The integration of static geospatial datasets and real-time IoT streams is essential for responsive and scalable urban Digital Twins (DTs). However, current infrastructures remain fragmented across domains, formats, and reference systems, limiting interoperability and city-scale deployment. This paper presents the first city-scale implementation of a Spatial ID–driven edge–cloud architecture that unifies heterogeneous static and dynamic urban data under a hierarchical four-dimensional identifier. Unlike prior DT systems that rely on ad hoc tiling or local schemas, our design operationalizes Spatial ID as a universal indexing layer across batch and streaming pipelines, enabling multi-resolution queries, real-time synchronization, and cross-domain interoperability. A prototype deployment in Tokyo’s Chiyoda and Bunkyo wards demonstrates the approach, integrating 3D city models with live IoT streams. Evaluation shows millisecond-to-second query performance over 148 million records, sub-100 ms vector tile delivery, and real-time IoT stream processing at 30 fps. These results establish Spatial ID not only as a conceptual framework but as a practical, deployable foundation for interoperable, low-latency, and scalable DT infrastructures aligned with the vision of Society 5.0.

Accurate localization in GPS-denied environments is vital for ITS and vehicular networks. While Root-MUSIC offers high accuracy in clean LoS conditions, FBSS enhances robustness under multipath and interference. To overcome the limitations of fixed estimators, this paper proposes an adaptive DOA-based localization framework that selects Root-MUSIC+UKF in low-interference regimes and FBSS-Root-MUSIC+UKF when interference is strong. Simulations show that the proposed method consistently reduces positioning error across varying SNR and interference levels, providing reliable GPS-free vehicle localization in non-stationary wireless environments.

Pedestrian–automated vehicle (AV) encounters in shared spaces often involve hesitation and ambiguity. Vehicle-mounted external human–machine interfaces (eHMIs) can help, but obscured or poorly timed communications create significant challenges. To address this, we present a mobile smart pole interaction unit (SPIU) with integrated cameras and LED displays, designed as a pedestrian-side system to deliver explicit cues (“WALK,” “STOP”). An in-the-wild evaluation of the SPIU (N = 21) using a four-factor analysis (CarBehavior, Mobility, eHMI, SPIU) showed that the SPIU improved understandability, trust, and perceived safety, and reduced workload compared with the baseline, with a combination (eHMI+SPIU) yielding the strongest results. Beyond these quantitative benefits, participants appreciated the mobility of the SPIU for its “clear” and “easy to decide” mediation. This work contributes to (1) a design and deployment framework for a mobile SPIU and (2) an in-the-wild evaluation protocol for pedestrian–AV interactions in nonsignalized spaces. Our work sparks discussions on real world evaluations involving detailed vehicle kinematics and accessible multimodality (e.g., audio), focusing on the role of personal robots as user-side eHMIs.

Field studies are irreplaceable but costly, time-consuming, and error-prone, which need careful preparation. Inspired by rapid-prototyping in manufacturing, we propose a fast, low-cost evaluation method using Vision-Language Model (VLM) personas to simulate outcomes comparable to field results. While LLMs show human-like reasoning and language capabilities, autonomous vehicle (AV)-pedestrian interaction requires spatial awareness, emotional empathy, and behavioral generation. This raises our research question: To what extent can VLM personas mimic human responses in field studies? We conducted parallel studies: 1) one real-world study with 20 participants, and 2) one video-study using 20 VLM personas, both on a street-crossing task. We compared their responses and interviewed five HCI researchers on potential applications. Results show that VLM personas mimic human response patterns (e.g., average crossing times of 5.25 s vs. 5.07 s) lack the behavioral variability and depth. They show promise for formative studies, field study preparation, and human data augmentation.

Timely channel conditions are essential for vehicles to determine which base station (BS) to connect to, but acquiring them in mmWave vehicular networks is costly. Without additional channel estimations, the proposed asynchronous contextual kernelized upper confidence bound (ACK-UCB) algorithm estimates the current instantaneous transmission rates based on the historical transmission rates and contexts, such as the vehicle’s historical locations, velocities, and numbers of concurrent transmissions at the BS. ACK-UCB captures the nonlinear relationship between context and transmission rate, mapping the context into a reproducing kernel Hilbert space (RKHS), where a linear relationship becomes observable. To enhance estimation accuracy, a novel kernel function incorporating mmWave signal propagation characteristics is introduced in RKHS, allowing for a more precise evaluation of context similarity in relation to transmission rates. Furthermore, ACK-UCB encourages vehicles to share only reward distribution features after sufficient explorations, accelerating the learning process while keeping communication costs manageable. Numerical results show that ACK-UCB achieves 99.5%–100.5% network throughput and reduces 89%–91% communication cost of a benchmark algorithm that directly shares all local historical contexts and transmission rates, demonstrating the sharing efficiency of the ACK-UCB algorithm.

