation force ratio, can be overcame by using the high flux trapping capacity bulk HTSs in the onboard unit together with the superconducting coils. The determined higher propulsion force in this PM-LSM Maglev model indicates that the Maglev vehicle can reach higher velocities in a short-distance and thus this vehicle can be effectively used in short-distance travels in addition to the long-distance transportation. On the other hand, since the EDS system to be accelerated in a certain time interval via conventional wheels to achieve sufficient levitation, PM-LSM system can be integrated into the EDS Maglev system to ensure higher acceleration in a short time interval in addition to the higher levitation and propulsion force performances.

Vision-Based Work Zone Safety Alert System in a Connected Vehicle Environment

Work zones, being a critical component of roadway transportation systems, can benefit greatly from computer vision-enabled roadway infrastructures, specifically in connected vehicle (CV) environments. Connected infrastructures, such as roadside units (RSU) and on-board units (OBU), can greatly improve the environmental awareness and safety of CVs driving through a work zone. The contribution of this paper lies in developing a vision-based approach to generate work zone safety messages in real time, utilizing video streams from roadside monocular traffic cameras that can be used by CV work zone safety apps on mobile devices to reliably navigate through a work zone. A monocular traffic camera calibration method is proposed to establish the accurate mapping between the image plane and Global Position System (GPS) space. Real test scenarios show that our algorithm can precisely and effectively locate work zone boundaries from a monocular traffic camera in real time. We demonstrate the capabilities and features of our system through real-world experiments where the driver cannot see the work zone. End-to-end latency analysis reveals that the vision-based work zone safety warning system satisfies the active safety latency requirements. This vision-based work zone safety alert system ensures the safety of both the worker and the driver in a CV environment.

Optimization of RBF-SVM Kernel Using Grid Search Algorithm for DDoS Attack Detection in SDN-Based VANET

The dynamic nature of the vehicular space exposes it to distributed malicious attacks irrespective of the integration of enabling technologies. The Software-Defined Network (SDN) represents one of these enabling technologies, providing an integrated improvement over the traditional VANET. Due to the centralized characteristics of SDN, they are vulnerable to attacks that may result in life-threatening situations. Securing SDN-based VANETs is vital and requires incorporating Artificial Intelligence (AI) techniques. Hence, this work proposed an Intrusion Detection Model (IDM) to identify Distributed Denial of Service (DDoS) attacks in the vehicular space. The proposed solution employs the Radial Basis Function (RBF) kernel of the Support Vector Machine (SVM) classifier and an exhaustive parameter search technique called Grid Search Cross-Validation (GSCV). In this framework, the proposed architecture can be deployed on the On-Board Units (OBUs) of each vehicle, which receive the vehicular data and run intrusion detection tasks to classify a message sequence as a DDoS attack or benign. The performance of the proposed algorithm compared to other ML algorithms using key performance metrics. The proposed framework is validated through experimental simulations to demonstrate its effectiveness in detecting DDoS intrusion. Using the GridSearchCV, optimal values of the RBF-SVM kernel parameters &#x201C;C&#x201D; and &#x201C;gamma&#x201D; (&#x03B3;) of 100 and 0.1 respectively gave the optimal performance. The proposed scheme showed an overall accuracy of 99.33%, a detection rate of 99.22%, and an average squared error of 0.007, outperforming existing benchmarks.

