Accurate motion prediction is a cornerstone of safe autonomous driving and a key enabler for building efficient intelligent transportation systems in Internet of Things environments. Mainstream methods typically follow a divided paradigm: first modeling scene dependencies and then generating trajectories in a one-shot manner. Despite their promising performance, persistent challenges still exist, especially regarding insufficient future representation learning due to constrained information and suboptimal motion decoding from overlooking varying temporal correlations. In this paper, we propose FLAMD, a novel trajectory prediction framework that integrates multi-level feature learning with multi-scale motion decoding. Different from previous methods with decoupled design, FLAMD introduces a joint optimization scheme for multimodal motion representation learning and scene context modeling. Equipped with the carefully designed motion-aware feature interaction module and cross-consistency enhancement mechanism, FLAMD promotes long-range interactions and feature co-evolution between historical observations and anticipated scenarios, guided by diverse future-oriented queries. This mutual interaction enables deeper reasoning about scene dynamics, leading to context-aware and future-guided representations. In the decoding phase, FLAMD proposes to extend traditional single-stage decoding into a multi-scale framework. Multiple horizon-specific decoders operate at different temporal resolutions, each custom-made for the complexity of its assigned prediction window. This structure helps to capture temporal correlations and allocates appropriate feature granularity across varying future steps, thereby improving the capacity of trajectory generation. Extensive experiments on two real-world benchmarks demonstrate the superiority of our approach in generating accurate and reliable predictions in diverse traffic scenarios.

Network attacks on the Internet of Vehicles (IoV) can lead to catastrophic consequences such as traffic congestion, incorrect routing, and even accidents. Existing rule-based countermeasures are effective only in specific scenarios, while machine learning-based methods suffer from suboptimal performance due to data quality issues. Furthermore, the isolation between in-vehicle networks (IVN) and external vehicle networks (EVN) prevents independent intrusion detection systems (IDS) from detecting continuous attack behaviors. To address these issues, we propose a mechanism that characterizes attack behaviors solely based on network connection information. This mechanism downplays the specifics of the traffic itself, allowing for the integration of IVN and EVN through structure, thus breaking the internal-external boundary and providing relatively stable logical structure information. This approach also offers opportunities to address the scarcity of labeled data. Based on this characterization mechanism, we develop an intrusion detection system – One$^{2}$, utilizing a multi-attribute heterogeneous graph transformer to achieve accurate multi-classification of various types of attacks. To assess the compatibility of the proposed IDS for IVN and EVN, extensive experiments were conducted using six real-world datasets that accurately depict IVN and EVN. The results show that One$^{2}$improves average accuracy by 7.14% and F1 score by 6.97% compared to state-of-the-art methods. The source code of this work is available at:https://github.com/LouHerGetUp/One2

Understanding the evolution of 3D scenes is crucial for autonomous driving. While conventional methods describe scene development through individual instance motions, world models provide a generative framework for modeling overall scene dynamics. However, most existing approaches rely on autoregressive next-token prediction, which suffers from error accumulation and limited global spatiotemporal reasoning, leading to degraded long-term consistency. To address these issues, we propose a diffusion-based 4D occupancy generation model, OccSora, to simulate 3D world evolution for autonomous driving. A 4D scene tokenizer is introduced to obtain compact spatiotemporal representations and enable high-quality reconstruction of long occupancy sequences. We then train a diffusion transformer on these representations to generate 4D occupancy conditioned on trajectory prompts. Experiments on the nuScenes dataset with Occ3D annotations show that OccSora can generate 16s videos with authentic 3D layout and strong temporal consistency. With trajectory-aware 4D generation, OccSora has the potential to serve as a world simulator for autonomous driving decision-making. Project page: https://wzzheng.net/OccSora.

Adaptive traffic signal control dynamically adapts to real-time traffic flow variations, effectively mitigating traffic congestion through efficient decision-making. Current ATSC methods primarily focus on vehicle mobility metrics, such as average waiting time and average speed. However, urban road networks require not only high traffic efficiency but also proactive prevention of critical scenarios like overflow, which, if not intervened, may degrade network-wide traffic conditions and compromise safety. Therefore, in order to a balance between overflow prevention and efficiency, we model traffic signal control as a Constrained Markov Decision Process (CMDP), and propose OAlight, an overflow-aware adaptive traffic signal control framework based on reinforcement learning (RL). Unlike existing RL approaches that merely incorporate overflow-related features into the reward function, OAlight integrates overflow awareness throughout the entire RL lifecycle — environment interaction, state representation, and policy learning. Specifically, considering the explicit overflow factor of excessive queue lengths and the implicit overflow factor of excessive waiting time for individual vehicles, we construct a dual safeguard mechanism for overflow prevention through environmental interactions by developing a comprehensive reward-cost function framework. In this framework, the reward function simultaneously captures both explicit and implicit factors, while two specialized cost functions assess behaviour that violates threshold performance metrics of explicit and implicit factors, respectively. We then design a state encoder and a state-action joint encoder to extract overflow-aware representations from the state space. Finally, we employ a Proximal Policy Optimization with Lagrangian method to guide policy learning, ensuring compliance with overflow constraints while optimizing control actions. The experimental results indicate that our approach achieves superior performance on our proposed overflow cost metric without compromising traffic efficiency metrics on both synthetic and real-world datasets, compared to the current state-of-the-art traffic signal control methods. Furthermore, the environment interaction module exhibits broad compatibility with diverse RL methods, significantly reducing overflow risks.