The importance of digital twins in 6G is rapidly increasing. However, while digital twins have been discussed in various contexts, defining their specific role within 6G remains crucial. This paper proposes a wireless digital twin (WDT) platform designed to ensure communication stability for connected autonomous vehicles (CAVs) integrated into networks. The proposed WDT reproduces 3D urban environments, calculates radio wave propagation using traffic simulators and ray tracing, and tracks CAV movement. Additionally, we discuss methods for evaluating multiple communication systems using proposed WDT, aiming to promote research and development toward 6G.

With the advent of sixth-generation (6G) technologies and growing communication demands, Low Earth Orbit (LEO) satellite networks have become essential in modern communications. However, due to the dynamic topology and complex network state of LEO environments, existing routing methods often fail to make effective decisions, limiting transmission performance. This paper proposes a LEO satellite routing method based on incremental evolutionary graph reinforcement learning (IEGRL). To address network state perception challenges, we introduce a topological learning model using deep graph attention (DGA), which captures complex inter-satellite connectivity and resource states. Additionally, by integrating incremental evolution strategies (IES) into deep reinforcement learning (DRL), we replace sequential interactive proximal policy optimization (PPO) with global parallel ES, achieving efficient routing convergence in the highly dynamic LEO environment. Experimental results demonstrate that our IEGRL approach enhances LEO network load balancing by reducing end-to-end (E2E) network latency, decreasing packet loss, and improving throughput compared with the benchmark approaches.

This paper introduces Multi-PrefDrive, a framework that significantly enhances LLM-based autonomous driving through multidimensional preference tuning. Aligning LLMs with human driving preferences is crucial yet challenging, as driving scenarios involve complex decisions where multiple incorrect actions can correspond to a single correct choice. Traditional binary preference tuning fails to capture this complexity. Our approach pairs each chosen action with multiple rejected alternatives, better reflecting real-world driving decisions. By implementing the Plackett-Luce preference model, we enable nuanced ranking of actions across the spectrum of possible errors. Experiments in the CARLA simulator demonstrate that our algorithm achieves an 11.0% improvement in overall score and an 83.6% reduction in infrastructure collisions, while showing perfect compliance with traffic signals in certain environments. Comparative analysis against DPO and its variants reveals that Multi-PrefDrive’s superior discrimination between chosen and rejected actions, which achieving a margin value of 25, and such ability has been directly translates to enhanced driving performance. We implement memory-efficient techniques including LoRA and 4-bit quantization to enable deployment on consumer-grade hardware and will open-source our training code and multi-rejected dataset to advance research in LLM-based autonomous driving systems. Project Page (https://liyun0607.github.io/).

The deployment of roadside LiDAR sensors plays a crucial role in the development of Cooperative Intelligent Transport Systems (C-ITS). However, the high cost of LiDAR sensors necessitates efficient placement strategies to maximize detection performance. Traditional roadside LiDAR deployment methods rely on expert insight, making them time-consuming. Automating this process, however, demands extensive computation, as it requires not only visibility evaluation but also assessing detection performance across different LiDAR placements. To address this challenge, we propose a fast surrogate metric, the Entropy-Guided Visibility Score (EGVS), based on information gain to evaluate object detection performance in roadside LiDAR configurations. EGVS leverages Traffic Probabilistic Occupancy Grids (TPOG) to prioritize critical areas and employs entropy-based calculations to quantify the information captured by LiDAR beams. This eliminates the need for direct detection performance evaluation, which typically requires extensive labeling and computational resources. By integrating EGVS into the optimization process, we significantly accelerate the search for optimal LiDAR configurations. Experimental results using the AWSIM simulator demonstrate that EGVS strongly correlates with Average Precision (AP) scores and effectively predicts object detection performance. This approach offers a computationally efficient solution for roadside LiDAR deployment, facilitating scalable smart infrastructure development.

This paper presents PrefDrive, a novel frame-work that integrates driving preferences into autonomous driving models through large language models (LLMs). While recent advances in LLMs have shown promise in autonomous driving, existing approaches often struggle to align with specific driving behaviors (e.g., maintaining safe distances, smooth acceleration patterns) and operational requirements (e.g., traffic rule compliance, route adherence). We address this challenge by developing a preference learning framework that combines multimodal perception with natural language understanding. Our approach leverages Direct Preference Optimization (DPO) to fine-tune LLMs efficiently on consumer-grade hardware, making advanced autonomous driving research more accessible to the broader research community. We introduce a comprehensive dataset of 74,040 sequences, carefully annotated with driving preferences and driving decisions, which, along with our trained model checkpoints, is made publicly available https://github.com/LiYun0607/PrefDrive/ to facilitate future research. Through extensive experiments in the CARLA simulator, we demonstrate that our preference-guided approach significantly improves driving performance across multiple metrics, including distance maintenance and trajectory smoothness. Results show up to 28.1% reduction in traffic light violations and 8.5% improvement in route completion while maintaining appropriate distances from obstacles. The framework demonstrates robust performance across different urban environments, showcasing the effectiveness of preference learning in autonomous driving applications.