VERCO: A privacy and data-centric architecture for verifiable cooperative maneuvers

Cooperative, Connected and Automated Mobility (CCAM) applications, enabled by vehicular communications and vehicle automation technologies, are set to increase traffic safety and efficiency. An important feature of CCAM is the potential to decide and coordinate maneuvers among automated vehicles in a more efficient and secure manner, when compared to human drivers. However, maneuver decision and coordination is a rather complex topic due to the multitude of possible maneuvers and the occasional cooperation failures, as caused by issues derived from the use of wireless communications in the vehicular ecosystem, like packet loss or even misbehaving entities. This work focuses on the latter problem by introducing a secure communication design for the decision of general cooperative maneuvers and the distributed storage of related data for accountability purposes using mechanisms based on distributed ledger technologies, while meeting user and data privacy requirements. We present the VERCO (VERifiable COoperation), a scalable geographical-based multi-layered blockchain architecture able to support the high volume of shared vehicular data in order to enhance the security and accountability of cooperative maneuvers, as well as potentially being able to support other vehicular services. To support this architecture, we also provide a performant communication protocol for the decision and negotiation of cooperative maneuvers, based on a new message: the Verifiable Cooperation Message (VCM). The architecture and communication protocol are analyzed and tested employing hardware-in-the-loop (HiL) simulations using two ETSI ITS standard compliant on-board units (OBUs) while deciding maneuvers in a lane-merging scenario. To test the scalability of the architecture a roadside unit (RSU) is put under different stress loads using a variable number of simulated vehicles in the same lane-merging scenario. Experimental results show the feasibility of the communication protocol, with an average delay of 34.68 ms, and the lightweightness of the proposed architecture, with an average overhead of only 2.62 ms for two vehicles and less than 6 ms for dozens of vehicles.

Ethernet Over Coaxial (EoC) Cable Telemetry Over High Voltage Dc And High Power Ac For Airborne Sonar Applications

&#x0D; &#x0D; &#x0D; We have proposed a new passive component-based coupling scheme to simultaneously transmit high power AC, high voltage DC and high speed data through a long, single core coaxial cable, particularly for Airborne Sonar applications. This method is intended to replace the bulky multicore cables with single core coaxial cables that are compact and effective for transmitting data over long distances. This coupling scheme consists of three couplers to superimpose high power AC, high voltage DC and high speed data at the onboard end of the cable, and three decouplers to separate them at the remote end. The long cable, couplers, decouplers and the acoustic transducer are represented by their corresponding equivalent circuits that are cascaded together to construct the complete network. Power loss in the circuit is minimised by providing impedance matching networks in the form of a T-network transmission line and a tuning coil. The Ethernet over Coaxial (EoC) module can transmit high speed Ethernet data at the rate of 10/100 Mbps for telemetry from onboard to remote units and vice versa. DC voltages up to 300 V are coupled to the single core coaxial cable from the onboard unit and superimposed with high power AC signals and high-speed data. Passive filter-based coupling and decoupling schemes are demonstrated. Network simulation studies and experimental studies are carried out to verify and validate the equivalent circuit model. The transfer functions of each set of couplers and decouplers are determined independently as well as collectively and their effects on the underwater acoustic performance of the system are studied. The model resuls are found to agree with experiments. The proposed system is capable of generating an acoustic Source Level of about 195 dB with the supply of 125 V continuous wave (CW) AC signals, with simultaneous transmission of data at 10 Mbps and DC supply of 260 V from the on-board unit to the remote unit at the end of a coaxial cable of 200 m length. Introduction of impedance matching network is found to increase the source level by about 12 dB.&#x0D; &#x0D; &#x0D;