Digital Twin (DT) technology has gained significant attention for simulating and optimizing urban traffic systems, especially in intersection vehicle trajectory prediction. However, digital twin traffic system faces significant challenges due to privacy and security regulations that prevent the centralized storage of trajectory and semantic data, which are essential for training accurate predictive models using sensitive traffic information. To address these issues, we introduce the integration of federated learning spatio-temporal-semantic attention-based trajectory (FedSTAST) model into the DT framework for vehicle trajectory prediction. In our FedSTAST, edge servers in physical space utilize local sensor data to perform computations and train models without transmitting raw data, only the model parameters are sent to a cloud server in the twin space for aggregation. This decentralized approach ensures data privacy while enabling collaborative model training. The simulation results demonstrate that the FedSTAST model effectively handles co-training and multi-source semantic input processing within spatio-temporal-semantic attention-based trajectory (STSAT) models, enhancing trajectory prediction accuracy and robustness in the dynamic, real-time context of DT-based urban traffic systems.

Reliable planning is crucial for achieving autonomous driving. Rule-based planners are efficient but lack generalization, while learning-based planners excel in generalization yet have limitations in real-time performance and interpretability. In long-tail scenarios, these challenges make planning particularly difficult. To leverage the strengths of both rule-based and learning-based planners, we proposed the Scenario-Aware Hybrid Planner (SAH-Drive) for closed-loop vehicle trajectory planning. Inspired by human driving behavior, SAH-Drive combines a lightweight rule-based planner and a comprehensive learning-based planner, utilizing a dual-timescale decision neuron to determine the final trajectory. To enhance the computational efficiency and robustness of the hybrid planner, we also employed a diffusion proposal number regulator and a trajectory fusion module. The experimental results show that the proposed method significantly improves the generalization capability of the planning system, achieving state-of-the-art performance in interPlan, while maintaining computational efficiency without incurring substantial additional runtime.

Autonomous vehicles (AVs) face significant threats to their safe operation in complex traffic environments. Adver-sarial policy for scenario generation has been established as a robust paradigm for enhancing AV resilience against adver-sarial perturbations through proactive exposure to synthetically engineered safety-critical scenarios. Training an attacker within an adversarial policy, allowing the target AV to expose vulnerabilities through interaction with this attacker. However, adversarial policies in existing methodologies often get stuck in a loop of over-exploiting established vulnerabilities, resulting in poor exploration for AVs. To overcome the limitations, we introduce a pioneering framework termed the vulnerability-aware and curiosity-driven adversarial reinforcement learning policy. Specifically, during the traffic vehicle attacker training phase, a surrogate network is employed to fit the value function of the AV victim, providing dense information about the victim's inherent vulnerabilities. Subsequently, random network distillation is used to characterize the novelty of the scenario, constructing an intrinsic reward to guide the attacker in exploring unexplored territories. Experimental results demonstrated that the adversarial policy embedded within the attacker exhibited robustness in convergence and significantly enhanced the activation of policy exposure in learning-based AVs, outperforming both other adversarial modalities and alternative reinforcement learning approaches, with a notable reduction in crash rates. The code is available at https://github.com/caixxuan/VCAT.

V2X technology facilitates real-time communication between vehicles, enabling collision avoidance systems, proactive hazard warnings, and cooperative maneuvers to prevent potential accidents. Due to the inherent openness of wireless communication channels, vehicular networks are highly susceptible to various security threats. Digital signatures have been widely adopted as an effective verification mechanism to ensure message integrity and authenticity. However, in high-density traffic environments, the sheer volume of messages imposes a significant computational burden on the verification process, leading to excessive delays and potential packet loss which compromises the timeliness and reliability of safety-critical applications. To address this issue, we propose a priority-aware signature verification scheme DPBV that dynamically prioritizes V2X messages based on their urgency and relevance. By leveraging clustering-based classification and batch verification techniques, the proposed approach optimizes the processing efficiency of safety messages while maintaining stringent security guarantees. Simulation results demonstrate that our scheme significantly reduces verification latency and improves message authentication throughput, making it well-suited for real-time V2X communication in high-density vehicular networks.