This paper proposes a robust composite control strategy for constrained continuous-time nonlinear systems by integrating sliding mode control (SMC) and model predictive control (MPC). SMC enhances disturbance rejection, while MPC handles constraints by solving an optimal control problem (OCP) based on the SMC input. The resulting control input ensures both robustness and constraint satisfaction. To improve computational efficiency, the OCP is solved only at sampling instants. Recursive feasibility and closed-loop stability are rigorously analyzed, and the method’s effectiveness is demonstrated on a cart-damper-spring system.

As autonomous vehicles (AVs) enter pedestrian-centric environments, existing vehicle-mounted external human–machine interfaces (eHMIs) often fall short in shared spaces due to line-of-sight limitations, inconsistent signaling, and increased decision latency on pedestrians. To address these challenges, we introduce the Smart Pole Interaction Unit (SPIU), an infrastructure-based eHMI that decouples intent signaling from vehicles and provides context-aware, elevated visual cues. We evaluate SPIU using immersive VR-AWSIM simulations in four high-risk urban scenarios: four-way intersections, autonomous mixed traffic, blindspots, and nighttime crosswalks. The experiment was developed in Japan and replicated in Norway, where forty participants engaged in 32 trials each under both SPIU-present and SPIU-absent conditions. Behavioral (response time) and subjective (acceptance scale) data were collected. Results show that SPIU significantly improves pedestrian decision-making, with reductions ranging from 40% to over 80% depending on scenario and cultural context, particularly in complex or low-visibility scenarios. Cross-cultural analyses highlight SPIU’s adaptability across differing urban and social contexts. We release our open-source Smartpole-VR-AWSIM framework to support reproducibility and global advancement of infrastructure-based eHMI research through reproducible and immersive behavioral studies.

In modern communication systems, meeting the growing demand for high-capacity transmission requires developing efficient and robust modulation techniques. To address this, we propose a low-PAPR page-style Orthogonal Time Frequency Space (OTFS) modulation framework that enhances communication capacity while maintaining a low peak-to-average power ratio (PAPR). The proposed design introduces a novel pilot signal placement and analysis method, improving channel estimation accuracy and system performance in high-mobility multi-user scenarios. This paper provides an overview of recent advancements in OTFS-based multi-user communication systems, emphasizing their contributions to enhancing spectral efficiency, reliability, and robustness. Through extensive simulations, we demonstrate the effectiveness of the proposed framework in achieving superior BER performance, improved interference mitigation, and robust transmission capabilities compared to traditional methods, validating its suitability for next-generation communication networks.

As networks increasingly become the backbone of modern society, the demands placed on them by various applications have become more complex. In particular, the demand for high-capacity, low-latency services such as real-time streaming is increasing every year. Although 5G has been deployed to meet these needs, its effectiveness can vary significantly by location and time, and sometimes falls short of requirements. Traditionally, much of the research to improve communication stability has focused on TCP-based systems, which do not translate well to real-time UDP streaming applications. To address the above challenges, we propose a multipath redundant communication framework designed to improve the quality of real-time media streaming. This framework has been tested using multipath redundant communication over two mobile networks with a moving vehicle in an urban environment. Using a real-time streaming application based on WebRTC, our framework demonstrates a significant reduction in packet loss and an increase in bitrate, outperforming existing multipath redundant communication systems without interfering with the application’s congestion control mechanisms. • Proposal of a Framework: The paper proposes a multipath redundant communication framework to improve the streaming quality via multipath redundant communications in 5G networks, particularly focusing on UDP media streaming applications. • Implementation and Verification: The framework has been implemented and verified using multipath communication over two mobile networks, with a vehicle moving in a real experiment, leveraging a real-time streaming application based on WebRTC. • Significant Improvements Demonstrated in a real experiment: Results from the implementation show significant reductions in packet loss and increases in bitrate, which notably outperforms existing multipath redundant communication systems without disrupting the applications’ congestion control mechanisms. • Challenges Addressed: The paper discusses the specific challenges related to the traditional approaches of multipath redundancy, especially in maintaining communication quality across varied network fields and discusses the advantages of the proposed method. • Future Research Directions: The paper concludes by discussing potential future research directions, including the necessity to further evaluate the framework in varied environments to verify its general applicability and latency performance.

This paper proposes a novel and robust framework for velocity and position estimation in vehicle-to-everything (V2X) communication networks, targeting challenges that arise when traditional GPS systems and line-of-sight (LoS) channels fail due to signal blockages between vehicles, base stations (BSs), and satellites. The proposed solution integrates multiple complementary methods to enhance robustness and accuracy. Specifically, the framework first applies the MUSIC algorithm for angle-of-arrival (AoA) estimation to obtain reliable initial positioning. In parallel, a newly designed low peak-to-average power ratio (PAPR) frame structure is introduced under reconfigurable intelligent surface (RIS)-assisted Orthogonal Time Frequency Space (OTFS) modulation, enabling resilient velocity embedding against noise and interference. These methods jointly support an unscented Kalman Filter (UKF), which refines the velocity and position estimation with high reliability. Extensive numerical evaluations confirm the effectiveness of the proposed multi-method integrated framework. Various modulation techniques, including zero padding (ZP), cyclic prefix (CP), and their recursive variants (e.g., RZP, RCP), demonstrate improved bit error rate (BER) performance over traditional OFDM. The proposed low-PAPR signal design achieves a 21.5% PAPR reduction compared to conventional embedded pilot schemes, significantly improving BER in 4-QAM scenarios. Velocity estimation results also show that the UKF outperforms the extended Kalman Filter (EKF) in both straight and curved road conditions. By combining MUSIC-based AoA estimation with RIS assistance under NLoS conditions, the proposed framework remains effective even in high-Doppler and low-SNR environments.

