Deep Learning-Based Raindrop Quantity Detection for Real-Time Vehicle-Safety Application

Wang, Szu-Hong; Hsia, Shih-Chang; Zheng, Mengjie

doi:10.1109/tce.2021.3127494

Cited by 12 publications

(1 citation statement)

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“…Several studies have been studied on detecting technologies in intelligent transportation systems (ITSs) [17], such as raindrop quantity detection [18] and driver fatigue detection [19]. Some researchers studied predicting technologies for vehicle status, such as hybrid velocity-profile prediction [20] and acceleration prediction [21].…”

Section: Introductionmentioning

confidence: 99%

Enabling Digital Twin in Vehicular Edge Computing: A Multi-Agent Multi-Objective Deep Reinforcement Learning Solution

Xu¹,

Li²,

Dai³

et al. 2022

Preprint

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With recent advances in sensing technologies, wireless communications, and computing paradigms, traditional vehicles are evolving to electronic consumer products, driving the research on digital twins in vehicular edge computing (DT-VEC). This paper makes the first attempt to achieve the quality-cost tradeoff in DT-VEC. First, a DT-VEC architecture is presented, where the heterogeneous information can be sensed by vehicles and uploaded to the edge node via vehicle-to-infrastructure (V2I) communications. The DT-VEC are modeled at the edge node, forming a logical view to reflect the physical vehicular environment. Second, we model the DT-VEC by deriving an ISAC (integrated sensing and communication)-assisted sensing model and a reliability-guaranteed uploading model. Third, we define the quality of DT-VEC by considering the timeliness and consistency, and define the cost of DT-VEC by considering the redundancy, sensing cost, and transmission cost. Then, a biobjective problem is formulated to maximize the quality and minimize the cost. Fourth, we propose a multi-agent multiobjective (MAMO) deep reinforcement learning solution implemented distributedly in the vehicles and the edge nodes. Specifically, a dueling critic network is proposed to evaluate the advantage of action over the average of random actions. Finally, we give a comprehensive performance evaluation, demonstrating the superiority of the proposed MAMO.

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