Existing autonomous driving systems are optimized for egocentric efficiency metrics, which are in fundamental conflict with the socialized expectations and habitual patterns of human drivers. This contradiction stems from the traditional approach's dual neglect of the trade-offs between self and other in driving decisions, and the culturally rooted social qualities of traffic interactions. To this end, this paper proposes a dual-adaptation framework that integrates social value orientation (SVO) and human-in-the-Ioop(HITL) guidance, modeling vehicular interactions as competitive-cooperative agents by quantifying the social utility function, and dynamically calibrating the SVO parameters with the help of real-time human feedback. The method innovatively transforms abstract social preferences into mathematically tractable decision boundaries, enabling the human-vehicle co-evolutionary mechanism to contextualize self-adaptation according to the regional driving etiquette, and thus cracking the inherent contradiction between individual trajectory optimization and group traffic harmony. Empirical studies based on Highway Env driving scenarios show that compared with pure reinforcement learning methods, this method reduces human-vehicle interaction conflicts while maintaining self-vehicle efficiency. The research results provide a quantifiable interaction paradigm and a verifiable training architecture for the construction of culturally-aware autonomous driving systems through the deep coupling of computational social value modeling and human social intelligence.

Evaluating and ensuring the adversarial robustness of autonomous driving (AD) systems is a critical and unresolved challenge. This paper introduces MetAdv, a novel adversarial testing platform that enables realistic, dynamic, and interactive evaluation by tightly integrating virtual simulation with physical vehicle feedback. At its core, MetAdv establishes a hybrid virtual-physical sandbox, within which we design a three-layer closed-loop testing environment with dynamic adversarial test evolution. This architecture facilitates end-to-end adversarial evaluation, ranging from high-level unified adversarial generation, through mid-level simulation-based interaction, to low-level execution on physical vehicles. Additionally, MetAdv supports a broad spectrum of AD tasks, algorithmic paradigms (e.g., modular deep learning pipelines, end-to-end learning, vision-language models). It supports flexible 3D vehicle modeling and seamless transitions between simulated and physical environments, with built-in compatibility for commercial platforms such as Apollo and Tesla. A key feature of MetAdv is its human-in-the-loop capability: besides flexible environmental configuration for more customized evaluation, it enables real-time capture of physiological signals and behavioral feedback from drivers, offering new insights into human-machine trust under adversarial conditions. We believe MetAdv can offer a scalable and unified framework for adversarial assessment, paving the way for safer AD. Our demo can be found at https://sites.google.com/view/metadv-demo-video.

Predicting the future trajectories of moving agents serves as a pivotal endeavor in advancing safe autonomous driving forward within the context of the evolving Internet of Things technology. Despite significant progress driven by deep learning methods, a gap in predictive capability remains when compared to experienced human drivers, particularly in scenarios that demand heightened perception and broader situational understanding. In this study, we propose Brimory, a novel trajectory prediction approach inspired by human driving, designed to equip autonomous vehicles with humanoid memory systems. The core concept of Brimory is to emulate human driving processes by enabling the model to deeply comprehend traffic scenes, establish working memory, and iteratively accumulate to form driving experience. Brimory first introduces the working memory generator, which processes multiple interactions between the scene elements in the driving environment. It is noted that unlike traditional models confined to the time domain, Brimory also extracts underlying dependencies in the frequency domain. The long-term memory builder module is then developed to adaptively retrieve information from working memory and gradually accumulate driving experience with the help of the designed iterative updating mechanism. To mitigate the impact of irrelevant components on long-term memory effectiveness, we devise a co-modulation strategy that retains meaningful representations in a learnable manner. Finally, the combined humanoid memories are leveraged by the multimodal trajectory predictor to forecast future motions. Extensive experiments on benchmark datasets demonstrate the superiority, feasibility, and efficiency of Brimory. The results underscore the potential of humanoid memory frameworks in trajectory prediction, offering a promising path toward the realization of safer autonomous vehicles.

Predicting future motions of target agents is crucial to ensuring the safety of autonomous vehicles in Internet of Things environments. Although significant progress has been made in this field, most mainstream approaches rely heavily on high-definition (HD) maps, which may not always be available or accurate owing to the high costs of map construction and the potential localization errors. Without the explicit guidance of HD maps, trajectory prediction would become more challenging. To address this challenge, we present MLB-Traj, an innovative framework for map-free motion prediction based on local behavior queries. MLB-Traj leverages the observation that agents often follow local behavior patterns in specific traffic scenarios, where these local behaviors reveal the potential trajectories of the targets and contain scenario-consistent information. It starts with a hierarchical dynamic modal query paradigm that first captures the scene’s general modal characteristics and then models target-specific properties. A dual Transformer query mechanism aggregates multiscale relationships to facilitate this process. To tackle potential inconsistency in map-free forecasting, we introduce a trajectory consistency module. It ensures the continuity of inferred trajectories by utilizing patch-wise interaction representations to capture local temporal dependencies, while also learning more robust representations by simulating the model’s response to spatial inconsistency in its predictions. Extensive experiments conducted on real-world datasets validate the effectiveness of MLB-Traj. The results indicate that our framework outperforms existing methods, highlighting its superiority in generating accurate predictions in map-free settings.