When employing autonomous driving technology in logistics, small autonomous delivery vehicles (aka delivery robots) encounter challenges different from passenger vehicles when interacting with other road users. We conducted an online video survey as a pre-study and found that autonomous delivery vehicles need external human-machine interfaces (eHMIs) to ask for help due to their small size and functional limitations. Inspired by everyday human communication, we chose arms as eHMI to show their request through limb motion and gesture. We held an in-house workshop to identify the arm’s requirements for designing a specific arm with shrink-ability (conspicuous when delivering messages but not affect traffic at other times). We prototyped a small delivery robot with a shrinkable arm and filmed the experiment videos. We conducted two studies (a video-based and a 360-degree-photo VR-based) with 18 participants. We demonstrated that arm-on-delivery robots can increase interaction efficiency by drawing more attention and communicating specific information.

The commercialization of 5G has been initiated for a while. Furthermore, millimeter wave (mmWave) has been introduced to small cells with small coverage due to its strong linearity and non-winding characteristics. On the other hand, in connected autonomous vehicles (CAV s), where various traffic systems can cooperatively perform recognition, decision-making, and execution, communication is assumed to be always connected. Therefore, to use low latency mm Wave for high-speed moving CAV, existing beamforming cannot follow them at high speed. This paper proposes an improved beam tracking algorithm for high-speed CAVs, which can be evaluated in a more general environment using a traffic simulator. We proposed an adaptive algorithm for a general road environment by increasing the number of beam searches and search dimensions.

This paper proposes an intensive null-steering around the target in angular domain to effectively suppress inter-user interference (IUI) leakage caused by channel varying environment such as vehicular multiuser spatial multiplexing. Multiuser MIMO can enhance spectral efficiency by multiplexing a number of user terminals in spatial domain. Suppose applying multiuser MIMO downlink in vehicle-to-everything (V2X) scenario, vehicles move at high speed which causes IUI. Null-space expansion has been conceived that can improve IUI suppression capability by steering nulls to the past and the present channel states on interfered users. Collective perception in intelligent transport systems (ITS) provides location information of vehicles every 100 ms. Exploiting this feature, this paper proposes angular-domain null-space expansion; broad-range null-steering (BRNS). Computer simulation verifies its effectiveness.

While text-based external human-machine interface (eHMI) is widely accepted, one limitation is the lack of capability to communicate spatial information such as a different person or location. We built a mixed-eHMI using "eye" as a target-specifier when "text" shows the clear intention to their communication partners. We conducted a pre-experimental observation to develop two testbed scenarios, followed by a video-based user study via life-size projection with a real-car prototype mounted a text display and a set of robotic eyes. The results demonstrated that our proposed "text + eye" combination may represent geospatial information by increasing the success pick-up rate.

Lane merging is one of the critical tasks for self-driving cars, and how to perform lane-merge maneuvers effectively and safely has become one of the important standards in measuring the capability of autonomous driving systems. However, due to the ambiguity in driving intentions and right-of-way issues, the lane merging process in autonomous driving remains deficient in terms of maintaining or ceding the right-of-way and attributing liability, which could result in protracted durations for merging and problems such as trajectory oscillation. Hence, we present a rule-compliance path planner (RCPP) for lane-merge scenarios, which initially employs the extended responsibility-sensitive safety (RSS) to elucidate the right-of-way, followed by the potential field-based sigmoid planner for path generation. In the simulation, we have validated the efficacy of the proposed algorithm. The algorithm demonstrated superior performance over previous approaches in aspects such as merging time (Saved 72.3%), path length (reduced 53.4%), and eliminating the trajectory oscillation.

The interactive decision-making in multi-agent autonomous racing offers insights valuable beyond the domain of self-driving cars. Mapless online path planning is particularly of practical appeal but poses a challenge for safely overtaking opponents due to the limited planning horizon. To address this, we introduce RaceMOP, a novel method for mapless online path planning designed for multi-agent racing of F1TENTH cars. Unlike classical planners that rely on predefined racing lines, RaceMOP operates without a map, utilizing only local observations to execute high-speed overtaking maneuvers. Our approach combines an artificial potential field method as a base policy with residual policy learning to enable long-horizon planning. We advance the field by introducing a novel approach for policy fusion with the residual policy directly in probability space. Extensive experiments on twelve simulated racetracks validate that RaceMOP is capable of long-horizon decision-making with robust collision avoidance during overtaking maneuvers. RaceMOP demonstrates superior handling over existing mapless planners and generalizes to unknown racetracks, affirming its potential for broader applications in robotics. Our code is available at http://github.com/raphajaner/racemop.

