As an emerging paradigm for urban sensing, vehicular crowd sensing (VCS) has received increasing attention in recent years. Unlike traditional sensing paradigms, VCS leverages ubiquitous connected vehicles (CVs) and diverse onboard sensors to efficiently collect city-scale data. Despite the considerable benefits of CVs, the fast-changing traffic environment and attendant human and social factors bring significant complexity to the VCS system and make it a typical cyber-physical-social system (CPSS), followed by the challenge of robust and efficient modeling of VCS systems. To cope with the complexity of social dimensions and optimize the decision-making process in the physical VCS, this article introduces the artificial societies, computational experiments, and the parallel execution (ACP) approach to the VCS system and develops a novel framework called parallel VCS (P-VCS). Three key components empower P-VCS to balance the physical environment, cyber networks, and human and social factors, namely, an artificial system that is used to parametrically describe the physical VCS, two types of computational experiments that simulate the decision process and evaluate different strategies, and the parallel execution mechanism that is used to characterize the system operation. To demonstrate the feasibility of the framework, we take participant selection under traffic events as an application example. Experimental results illustrate that the P-VCS-based parallel learning strategy maintains competitive performance in all cases.

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.

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.

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 Vehicular networks, Intelligent Connected Vehicles (ICVs) perform data exchange and sharing with others via wireless channels for comfortable and environment-friendly driving. Since the open nature of wireless channels, it is necessary to encrypt sensitive data before sharing to ensure data security. However, existing efforts for secure data sharing still encounter efficiency and security bottle-neck because of the following issues: 1) absence of an effective policy to cross-system dynamically assign ciphertexts to multiple recipients. 2) Difficulty in hindering data disclosure resulting from malicious behaviors of a malicious data publisher. With the motivation to address the above issues simultaneously, in this article, we propose a sanitizable cross-system ciphertext authorization scheme (SI-APRE) for secure communication in ICVs. Our SI-APRE enables the conversion of the ciphertext generated with the original recipient's identity into a new ciphertext bound to the access policy, so that other new authorized recipients can also access the raw data. Meanwhile, our SI-APRE can check and sanitize the original ciphertext to prevent malicious publishers from encrypting data in a harmful way. Experimental and theoretical analysis demonstrate that our SI-APRE solution enjoys impressive performance regarding efficiency, which makes it more feasible and practical in reality.