Signature Split Method for a PQC-DSA Compliant with V2V Communication Standards

The development of quantum computing systems poses a great threat to the security of existing public key-based systems. As a result, the National Institute of Standards and Technology (NIST) started a Post-Quantum Cryptography (PQC) standardization project in 2015, and currently active research is being conducted to apply PQC to various cryptographic protocols. Unlike elliptic curve cryptography (ECC)-based schemes, PQC requires a large memory footprint and key/signature size. Therefore, when migrating PQC to a protocol, depending on the PQC and protocol specifications, it can be hard to migrate PQC. In the case of the WAVE protocol, it is difficult to satisfy the accuracy of a specific PQC algorithm because segmentation of the signature occurs during transmission due to the limitation of the maximum packet size. Therefore, in this paper, we present two methodologies that can apply PQC while complying with IEEE 1609.2 standards to the WAVE protocol in the V2V environment. Whereas previous migration studies have focused on designing a hybrid mode of protocols, this paper explores solutions more intuitively at the application layer of protocols. We analyzed two postquantum digital signature algorithms (Crystals-Dilithium and Falcon) and the structure of basic-safety messages (BSMs) of the V2V protocol on the size side. Through this, we propose methods that can perform an independent signature verification process without waiting for all divided signatures in the WAVE protocol. Our methodology overcomes the limitation that schemes with large signature sizes cannot be mounted into the WAVE protocol. We also note that the architecture used as an on-board unit (OBU) in an autonomous driving environment is mainly a microprocessor. We investigated an optimized PQC implementation in the OBU environment and simulated our methodology with the V2Verifier. Finally, we measured the accurate latency through simulation in Jetson Xavier, which is mainly used as an OBU in the V2V communication network.

A secure access control scheme with batch verification for VANETs

When an onboard unit (OBU) moves into the area of a roadside unit (RSU), authentication occurs. In areas with heavy traffic, e.g., intersections, an RSU may receive thousands of requests in a short period . Verifying each request separately can lead to wasted resources and cause RSU service bottlenecks. Because these requests have stringent real-time requirements, if they are not processed in time, vehicles may not be able to change their driving status and could encounter large-scale congestion or even dangerous areas. To improve efficiency, this study investigated a secure access control scheme with batch verification for vehicular ad hoc networks . The proposed method requires only two bilinear pairing operations to verify n requests, which is the same number for a single verification. Through a performance analysis, it was confirmed that the proposed scheme has a lower computation cost and the least communication overhead among existing schemes. Furthermore, extensive simulations demonstrated that the scheme has the lowest processing delay, and as the number of batch verifications increases, its processing delay growth range also diminishes. A detailed security analysis shows that the scheme achieves collusion resistance, signature unforgeability, and replaying resistance.

Magnetic Force Performance of Hybrid Multisurface HTS Maglev System With Auxiliary Onboard PMs

The vertical levitation force, guidance force, and magnetic stiffness values, and thus the loading capacity and movement stability of high-temperature superconducting (HTS) Maglev systems, are aimed to be increased in this study by using auxiliary permanent magnets (PMs) in the onboard unit together with the multisurface HTS-permanent magnetic guideway (PMG) arrangement (hybrid multisurface arrangement). First, the magnetic levitation force, guidance force, and stiffness performances of the hybrid multisurface arrangement were investigated at different field cooling heights (FCH). Then, to compensate for the negation of instability that results from the higher repulsive force between the onboard PMs and the PMG and to obtain an optimal magnetic field medium, we have changed the vertical position of the auxiliary onboard PMs (Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PM</sub> ) to Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PM</sub> = 0, 2, and 4 mm, at the cost of a bit of adecrement in the vertical levitation force. The bigger levitation force, together with the guidance force values for FCH = 25 mm and Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PM</sub> = 0 mm, indicates that the hybrid multisurface HTS–PMG arrangements are beneficial to increasing the practical applicability of Maglev systems.

A Lightweight V2R Authentication Protocol Based on PUF and Chebyshev Chaotic Map

&lt;p&gt;Internet of Vehicles (IoV) plays an important role in enhancing the intelligence of social transportation services. However, there are existing such as privacy leakage, computational complexity and low efficiency on V2R authentication protocols. To solve these problems, a lightweight V2R authentication protocol according to physical unclonable function (PUF) and Chebyshev chaotic map is proposed. The lightweight property of PUF in this scheme can solve the resource constraint problem of the On-Board Unit (OBU) effectively. The fuzzy extractor can correct for small variations in PUF response and improve the accuracy of data transmission. Besides, Chebyshev chaotic map with good cryptographic properties establishes a secure session key while achieving mutual authentication of V2R. Finally, simulation results show that the scheme combining PUF, chaotic map, and fuzzy extractor in this paper saves 4.7% to 49% in communication and calculation overhead comparing with existing protocols. In terms of security, our scheme can also meet the requirements well in the V2R authentication protocol for IoV.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