Pseudonyms are widely used in Cooperative Intelligent Transport Systems (C-ITS) to protect the location privacy of vehicles. However, the unlinkability nature of pseudonyms also enables Sybil attacks, where a malicious vehicle can pretend to be multiple vehicles at the same time. In this paper, we propose a novel protocol called zero-knowledge Proof of Distinct Identity (zk-PoDI,) which allows a vehicle to prove that it is not the owner of another pseudonym in the local area, without revealing its actual identity. Zk-PoDI is based on the Diophantine equation and zk-SNARK, and does not rely on any specific pseudonym design or infrastructure assistance. We show that zk-PoDI satisfies all the requirements for a practical Sybil-resistance pseudonym system, and it has low latency, adjustable difficulty, moderate computation overhead, and negligible communication cost. We also discuss the future work of implementing and evaluating zk-PoDI in a realistic city-scale simulation environment.

The burgeoning Intelligent Transportation System (ITS) spurs global technological advancements, notably in innovative community development through vehicle-to-everything (V2X) communication. This study focuses on the high data rates and low latency offered by a millimeter-wave (mmWave) enabled vehicular network while addressing the significant challenge of link quality degradation due to blockages, exacerbated by the mmWave band's small wavelength in high mobility and traffic conditions. We propose an RSU-assisted ITS system tailored for multi-lane, straight-road scenarios, effectively identifying blockage status for vehicles. Combining Simulation of Urban Mobility (SUMO) and MATLAB, this blockage-aware scheme lays the groundwork for future ITS enhancements. The research also delves into the effects of various frequency bands, vehicle types, and communication ranges, offering a holistic system performance analysis.

Despite advancements in connected and autonomous vehicles (CAVs), vulnerable road users (VRUs) face a challenge as they lack Communication-Intelligent Transport System (C-ITS) equipment. This deficiency impedes their interaction with CAVs. We underscore the significance of Vehicle-to-Everything (V2X) communication in enhancing road safety with VRUs by facilitating information exchange between CAVs and the infrastructure. This communication is pivotal for reintegrating VRUs into the environmental awareness of CAVs. The Carla Simulator, used for autonomous vehicle training, currently lacks comprehensive V2X communication capabilities. In response, we propose an architecture for Carla, integrating OpenCDA and ROS2 to establish a simulated V2X network communication system for CAVs and roadside units (RSUs) within the Carla environment. This setup allows for the generation of V2X datasets and the refinement of algorithms for Advanced Driver Assistance Systems (ADAS). To illustrate and assess our proposed architecture, we present a scenario involving a pedestrian concealed in a blind spot for a connected vehicle.

Coordination of connected autonomated vehicles (CAVs) is expected to provide a more efficient sequential route design and enhance safety. This is accomplished by sharing sensor data among roadside equipment and other vehicles. Given the substantial volume of sensor data involved, it is advantageous to employ millimeter-wave (mmWave) band. MmWave offers high-speed and large-capacity for transmission. However, wireless communication systems designed for CAVs face the challenge of radio signal degradation caused by the movement of vehicles. This paper proposes a fast beam tracking Open Radio Access Network (O-RAN) architecture for CAVs. The most prominent aspect of this system is its Near-Realtime (Near-RT) RAN Intelli-gent Controller (RIC), which swiftly adjusts and tracks the beam using vehicle information transmitted every 100 msec by CAV. By conducting simulations using Simulation of Urban Mobility (SUMO), which emulates vehicle movement on various roads, we verified the effective operation of the proposed architecture.

The rapid increase in the number of connected vehicles on roads has made vehicular ad-hoc networks (VANETs) an attractive target for malicious actors. As a result, VANETs require secure data transmission to maintain the network’s integrity. Federated learning (FL) has been proposed as a secure data-sharing method for VANETs, but it is limited in its ability to protect sensitive data. This article proposes integrating Blockchain technology into FL to provide an additional layer of security for VANETs. In particular, we propose a secure and efficient blockchain-based FL (SEBFL) approach to ensure communication efficiency and data privacy in VANETs. To this end, we use the FL model for VANETs, where computation tasks are decomposed from a base station to individual vehicles. This effectively reduces the congestion delay and communication overhead. Integrating blockchain with the FL model provides a reliable and secure data communication system between vehicles, roadside units, and a cloud server. Additionally, we use a homomorphic encryption system (HES) that effectively preserves the confidentiality and credibility of vehicles. Besides, the proposed SEBFL leverages the asynchronous FL model, minimizing the long delay while avoiding possible threats and attacks using HES. The experimental results show that the proposed SEBFL achieves 0.87% accuracy while a model inversion attack and 0.86% accuracy while a membership inference attack.