With the accelerated growth of the Internet of Vehicles (IoV), secure and efficient task offloading of vehicle has emerged as a critical challenge, particularly in highway scenarios. Traditional mobile edge computing (MEC) solutions face significant limitations in these environments due to frequent link disruptions and the dynamic nature of vehicle movements. Platoon offloading is considered a feasible solution, to address these challenges, we propose a novel dynamic pricing-based task offloading and resource allocation scheme specifically tailored for vehicle platoons, integrating robust data security measures. Our scheme employs a Stackelberg game framework to model the interaction between task vehicles and platoon members (PMs), ensuring fair compensation for resource allocation while maintaining low latency. We introduce a personalized security layer utilizing advanced encryption standard (AES) encryption to safeguard platoon communications, a critical enhancement given the vulnerability of wireless channels. Our scheme not only proves the existence of a unique Nash equilibrium but also optimizes the utility for both task vehicles and PMs through a dynamic pricing-based Stackelberg game (DPSG) algorithm. Simulation results demonstrate that DPSG can substantially improve entire performance compared to other schemes, such as local execution, MEC offloading scheme, Hooke-Jeeves-based Stackelberg game algorithm, and reinforcement learning-based offloading optimal scheme.

Predicting the future trajectories of dynamic traffic actors is the Gordian knot for autonomous vehicles to achieve collision-free driving. Most existing works suffer from a gap in characterizing the evolving interactions of scenario components and ensuring the physical feasibility of predictions, particularly in highly heterogeneous scenarios. Therefore, we propose an Enhanced Multi-Stream Interaction Network (EMSIN), which is devoted to providing accurate trajectory predictions. EMSIN highlights several threads of high-level time-varying interactions, including agent-traffic semantic, self-trend, and agentagent dependencies. A novelly-designed trend-aware mechanism is developed to capture the self-trend interactions from different representation subspaces sufficiently. To model the spatial information of traffic agents and extract their evolutions, we present a dynamic adaptive graph convolutional network that extends previously predefined graph paradigms. The adaptive and dynamic graphs in EMSIN are created using learnable node embeddings, allowing the model to discern interaction strengths without additional attention modules. Finally, all highlevel feature spaces elaborating multi-stream interactions are fused to generate possible agent actions with corresponding confidence values. Comprehensive experiments conducted on both L5kit and nuScenes datasets demonstrate that EMSIN surpasses its counterparts, boasting smaller prediction errors and faster inference times. This study also introduces a fuzzy-based metric to probe the physical feasibility of predicted trajectories, providing valuable insights into appraising the performance of various prediction models from the perspective of fuzziness.

A mixed traffic environment of manual driving and automatic driving will become the norm in future intelligent transportation systems. The deep reinforcement learning (DRL) method has shown significant promise in cooperative control for traffic lights and connected autonomous vehicles (CAV) in a mixed-traffic environment. However, the uncertainty and noise in integrating agents' observations can lead to inadequate exploration of environmental data by DRL algorithms. Consequently, these algorithms are prone to overfitting and becoming trapped in local optimal, which limits the performance of control strategies. To more effectively harness the gathered environmental data and thereby facilitate improved decision-making by agents, a DRL-based cooperative control method with fuzzy feature fusion (F3DRL) was proposed in this article. First, the adaptive fuzzy inference module is implemented to adaptively mitigate information uncertainty as the data from CAV is aggregated. Then, a deep information extraction module was introduced and integrated with the output of the adaptive fuzzy inference module to establish a parallel feature fusion module. The adaptive fuzzy inference module mitigates uncertainty in the extracted traffic environmental states, while the deep information extraction module facilitates the extraction of a more comprehensive environmental representation. The fusion of features derived from these two distinct modules aids DRL agents in making better action selections, which significantly enhances the effectiveness and stability of the F3DRL method. In simulations, F3DRL significantly reduced travel and delay times, fuel consumption, and CO$_{2}$emissions, outperforming both traditional and state-of-the-art methods.

As an emerging urban computing paradigm, vehicle crowd sensing (VCS) leverages ubiquitous vehicles as basic sensing units to achieve more efficient data collection. However, with the expansion of the sensing range, the tens of thousands of vehicles and the openness of urban road networks pose a huge challenge for real-time participant recruitment in online VCS systems. To achieve efficient city-scale VCS, this paper proposes Dec-Recruiter, a decentralized framework for online participant recruitment. Specifically, Dec-Recruiter adopts a novel decision-making mode based on virtual grid agents, where vehicles traveling in the same direction within the same grid are considered homogeneous, simplifying the recruitment of specific vehicles to the selection of the number of vehicles in each direction. Meanwhile, through policy sharing among grid agents with the same geographic features, the complexity of city-scale VCS participant recruitment is further reduced. The core of Dec-Recruiter is a multi-agent contextual double-deep Q-network algorithm, which enables grid agents with different geographic features to collaborate on network-wide sensing tasks through their asynchronous decision-making. In this process, the Gaussian function is employed to adjust the reward distribution to address cold-start and data integrity issues in VCS. In addition, to ensure the convergence and training efficiency of the model on large-scale road networks, a pre-training-based transfer learning paradigm is also introduced. We conduct extensive experiments on both synthetic and real-world datasets. The results demonstrate that Dec-Recruiter can effectively recruit appropriate participants in the large-scale VCS and outperforms all baselines.