An edge communication based probabilistic caching for transient content distribution in vehicular networks

Vehicular Content Networks (VCNs) represent key empowering solution for content distribution in fully distributed manner for vehicular infotainment applications. In VCN, both on board unit (OBU) of each vehicle and road side units (RSUs) facilitate content caching to support timely content delivery for moving vehicles when requested. However, due to limited caching capacity available at both RSUs and OBUs, only selected content can be cached. Moreover, the contents being demanded in vehicular infotainment applications are transient in nature. The transient content caching in vehicular content networks with the use of edge communication for delay free services is fundamental issue and need to get addressed (Yang et al. in ICC 2022-IEEE international conference on communications. IEEE, pp 1–6, 2022). Therefore, this study focuses on edge communication in VCNs by firstly organizing a region based classification for vehicular network components including RSUs and OBUs. Secondly, a theoretical model is designed for each vehicle to decide its content fetching location (i.e. either RSU or OBU) in current region or neighboring region. Further, the caching of transient contents inside vehicular network components (such as RSU, OBU) is based on content caching probability. Finally, the proposed scheme is evaluated under different network condition in Icarus simulator for various performance parameters. The simulation results proved outstanding performance of the proposed approach over various state of art caching strategies.

A Smart Road Side Unit in a Microeolic Box to Provide Edge Computing for Vehicular Applications

Deployment of technologies for Intelligent Transportation Systems (ITS) involves the installation of Road Side Units (RSU) on the roadway and On-Board Units (OBU) inside vehicles. In this direction, 5G technologies will make a great impulse providing low latency communication and computation at the edge of the network. First, this paper defines VMEC-in-a-Box, a smart RSU combined with a MEC station, aimed at providing edge computing for vehicular applications by enabling job offloading from vehicles. VMEC-in-a-Box is equipped with a microeolic power generator to be autonomous and self-consistent even in presence of low levels of wind. The behavior of VMEC-in-a-Box is controlled by artificial intelligence to vary its computing capacity dynamically to pursue the best tradeoff between performance and power consumption, and to cooperate by offloading jobs to each other (horizontal offload) to improve performance and reliability of the system. Hence, the paper defines a Markov model to support decisions to optimize by means of Reinforcement Learning the system behavior according to two reward functions defined at the MEC and at the Vehicular Domains. To the best of our knowledge, this is the first work proposing an integrated framework to maximize reliability and performance at both Vehicular and MEC Domains.

DRL-assisted delay optimized task offloading in automotive-industry 5.0 based VECNs

The rapid growth of Automotive-Industry 5.0 and its emergence with beyond fifth-generation (B5G) communications, is making vehicular edge computing networks (VECNs) increasingly complex. The latency constraints of modern automotive applications make it difficult to run complex applications on vehicle on-board units (OBUs). While multi-access edge computing (MEC) can facilitate task offloading to execute these applications, it is still a challenge to access them promptly and optimally. Traditional algorithms struggle to guarantee accuracy in such dynamic environment, but deep reinforcement learning (DRL) methods offer improved accuracy, robustness, and real-time decision-making capabilities. In this paper, we propose a DRL-based mobility, contact, and load aware cooperative task offloading (DCTO) scheme. DCTO is designed for both cellular and mmWave radio access technologies (RATs), and both binary and partial offloading mechanisms. DCTO targets delay minimization by opportunistically switching RATs and offloading mechanisms. We consider relative efficacy and neutrality factors as key performance indicators and use them to derive the DRL agent’s reward function. Extensive evaluations demonstrate that the DCTO scheme exhibits a substantial enhancement in task success rate, with an increase from 2.61% to 21.34%. It also improves the efficacy factor from 1.38 to 3.52 and reduces the neutrality factor from 4.99 to 0.76. Furthermore, the average task processing time is reduced by a range of 3.77% to 24.15%. Additionally, the DCTO scheme outperforms the other evaluated schemes in terms of reward and TFPS ratio.

