A Pathway Forward: The Evolution of Intelligent Vehicles Research on IEEE T-IV

Wang, Haoran; Chen, Leiming; Li, Xuan; Wu, Xingtang; Hu, Jia; Chen, Yuanyuan; Liu, Zhongmin; Liu, Ying

doi:10.1109/tiv.2022.3215784

Cited by 15 publications

(4 citation statements)

References 33 publications

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“…Along with the rapid growth of IEEE Transactions on Intelligent Vehicles since the beginning of 2022 [13], we shared a call for papers to the research community about a Special Issue (SI) on "Digital Twins and Parallel Intelligence for Intelligent Vehicles and Intelligent Transportation Systems", soliciting articles with the latest research and development advances in DTPI and their applications in IV and ITS. Among 81 submissions to this SI from researchers around the world, 18 top-quality articles were selected for final publication on our journal.…”

Section: Digital Twins and Parallel Intelligence For Ivsmentioning

confidence: 99%

A New Era of Intelligent Vehicles and Intelligent Transportation Systems: Digital Twins and Parallel Intelligence

Wang

2023

IEEE Trans. Intell. Veh.

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r By March 25, our transaction has received a total of 619 original submissions. Our current average number of submissions per day (SPD) rate is 7.37. Given this SPD, I would like to expand our editorial board to include at least 120 members to ensure the review quality of services.r I would like to share my philosophy on reviewing assignments for submissions. First, I would assign papers to AEs without assignments and with less than six assignments for the year. Second, I will consider their fields of expertise. In this way, I would like to push our AEs, especially the senior ones, to go out of their own comfort zones for paper reviewing. Why? Technology, especially intelligent technology, changes so fast, we must be prepared to process papers that are outside our specialties at TIV or any other academic publications.r On January 14, 2023, I reported the state of IEEE TIV as well as its future directions to IEEE ITSS Publication Board, received very positive feedback, and then sent the annual report to the TIV Steering Committee (SC). On March 4th, I received a notice from the SC for a meeting on March 13th. The only action item from the SC meeting was

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Section: Digital Twins and Parallel Intelligence For Ivsmentioning

confidence: 99%

A New Era of Intelligent Vehicles and Intelligent Transportation Systems: Digital Twins and Parallel Intelligence

Wang

2023

IEEE Trans. Intell. Veh.

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“…7. Similarly, Wang et al [112] also borrow the GCN to model the interaction of objects in driving scenes, where a spatial-temporal continuity is learned for the edge weight updating in accident anticipation. The frame-level accident score a t for each time can be computed by…”

Section: Vision-taa With Object Feature Interactionmentioning

confidence: 99%

“…where τ is the time interval of Time-to-Accident (TTA) from the current time, and r is the frame rate. The ExpLoss is widely used in other accident anticipation works [119], [122], [94], [110], [112]. Suzuki et al [124] extend the ExpLoss further and propose the Loss of Early Anticipation (LEA) and Adaptive Loss for Early Anticipation (AdaLEA).…”

Section: Loss Functionsmentioning

confidence: 99%

Traffic Accident Detection via Self-Supervised Consistency Learning in Driving Scenarios

Fang

Qiao²,

Bai³

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Traffic accident detection and anticipation is an obstinate road safety problem and painstaking efforts have been devoted. With the rapid growth of video data, Vision-based Traffic Accident Detection and Anticipation (named Vision-TAD and Vision-TAA) become the last one-mile problem for safe driving and surveillance safety. However, the long-tailed, unbalanced, highly dynamic, complex, and uncertain properties of traffic accidents form the Out-of-Distribution (OOD) feature for Vision-TAD and Vision-TAA. Current AI development may focus on these OOD but important problems. What has been done for Vision-TAD and Vision-TAA? What direction we should focus on in the future for this problem? A comprehensive survey is important. We present the first survey on Vision-TAD in the deep learning era and the first-ever survey for Vision-TAA. The pros and cons of each research prototype are discussed in detail during the investigation. In addition, we also provide a critical review of 31 publicly available benchmarks and related evaluation metrics. Through this survey, we want to spawn new insights and open possible trends for Vision-TAD and Vision-TAA tasks.

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“…Emerging connected technologies enable information sharing among vehicles and infrastructure through vehicle-to-everything (V2X) communications in a connected traffic environment [ 11 , 12 , 13 , 14 , 15 ], facilitating more informed collaborations among connected vehicles (CVs) in the re-routing process [ 16 , 17 , 18 ]. Li et al [ 19 ] proposed a routing method facilitating the navigation through passing time windows in a connected vehicle environment; this has informed the direction of our study.…”

Section: Introductionmentioning

confidence: 99%

Incentive Mechanism for Privacy-Preserving Collaborative Routing Using Secure Multi-Party Computation and Blockchain

Wang,

Peeta

2024

Sensors

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Traffic congestion results from the spatio-temporal imbalance of demand and supply. With the advances in connected technologies, incentive mechanisms for collaborative routing have the potential to provide behavior-consistent solutions to traffic congestion. However, such mechanisms raise privacy concerns due to their information-sharing and execution-validation procedures. This study leverages secure Multi-party Computation (MPC) and blockchain technologies to propose a privacy-preserving incentive mechanism for collaborative routing in a vehicle-to-everything (V2X) context, which consists of a collaborative routing scheme and a route validation scheme. In the collaborative routing scheme, sensitive information is shared through an off-chain MPC protocol for route updating and incentive computation. The incentives are then temporarily frozen in a series of cascading multi-signature wallets in case vehicles behave dishonestly or roadside units (RSUs) are hacked. The route validation scheme requires vehicles to create position proofs at checkpoints along their selected routes with the assistance of witness vehicles using an off-chain threshold signature protocol. RSUs will validate the position proofs, store them on the blockchain, and unfreeze the associated incentives. The privacy and security analysis illustrates the scheme’s efficacy. Numerical studies reveal that the proposed incentive mechanism with tuned parameters is both efficient and implementable.

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A Pathway Forward: The Evolution of Intelligent Vehicles Research on IEEE T-IV

Cited by 15 publications

References 33 publications

A New Era of Intelligent Vehicles and Intelligent Transportation Systems: Digital Twins and Parallel Intelligence

A New Era of Intelligent Vehicles and Intelligent Transportation Systems: Digital Twins and Parallel Intelligence

Traffic Accident Detection via Self-Supervised Consistency Learning in Driving Scenarios

Incentive Mechanism for Privacy-Preserving Collaborative Routing Using Secure Multi-Party Computation and Blockchain

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