In mixed autonomous driving environments, accurately predicting the future trajectories of surrounding vehicles is crucial for the safe operation of autonomous vehicles (AVs). In driving scenarios, a vehicle’s trajectory is determined by the decision-making process of human drivers. However, existing models primarily focus on the inherent statistical patterns in the data, often neglecting the critical aspect of understanding the decision-making processes of human drivers. This oversight results in models that fail to capture the true intentions of human drivers, leading to suboptimal performance in long-term trajectory prediction. To address this limitation, we introduce a Cognitive-Informed Transformer (CITF) that incorporates a cognitive concept, Perceived Safety, to interpret drivers’ decision-making mechanisms. Perceived Safety encapsulates the varying risk tolerances across drivers with different driving behaviors. Specifically, we develop a Perceived Safety-aware Module that includes a Quantitative Safety Assessment for measuring the subject risk levels within scenarios, and Driver Behavior Profiling for characterizing driver behaviors. Furthermore, we present a novel module, Leanformer, designed to capture social interactions among vehicles. CITF demonstrates significant performance improvements on three well-established datasets. In terms of long-term prediction, it surpasses existing benchmarks by 12.0% on the NGSIM, 28.2% on the HighD, and 20.8% on the MoCAD dataset. Additionally, its robustness in scenarios with limited or missing data is evident, surpassing most state-of-the-art (SOTA) baselines, and paving the way for real-world applications.

Occupancy prediction plays a crucial role in supporting autonomous driving planning and decision-making. Existing methods typically rely on modular stacking and fusion techniques of object detection, semantic segmentation, and depth estimation to achieve 3D occupancy. However, they fail to deeply explore the transformation relationships between 2D and 3D spaces and to efficiently fuse the different characteristics of multi-source sensors. We propose R$M^{2}$Occ, the first 3D occupancy perception network that integrates multi-sensor fusion based on different sensor principles and achieves multi-task learning. To leverage the rich 2D semantic information captured by cameras and elevate it to the 3D domain, we begin by querying and populating predefined empty voxels with multi-view image features. Subsequently, we progressively fuse 3D LiDAR point clouds with these populated voxels through an unbalanced fusion strategy that effectively supplements missing information and suppresses noise. Leveraging IMU data and calibration parameters, we then re-project the enriched voxels back onto the 2D image plane according to camera coordinates, performing a secondary query using the semantic segmentation results to recover semantic details potentially lost due to radar fusion limitations and incomplete voxel querying. Finally, supported by a multi-task detection head, R$M^{2}$Occ simultaneously accomplishes 3D object detection, semantic segmentation, Bird’s Eye View (BEV) detection, and full-scene grid occupancy prediction, enabling comprehensive multi-task output. Extensive experiments and ablation studies on the nuScenes dataset demonstrate that R$M^{2}$Occ significantly outperforms existing state-of-the-art methods, establishing a new paradigm for accurate and efficient multi-sensor fusion and multi-task perception in autonomous driving scenarios.

Vehicular crowdsensing (VCS) has aroused extensive attention because of its ability to provide comprehensive data services for intelligent transportation systems. Wherein, secure data transmission is a prerequisite for realizing the above benefits of VCS. Unfortunately, although many works on secure data sharing have been proposed, these schemes suffer from practical weaknesses such as data source authentication, malicious identity traceability, and inefficiency. In this paper, we propose a practical conditional anonymization and leakage-resilient authentication solution for vehicular crowdsensing communication (CALRA). Our proposal not only resists the leakage of sensitive information about vehicles but also realizes the authentication of data senders, while guaranteeing the integrity, authenticity, and confidentiality of data. Besides, CALRA exploits traceability technology to pursue malicious/illegal participants, thus avoiding participants’ accountability evasion caused by absolute anonymity. Furthermore, our CALRA solution delegates complex computational processes into an offline formulation to reduce computational and communication overheads. Finally, our scheme is proved to be secure and unforgeable through the random oracle model, and the performance evaluation illustrates that our CALRA proposal is superior and practical.

Road ponding, a prevalent traffic hazard, poses a serious threat to road safety by causing vehicles to lose control and leading to accidents ranging from minor fender benders to severe collisions. Existing technologies struggle to accurately identify road ponding due to complex road textures and variable ponding coloration influenced by reflection characteristics. To address this challenge, we propose a novel approach called Self-Attention-based Global Saliency-Enhanced Network (AGSENet) for proactive road ponding detection and traffic safety improvement. AGSENet incorporates saliency detection techniques through the Channel Saliency Information Focus (CSIF) and Spatial Saliency Information Enhancement (SSIE) modules. The CSIF module, integrated into the encoder, employs self-attention to highlight similar features by fusing spatial and channel information. The SSIE module, embedded in the decoder, refines edge features and reduces noise by leveraging correlations across different feature levels. To ensure accurate and reliable evaluation, we corrected significant mislabeling and missing annotations in the Puddle-1000 dataset. Additionally, we constructed the Foggy-Puddle and Night-Puddle datasets for road ponding detection in low-light and foggy conditions, respectively. Experimental results demonstrate that AGSENet outperforms existing methods, achieving IoU improvements of 2.03%, 0.62%, and 1.06% on the Puddle-1000, Foggy-Puddle, and Night-Puddle datasets, respectively, setting a new state-of-the-art in this field. Finally, we verified the algorithm’s reliability on edge computing devices. This work provides a valuable reference for proactive warning research in road traffic safety. The source code and datasets are placed in thehttps://github.com/Lyu-Dakang/AGSENet.