An authentication approach in SDN-VANET architecture with Rider-Sea Lion optimized neural network for intrusion detection

Vehicular Ad-hoc Network (VANET) offers expedient services in intellectual transportation models but is susceptible to several attacks. The Intrusion detection systems (IDSs) help to prevent security risks by discovering irregular network behaviours under local sub-networks inspite of complete VANET. An optimization-driven model is devised for detecting intrusion in SDN-VANET. The network comprises several entities, like the On-Board Unit (OBU), SDN, Road-Side Unit (RSU), and Authentication Server. The proposed approach involves five phases, namely registration, key generation, data encoding, authentication, and decoding. The authentication of OBU is done with the data packets. The detection of intrusion is performed using the optimized model. The selection of features is done from log files considering the Renyi entropy. The detection of intrusion is done using the Rider-based neural network (RideNN), trained with designed Rider-based Sea Lion Optimization (RBSLO), and is produced by blending the Rider optimization algorithm (ROA) and Sea Lion Optimization (SLnO). The proposed RBSLO-based RideNN offered to improve the achievement with the elevated precision of 92.5%, the recall of 95.4%, the F-measures of 94%, and the smallest computation time of 135.654 s.

Symbiotic Combination of a Bayesian Network and Fuzzy Logic to Quantify the QoS in a VANET: Application in Logistic 4.0

Intelligent transportation systems use new technologies to improve road safety. In them, vehicles have been equipped with wireless communication systems called on-board units (OBUs) to be able to communicate with each other. This type of wireless network refers to vehicular ad hoc networks (VANET). The primary problem in a VANET is the quality of service (QoS) because a small problem in the services can extremely damage both human lives and the economy. From this perspective, this article makes a contribution within the framework of a new conceptual project called the Smart Digital Logistic Services Provider (Smart DLSP). This is intended to give freight vehicles more intelligence in the service of logistics on a global scale. This article proposes a model that combines two approaches—a Bayesian network and fuzzy logic for calculating the QoS in a VANET as a function of multiple criteria—and provides a database that helps determine the originality of the risk of degrading the QoS in the network. The outcome of this approach was employed in an event tree analysis to assess the impact of the system’s security mechanisms.

The Effects of ADAS on Driving Behavior: A Case Study.

The presence of numerous sensors inside modern vehicles leads to the development of new driving assistance tools, the real usefulness of which depends, however, on the environmental context. This study proposes a procedure capable of quantifying the effectiveness of some warnings produced by an On-Board Unit (OBU) inside the vehicle in a specific environmental context, even if limited only to the considered road. The experimentation was carried out by means of a driving simulator with a sample of young users with sufficiently homogeneous characteristics. The collected data were treated by ANOVA to highlight any differentiation between a traditional driving condition, without any instrumental support, and another involving the OBU was present. The results showed that only in relation to the investigated road, the OBU ensured the advantage of sending information of interest to the driver without invalidating their performance in terms of longitudinal and transverse acceleration, speeding, and steering angle. This research could be of interest to the infrastructure managers who, in case of inappropriate use of a road, could intensify active and passive safety devices for users' safety.

Deep Reinforcement Learning for Edge Caching with Mobility Prediction in Vehicular Networks.