Autonomous driving (AD) endows vehicles with the capability to drive partly or entirely without human intervention. AD agents generate driving policies based on online perception results, which are crucial to the realization of safe, efficient, and comfortable driving behaviors, particularly in high-dimensional and stochastic traffic scenarios. Currently, deep reinforcement learning (DRL) techniques to derive and validate AD policies have witnessed vast research efforts and have shown rapid development in recent years. However, a comprehensive interpretation and evaluation of their strengths and limitations concerning the full-stack AD tasks remain uncharted. This paper presents a survey of this body of work, which is conducted at three levels. First, it analyzes the multi-level AD task characteristics and delves deeply into the current DRL methodologies primarily employed in AD. Second, a taxonomy of the literature studies is constructed from the system perspective, identifying six modes of DRL model integration into an AD architecture that span the entire spectrum of AD policy processes, from perception understanding and decision-making to motion control, as well as verification and validation. Each literature review comprehensively encompasses the main elements of designing such a system, including modeling partially observable environments, state and action spaces, reward structuring, and the design and training methodologies of neural network models. Finally, an in-depth foresight is conducted on how the eight critical issues of AD application development are addressed by the DRL models tailored for real-world AD challenges.

Cooperative perception is an essential and widely discussed application of connected automated vehicles. However, the authenticity of perception data is not ensured, because the vehicles cannot independently verify the event they did not see. Many methods, including trust-based (i.e., statistical) approaches and plausibility-based methods, have been proposed to determine data authenticity. However, these methods cannot verify data without a priori knowledge. In this study, a novel approach of constructing a self-proving data from the number plate of target vehicles was proposed. By regarding the pseudonym and number plate as a shared secret and letting multiple vehicles prove they know it independently, the data authenticity problem can be transformed to a cryptography problem that can be solved without trust or plausibility evaluations. Our work can be adapted to the existing works including ETSI/ISO ITS standards while maintaining backward compatibility. Analyses of common attacks and attacks specific to the proposed method reveal that most attacks can be prevented, whereas preventing some other attacks, such as collusion attacks, can be mitigated. Experiments based on realistic data set show that the rate of successful verification can achieve 70% to 80% at rush hours.

The exhaustion of IPv4 addresses has driven the rapid adoption of IPv6 networks, which has created challenges in the domain name resolution process, particularly for IPv6-only iterative resolvers. This paper presents an experimental analysis to quantify the extent of this problem, revealing a significantly lower success rate of name resolution using IPv6-only resolvers (64.1%) compared to IPv4-only resolvers (98.8%). By analysing the success rates and percentages of A and AAAA records for the top 1,000,000 domains in the Tranco list, we identify the limitations of IPv6-only iterative resolvers and highlight the urgent need for comprehensive solutions to improve DNS resolution in IPv6-only networks. Our findings emphasise the importance of full IPv6 adoption for improved compatibility in IPv6-only environments, and serve as a basis for addressing the challenges faced by IPv6-only networks.

Path planning is critical for autonomous vehicles (AVs) to determine the optimal route while considering constraints and objectives. The potential field (PF) approach has become prevalent in path planning due to its simple structure and computational efficiency. However, current PF methods used in AVs focus solely on the path generation of the ego vehicle while assuming that the surrounding obstacle vehicles drive at a preset behavior without the PF-based path planner, which ignores the fact that the ego vehicle's PF could also impact the path generation of the obstacle vehicles. To tackle this problem, we propose a PF-based path planning approach where local paths are shared among ego and obstacle vehicles via vehicle-to-vehicle (V2V) communication. Then by integrating this shared local path into an objective function, a new optimization function called interactive speed optimization (ISO) is designed to allow driving safety and comfort for both ego and obstacle vehicles. The proposed method is evaluated using MATLAB/Simulink in the urgent merging scenarios by comparing it with conventional methods. The simulation results indicate that the proposed method can mitigate the impact of other AVs' PFs by slowing down in advance, effectively reducing the oscillations for both ego and obstacle AVs.

Making safe and successful lane changes (LCs) is one of the many vitally important functions of autonomous vehicles (AVs) that are needed to ensure safe driving on expressways. Recently, the simplicity and real-time performance of the potential field (PF) method have been leveraged to design decision and planning modules for AVs. However, the LC trajectory planned by the PF method is usually lengthy and takes the ego vehicle laterally parallel and close to the obstacle vehicle, which creates a dangerous situation if the obstacle vehicle suddenly steers. To mitigate this risk, we propose a time-to-collision-aware LC (TTCA-LC) strategy based on the PF and cubic polynomial in which the TTC constraint is imposed in the optimized curve fitting. The proposed approach is evaluated using MATLAB/Simulink under high-speed conditions in a comparative driving scenario. The simulation results indicate that the TTCA-LC method performs better than the conventional PF-based LC (CPF-LC) method in generating shorter, safer, and smoother trajectories. The length of the LC trajectory is shortened by over 27.1%, and the curvature is reduced by approximately 56.1% compared with the CPF-LC method.