Recently, 3D object detection techniques based on the fusion of camera and LiDAR sensor modalities have received much attention due to their complementary capabilities. How-ever, prevalent multi-modal models are relatively homogeneous in terms of feature fusion strategies, making their performance being strictly limited to the detection results of one of the modalities. While the latest data-level fusion models based on virtual point clouds do not make further use of image features, resulting in a large amount of noise in depth estimation. To address the above issues, this paper integrates the advantages of data-level and feature-level sensor fusion, and proposes MLF3D, a 3D object detection based on multi-level fusion. MLF3D generates virtual point clouds to realize the data-level fusion, and implements feature-level fusion through two key designs: VIConv3D and ASFA. VIConv3D reduces the noise problem and realizes deep interactive enhancement of features through cross-modal fusion, noise sensing, and cross-space fusion. ASFA refines the bounding box by adaptively fusing cross-layer spatial semantic information. Our MLF3D achieves 92.91%, 87.71% AP and 85.25% AP in easy, medium and hard scenarios on the KITTI’s 3D Car Detection Leaderboard, realizing excellent performance.

Online High-definition map (HD-map) construction based on vehicle sensors has garnered widespread attention recently. While state-of-the-art methods achieve remarkable accuracy, most of them overlook the importance of inference speed and the inherent linear priors of map elements. Concretely, slow inference speed impacts the safety of autonomous vehicles, making it challenging for applications. Additionally, the absence of linear priors in map element predictions results in distorted or blurry outcomes. To address these issues, we propose E-MLP, an effortless online HD-map construction method that relies solely on camera sensors and incorporates the linear priors of map elements. Specifically, we first introduce a novel Principal Feature Analysis (PFA) module, designed to efficiently reduce the time cost of view transformation. Then, two thoughtfully crafted loss functions are introduced to incorporate the natural linear priors of map elements as constraints in the map construction process. Extensive experiments conducted on the nuScenes dataset revealed that, compared to the baseline method, our approach achieved a remarkable 34.9% increase in inference speed with virtually no loss in accuracy.

Traffic accidents are a significant factor leading to injuries and property losses, prompting extensive research in the field of traffic safety. However, previous studies, whether focused on static environment assessment, dynamic driving analysis, pre-accident prediction, or post-accident rule checks, have often been conducted independently. Our introduces V2X Environmental Perception Multi-modal Large Model AccidentGPT for accident analysis and prevention. AccidentGPT establishes a multi-modal information interaction framework based on multisensory perception. It adopts a holistic approach to address traffic safety issues, providing environmental perception for autonomous vehicles to avoid collisions and maintain control. In human-driven vehicles, it offers proactive safety warnings, blind spot alerts, and driving suggestions through human-machine dialogue. Additionally, it aids traffic police and management agencies in considering factors such as pedestrians, vehicles, roads, and the environment for intelligent real-time analysis of traffic safety. The system also conducts a thorough analysis of accident causes and post-accident liabilities, making it the first large-scale model to integrate comprehensive scene understanding into traffic safety research. Project page: https://accidentgpt.github.io

In this study, we introduce an AR-based Meta-Vehicle Road Collaboration Testing System (AR-MVRTs), a significant advancement in autonomous driving testing. This system utilizes vehicle-road collaboration, Augmented Reality (AR), and Metaverse technologies for high-risk scenario simulation and dynamic interaction between virtual data and actual vehicles and infrastructure, enhancing verification and optimization methods. The core contribution is the innovative framework and component model, integrating AR with vehicle-road collaboration technologies for a comprehensive environment that includes real autonomous vehicles, virtual scenarios, and parameterized test settings. The AR-MVRTs method efficiently generates critical testing scenarios, significantly improving testing efficiency. An implementation case shows the system’s practical application, where AR-MVRTs demonstrated a 658-fold efficiency increase, completing tests in 8.5 hours compared to 5580 hours required by traditional test matrices. This integration accelerates the transition from theoretical research to practical applications and offers deep insights into autonomous vehicle safety and reliability. This research promises to advance autonomous driving technology and lay a solid foundation for its future development.

Benefiting from unified sensors and long-term traffic engagement, for-hire vehicles (FHVs) are widely considered the mainstay for vehicular crowd sensing (VCS) tasks. However, incentivizing FHVs to participate in sensing tasks remains a fundamental challenge for FHV-enabled VCS systems: for one thing, the distribution diversity of orders and tasks limits FHVs from participating in VCS; for another, FHVs’ operating states determine whether they are free to execute sensing tasks. To address the above issues, this article proposes SHIP, a State-aware Hybrid Incentive Program for FHV-enabled VCS systems. Our proposal finely categorizes FHVs’ operating states and provides a hybrid incentive scheme that incorporates both opportunistic and participatory approaches. We also introduce coverage diversity to reflect the distribution of FHVs and sensing tasks. By combining coverage diversity with vehicle revenue, we establish a dynamic multi-objective optimization model to select appropriate FHVs for sensing to achieve a multi-win situation. Experiments based on real-world datasets show that our proposal can effectively utilize FHVs with different operating states to improve the quality of sensing tasks while increasing FHVs’ revenue.