As vehicles are connected to the Internet, various services can be provided to users. However, if the requests of vehicle users are concentrated on the remote server, the transmission delay increases, and there is a high possibility that the delay constraint cannot be satisfied. To solve this problem, caching can be performed at a closer proximity to the user which in turn would reduce the latency by distributing requests. The road side unit (RSU) and vehicle can serve as caching nodes by providing storage space closer to users through a mobile edge computing (MEC) server and an on-board unit (OBU), respectively. In this paper, we propose a caching strategy for both RSUs and vehicles with the goal of maximizing the caching node throughput. The vehicles move at a greater speed; thus, if positions of the vehicles are predictable in advance, this helps to determine the location and type of content that has to be cached. By using the temporal and spatial characteristics of vehicles, we adopted a long short-term memory (LSTM) to predict the locations of the vehicles. To respond to time-varying content popularity, a deep deterministic policy gradient (DDPG) was used to determine the size of each piece of content to be stored in the caching nodes. Experiments in various environments have proven that the proposed algorithm performs better when compared to other caching methods in terms of the throughput of caching nodes, delay constraint satisfaction, and update cost.

Parallel implementation of CRYSTALS-Dilithium for effective signing and verification in autonomous driving environment

In the autonomous driving environment, each vehicle performs numerous signing and verification while sending and receiving BSMs (Basic Security Messages) in real-time. We present an optimized CRYSTALS-Dilithium software which can quickly process each signing and verification in parallel using embedded Graphic Processing Unit. For efficiency, we propose several optimization techniques such as dummy operation-based warp divergence reducing technique, parallel implementation NTT (Number Theoretic Transform)-based polynomial multiplication, optimization of rejection sampling process using rejection sequence table, and so on. The proposed CRYSTALS-Dilithium software provides a performance improvement of up to 19.41 times compared to the Dilithium Software on CPUs on NVIDIA Jetson AGX Xavier which is an off-the-shelf autonomous vehicle OBU (On-Board Unit).

Reinforcement learning based scheme for on-demand vehicular fog formation

The high need of real-time vehicular applications for self-driving cars to maintain service availability and reachability, and to process huge amount of generated data within a small amount of time, rise the need to improve the vehicular network infrastructure. Fog computing has been introduced to reduce the amount of data sent to cloud by bringing processing power near the edge and reducing latency. In this paper, we overcome the aforementioned limitations by taking advantage of Istio service mesh and kubeadm clustering to handle inter-service communication and provide clusters to serve nearby vehicles, the evolvement of On-Board Units in vehicle supplies enough processing power to run reinforcement learning model in taking action of pushing microservices to cluster, and the efficiency of Hungarian Algorithm to assign microservices to vehicles. We propose in our scheme (1) a service mesh architecture that manages communication between multiple microservices across different clusters and tackle inter-service communication, (2) a Reinforcement Learning model deployed on road side units to predict on-demand placement of microservices on cluster or caching at RSU, and (3) Reformulating the vehicular container placement (VCP) problem as an assignment problem to map microservices to vehicles. Experiments and simulations show that the reinforcement learning model served 90% of requests when deploying microservices which is more efficient than current strategies in the literature. Given that only needed microservices are deployed in limited resource cluster, mesh network shows an improvement of 84% in the deployment time and inter-service communication across clusters, and the usage of Hungarian algorithm in solving the VCP improves Quality of Service (QoS).

How I Met Your V2X Sensor Data: Analysis of Projection-Based Light Field Visualization for Vehicle-to-Everything Communication Protocols and Use Cases.

The practical usage of V2X communication protocols started emerging in recent years. Data built on sensor information are displayed via onboard units and smart devices. However, perceptually obtaining such data may be counterproductive in terms of visual attention, particularly in the case of safety-related applications. Using the windshield as a display may solve this issue, but switching between 2D information and the 3D reality of traffic may introduce issues of its own. To overcome such difficulties, automotive light field visualization is introduced. In this paper, we investigate the visualization of V2X communication protocols and use cases via projection-based light field technology. Our work is motivated by the abundance of V2X sensor data, the low latency of V2X data transfer, the availability of automotive light field prototypes, the prevalent dominance of non-autonomous and non-remote driving, and the lack of V2X-based light field solutions. As our primary contributions, we provide a comprehensive technological review of light field and V2X communication, a set of recommendations for design and implementation, an extensive discussion and implication analysis, the exploration of utilization based on standardized protocols, and use-case-specific considerations.