This paper introduces an "IPv6-only-Capable resolver" to address the issue of many zones remaining unresolvable due to a lack of IPv6 connectivity in authoritative name servers. The proposed method utilizes NAT64 to transmit packets to IPv4-only authoritative name servers and increases resolution success rates with competitive response times compared to a traditional IPv6-only resolver.

For cooperative intelligent transport systems (C-ITS), vehicle-to-everything (V2X) communication is utilized to allow autonomous vehicles to share critical information with each other. We propose AutowareV2X, an implementation of a V2X communication module that is integrated into the autonomous driving (AD) software, Autoware. AutowareV2X provides external connectivity to the entire AD stack, enabling the end-to-end (E2E) experimentation and evaluation of connected autonomous vehicles (CAV). The Collective Perception Service was also implemented, allowing the transmission of Collective Perception Messages (CPMs). A dual-channel mechanism that enables wireless link redundancy on the critical object information shared by CPMs is also proposed. Performance evaluation in field experiments has indicated that the CPM-based perception information can be transmitted in around 30 ms, and shared object data can be used by the AD software to conduct collision avoidance maneuvers. The dual-channel delivery of CPMs transmits perception information through two different wireless technologies. The receiver-side CAV can then dynamically select the best CPM from CPMs received from both links, depending on the freshness of their information.

The potential functions (PFs) have generally shown good performances in real-time path planning with computation efficiency conforming to the requirements of lower control systems in autonomous driving. However, several inherent limitations exist in using the PFs, including a local minimum in specific scenarios and no passage between closely spaced obstacles. Recent studies have focused on conventional scenarios where PFs are assumed to work normally, without malfunctioning, occurring during perilous situations. Therefore, we propose a specific safety tunnel (ST)-based model predictive controller (MPC) combined with PFs (PF-STMPC) to handle path-planning in extreme-emergency traffic scenarios (e.g., emergency braking and lane-changing obstacles). To further guarantee driving safety, we improve PFs with the responsibility-sensitive safety (RSS) model that accurately calculates the minimum safe longitudinal and lateral distances. Furthermore, a sigmoid-based ST is designed for emergency navigation if the PFs fail to plan a safe path due to the aforementioned inherent limitations, enabling the controller with planning functionality if necessary. The ST is embedded in the MPC-based tracking controller as a safe constraint sensitive to surrounding environments (e.g., road structure and obstacles). The proposed PF-STMPC was co-simulated using MATLAB/Simulink and CarSim Simulator under the constant speed condition. Compared with the state-of-the-art method, the proposed method demonstrated better performance in finding a safe path and eliminating severe yawing of the ego-vehicle (82.8% less in sideslip yawing amplitude and 57.7% shorter in the oscillation period of yaw angle) when facing traffic emergencies.

In cooperative ITS, security and privacy protection are essential. Cooperative Awareness Message (CAM) is a basic V2V message standard, and misbehavior detection is critical for protection against attacking CAMs from the inside system, in addition to node authentication by Public Key Infrastructure (PKI). On the contrary, pseudonym IDs, which have been introduced to protect privacy from tracking, make it challenging to perform misbehavior detection. In this study, we improve the performance of misbehavior detection using observation data of other vehicles. This is referred to as collective perception message (CPM), which is becoming the new standard in European countries. We have experimented using realistic traffic scenarios and succeeded in reducing the rate of rejecting valid CAMs (false positive) by approximately 15 percentage points while maintaining the rate of correctly detecting attacks (true positive).

Potential field (PF)-based path planning is reported to be highly efficient for autonomous vehicles because it performs risk-aware computation and has a simple structure. However, the inherent limitations of the PF make it vulnerable in some specific traffic scenarios, such as local minima and oscillations in close obstacles. Therefore, a hybrid path planning with the sigmoid curve has recently been presented to generate better trajectories than those generated by the PF for collision avoidance. However, it is time-consuming and less applicable in complex dynamic environments, especially in traffic emergencies. To address these limitations, we propose a cooperative hybrid path planning (CHPP) approach that involves collaboration with adjacent vehicles for emergency collision avoidance via V2V communication. Moreover, the responsibility-sensitive safety (RSS) model is introduced to enhance the PF and sigmoid curve for safe-critical and time-saving requirements. The effectiveness of the proposed CHPP method compared with the state-of-the-art methods is studied through simulation of both static and dynamic traffic emergency scenarios. The simulation results prove that the CHPP approach performs better in terms of computation time (0.02 s faster) and driving safety (avoiding collision) than other methods, which are more supportive for emergency cooperative driving.