The secure transmission of instructions among vehicles in a platoon is one of the most essential needs for a vehicle platoon. Despite the existence of cryptographic methods to securely share instructions, instruction sharing is still subject to forgery, tampering, and denial-of-service attacks. Therefore, it is urgent to find a solution to perform data source identification to filter out irrelevant information (not instructions) while ensuring the authenticity of encrypted instructions is urgent to address. In addition, immediate revocation of credentials is also a crucial requirement for a vehicle platoon when an authorized vehicle member misbehaves. In this paper, we propose the first Secure Source Identification Scheme for Revocable Instruction Sharing (SI-RIS) to securely simultaneously achieve bilateral fine-grained access control, data source identification, immediate vehicle user revocation, and efficient encryption in vehicle platoons. Specifically, our SI-RIS solution supports fine-grained access control for both the sender and receiver over the encrypted instructions. As a result, only authorized correspondents are able to access the commands. Furthermore, upon identification of malicious members in the platoon, our SI-RIS provides an efficient direct vehicle user revocation mechanism capable of immediate revocation credentials without affecting other vehicles. We prove the security of our SI-RIS via rigorous mathematical security proof. Moreover, performance evaluation and comparisons illustrate the feasibility and practicability of SI-RIS for vehicle platoon.

As an emerging field of communication, Vehicular Social Networks (VSNs) can reduce traffic congestion while enhancing road safety by sharing data among groups of commuters. In VSNs, Vehicular Cloud Server (VCS) based data sharing technology with encrypted primitives allows local users to outsource encrypted data for reducing the storage burden on the user side and sharing data without location restrictions. However, existing data encryption solutions that have been applied in VSNs environments still encounter weaknesses in efficiency, security, or privacy due to the following problems: (1) lack of effective access policies for flexible authorizing ciphertext to multiple data users; (2) data breaches caused by malicious data publishers; (3) necessity in hiding the private information of receivers. To date, no such solution has been available that securely enables one-to-many user authorization with privacy protection, while greatly resisting malicious data publishers. We propose a Sanitizable Access Control System with Policy-protection (SASP) for VSNs in this paper. Our SASP enables a sanitizer to test and sanitize encrypted data to defend against malicious data publishers, ensuring that the plaintext can only be recovered if an authorized user has a valid key. Furthermore, in our SASP system, the access policy is separated into attribute names and attribute values. Wherein, the attribute values contain a lot of private information, which is hidden in the ciphertext to guarantee data users’ privacy. Rigorous security analysis and performance evaluations demonstrate the practicality of SASP for VSNs.

Autonomous Vehicle Platooning (AVP) presents a promising approach to reducing energy expenses, improving traffic safety, and enhancing capacity. In multi-platoon vehicular networks, platoons can collaborate to ensure better data transfer reliability. However, the existing secure information-sharing methods between platoons face efficiency and security challenges. These challenges arise from (1) a lack of an efficient method to simultaneously ensure confidentiality, authenticity, and non-repudiation of the transmitted information; (2) difficulty in cross-domain dynamically sharing ciphertexts to multiple recipients is challenging. To overcome these challenges, we propose a Flexible Cross-domain Data Access Control (FC-DAC) system in this paper. Our FC-DAC system ensures confidentiality, authenticity, and non-repudiation of transmitted information while enabling high-efficiency ciphertext sharing between various platoons. Additionally, the FC-DAC solution allows vehicles with valid authorizations to access signcrypted information quickly and efficiently, even if they belong to different platoons. Our theoretical and experimental analysis demonstrates that the FC-DAC scheme is resistant to various common attacks and is applicable to multi-platoon vehicular networks.

Pure point-based neural networks have recently shown tremendous promise for point cloud tasks, including 3D object classification, 3D object part segmentation, 3D semantic segmentation, and 3D object detection. Nevertheless, it is a laborious process to construct a network for each task due to the artificial parameters and hyperparameters involved, e.g., the depths and widths of the network and the number of sampled points at each stage. In this work, we propose Auto-Points, a novel one-shot search framework that automatically seeks the optimal architecture configuration for point cloud tasks. Technically, we introduce a set abstraction mixer (SAM) layer that is capable of scaling up flexibly along the depth and width of the network. Each SAM layer consists of numerous child candidates, which simplifies architecture search and enables us to discover the optimum design for each point cloud task pursuant to resource constraint from an enormous search space. To fully optimize the child candidates, we develop a weight-entwinement neural architecture search (NAS) technique that entwines the weights of different candidates in the same layer during supernet training such that all candidates can be extremely optimized. Benefiting from the proposed techniques, the trained supernet allows the searched subnets to be exceptionally well-optimized without further retraining or finetuning. In particular, the searched models deliver superior performances on multiple extensively employed benchmarks, 93.9% overall accuracy (OA) on ModelNet40, 89.1% OA on ScanObjectNN, 87.1% instance average IoU on ShapeNetPart, 69.1% mIoU on S3DIS, 70.4% [email protected] on ScanNet V2, and 64.4% [email protected] on SUN RGB-D.