Cooperative intelligent transportation systems (ITS) are used by autonomous vehicles to communicate with surrounding autonomous vehicles and roadside units (RSU). Current C-ITS applications focus primarily on real-time information sharing, such as cooperative perception. In addition to realtime information sharing, self-driving cars need to coordinate their action plans to achieve higher safety and efficiency. For this reason, this study defines a vehicles future action plan/path and designs a cooperative path-planning model at intersections using future path sharing based on the future path information of multiple vehicles. The notion is that when the RSU detects a potential conflict of vehicle paths or an acceleration opportunity according to the shared future paths, it will generate a coordinated path update that adjusts the speeds of the vehicles. We implemented the proposed method using the open-source Autoware autonomous driving software and evaluated it with the LGSVL autonomous vehicle simulator. We conducted simulation experiments with two vehicles at a blind intersection scenario, finding that each car can travel safely and more efficiently by planning a path that reflects the action plans of all vehicles involved. The time consumed by introducing the RSU is 23.0 % and 28.1 % shorter than that of the stand-alone autonomous driving case at the intersection.

The realization of vehicle-to-everything (V2X) communication enhances the capabilities of autonomous vehicles in terms of safety efficiency and comfort. In particular, sensor data sharing, known as cooperative perception, is a crucial technique to accommodate vulnerable road users in a cooperative intelligent transport system (ITS). In this regard, open-source software plays a significant role in prototyping, validation, and deployment. Specifically, in the developer community, Autoware is a popular open-source software for self-driving vehicles, and OpenC2X is an open-source experimental and prototyping platform for cooperative ITS. This paper reports on a system named AutoC2X to enable cooperative perception by using OpenC2X for Autoware-based autonomous vehicles. The developed system is evaluated by conducting field experiments involving real hardware. The results demonstrate that AutoC2X can deliver the cooperative perception message within 100 ms in the worst case.

Internet-native audio-visual services are witnessing rapid development. Among these services, object-based audiovisual services are gaining importance. In 2014, we established the Software Defined Media (sDM) consortium to target new research areas and markets involving object-based digital media and Internet-by-design audio-visual environments. In this paper, we introduce the SDM architecture that virtualizes networked audio-visual services along with the development of smart buildings and smart cities using Internet of Things (IoT) devices and smart building facilities. Moreover, we design the SDM architecture as a layered architecture to promote the development of innovative applications on the basis of rapid advancements in software-defined networking (SDN). Then, we implement a prototype system based on the architecture, present the system at an exhibition, and provide it as an SDM API to application developers at hackathons. Various types of applications are developed using the API at these events. An evaluation of SDM API access shows that the prototype SDM platform effectively provides 3D audio reproducibility and interactiveness for SDM applications.

Local mapping is valuable for many real-time applications of intelligent vehicle systems. Multivehiclecooperative local mappingcan bring considerable benefits to vehicles operating in some challenging scenarios. In this paper, we introduce a method of occupancy grid map merging, dedicated to multivehicle cooperative local mapping purpose in outdoor environments. In a general map merging framework, we propose an objective function based on occupancy likelihood and provide some concrete procedures designed in the spirit of genetic algorithm to optimize the defined objective function. Based on the introduced method, we further describe a strategy of indirect vehicle-to-vehicle (V2V) relative pose (RP) estimation, which can serve as a general solution for multivehicle perception association. We present a variety of experiments that validate the effectiveness of the proposed occupancy grid map merging method. We also demonstrate several useful application examples of the indirect V2V RP estimation strategy.

One of the essential usage of Intelligent Transportation Systems (ITS) applications is to provide road traffic information to vehicle drivers for road safety and efficient drive. For this usage, it is necessary to integrate geographical routing mechanisms in vehicular ad hoc network (VANET) into ITS applications. In this paper, we design and implement an ITS application which relies on IPv6 GeoNetworking; a geographical addressing and routing mechanism developed in the GeoNet project. Our application supports realistic use case scenarios, therefore we integrated it into INRIA's vehicular platform. The system has publicly been demonstrated in realistic scenarios.

Vehicular communication is an important part of the Intelligent Transportation Systems (ITS). Geographic routing in vehicular ad hoc network (VANET) is becoming an interesting topic to deliver safety messages between cars but also between a car and a roadside infrastructure within a designated destination area. The Car2Car Communication Consortium specified C2CNet architecture as a geographic routing protocol. The results of GeoNet project are presented in the paper, which aims at combining IPv6 networking and C2CNet. The system with IPv6 and C2CNet is designed and implemented in Linux. The prototype implementation is first evaluated indoor testbed with the fixed positions. Then it is evaluated in the field testbed with three vehicles with various scenarios. For evaluation in field testbed, we have developed the AnaVANET evaluation tool to perform the evaluation taking into account all of geographic factors.

Vehicular communications attract the attention of many people in the networking research world. These networks present some special features, such as high mobility or specific topologies, which affect the performance of applications. In order to select the appropriate technologies, more effort should be directed to identify the final necessities of the network. Few works identify possible applications of vehicular networks, but none of them link application requirements which networking technologies available in the vehicular field. In this paper, we fill this gap, and propose an analysis of application requirements and study how to deal with them using communication technologies for the physical and network level. This study contains key factors which must be taken into account, especially, at the designing stage of the vehicular network.