The success of deep learning is largely attributed to the training over clean data. However, data is often coupled with noisy labels in practice. Learning with noisy labels is challenging because the performance of the deep neural networks (DNN) drastically degenerates, due to confirmation bias caused by the network memorization over noisy labels. To alleviate that, a recent prominent direction is on sample selection, which retrieves clean data samples from noisy samples, so as to enhance the model's robustness and tolerance to noisy labels. In this paper, we re-vamp the sample selection from the perspective of optimal transport theory and propose a novel method, called the OT-Filter. The OT-Filter provides geometrically meaningful distances and preserves distribution patterns to measure the data discrepancy, thus alleviating the confirmation bias. Extensive experiments on benchmarks, such as Clothing1M and ANIMAL-10N, show that the performance of the OT-Filter outperforms its counterparts. Meanwhile, results on benchmarks with synthetic labels, such as CIFAR-10/100, show the superiority of the OT-Filter in handling data labels of high noise.

Vehicular platooning is emerging as a promising method that can enhance road utilization, alleviate traffic congestion, and even decrease energy expenditure by shortening the distance between vehicles in the platoon. In platooning, a platoon leader (PL) is required to communicate with platoon members (PMs) to issue instructions. In this way, the PMs are simply expected to follow the command and enjoy their free time. However, when the same instruction is sent to different PMs in the form of single-hop unicast, the ciphertext will go up as the quantity of PMs in the platoon resulting in a dramatic increase in transmission time. Moreover, transmission instructions without security and authentication guarantees are easily intercepted, forged, or deleted. Therefore, in this article, we propose broadcast signcryption scheme for platoon communication (BSPC), a broadcast signcryption scheme for platoon communication based on identity. In our BSPC, with only one signcryption, the PL can generate a public verifiable ciphertext with fixed-length for PMs, while employing broadcast functionality to deliver the ciphertext to multiple PMs at once. In such a manner, the confidentiality, integrity, and authentication of transmitted commands are ensured, such that malicious vehicles cannot manipulate data and unauthorized vehicles have no way to access it. Besides, the instructions' transmit time is greatly reduced. Our BSPC proposal is proved to be secure and unforgeable through rigorous analysis. Furthermore, simulation results demonstrate our BSPC is practical and efficient for platoon communication.

The rapid development of connected automatic vehicle (CAV) technology makes vehicularad hocnetworks (VANETs) an urgently needed research field. It includes vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) message flows. A roadside unit (RSU) is an important infrastructure for V2I communication and provides roadside information services for CAVs. However, an unoptimal RSU deployment may result in RSUs failing to improve the efficiency of VANETs and compromising the capability of service to most vehicles. Motivated by this observation, this study focuses on balancing the two objectives of efficiency and coverage and establishing an RSU deployment strategy based on traffic demand. In detail, this model optimizes both the average data delivery delay in VANETs and the number of vehicles covered by RSUs. The effectiveness of the method is verified by simulation in a 4 km${\times }4$km virtual road network. We also found that: 1) if 25% of the road segments in the road network are covered by RSUs, most vehicles can be served, and the delay of VANETs can be reduced; 2) compared with the road network with low traffic demand, more RSUs need to be deployed in the road network with high traffic demand to achieve the same effect; and 3) early RSU investment is more cost effective. Our method can provide a reference for the areas where RSU investments should be made and the priority of the areas.

Autonomous Vehicle Platoon (AVP) is conceived as a promising solution to enhance the traffic capacity and reduce the energy consumption in the intelligent transportation system. Nevertheless, AVP without security guarantees are prone to various attacks, which probably lead to life-threatening accidents. Motivated by solving this issue, an outsourced attribute-based access control mechanism with direct revocation for AVP (RFAP) is introduced in this paper. Among them, attribute-based encryption is utilized to implement fine-grained access control during the encryption process. Furthermore, RFAP can not only realize the immediate revocation of platoon member who is about to leave the platoon without affecting others, but also achieve secure outsourced decryption with the help of edge computing units for minimizing the computational overhead of decryption on the vehicle side. Security analysis and simulation results indicate that our RFAP mechanism is practical in aspects of security and efficiency.

In this paper, we present an adaptive signal control scheme to prevent intersection traffic blockage resulted from vehicle queue spillover. A method to identify vehicle queue spillover condition through simplified shockwave analysis is developed. Instead of measuring the vehicle queue length or locating the end of queue directly, this method relies on the vehicle speed which is more feasible to measure in practice. The adaptive traffic signal control scheme is designed to prevent potential intersection traffic blockage, and adaptively allocates green time to appropriate signal phases. At the end, a simulation study is carried out to evaluate the proposed adaptive control scheme. The results show that the scheme can effectively prevent intersection traffic blockage and significantly improve the performance of the intersection in terms of vehicle delay.