Intelligent Vehicle Communication and Obstacle Detection Based on Millimetre Wave Radar Base Station

Zhao, Jingcheng; Lou, Changyu; Hao, Hai

doi:10.1109/iwcmc51323.2021.9498843

Cited by 2 publications

(1 citation statement)

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“…Obstacle detection is a very important part of the realization of intelligent vehicles. To avoid the problem that metal objects seriously block the millimeter wave, an active obstacle detection method based on a millimeter-wave radar base station is proposed [35]. The radar and communication integrated system (RCIS) can overcome the time-consuming problems of data format transfer and complex data fusion across multiple sensors in autonomous driving vehicles (ADVs) [36].…”

Section: Radar Sensing and Communication In The Iovmentioning

confidence: 99%

Research on task offloading optimization strategies for vehicular networks based on game theory and deep reinforcement learning

Wang,

Zhou,

et al. 2023

Front. Phys.

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With the continuous development of the 6G mobile network, computing-intensive and delay-sensitive onboard applications generate task data traffic more frequently. Particularly, when multiple intelligent agents are involved in tasks, limited computational resources cannot meet the new Quality of Service (QoS) requirements. To provide a satisfactory task offloading strategy, combining Multi-Access Edge Computing (MEC) with artificial intelligence has become a potential solution. In this context, we have proposed a task offloading decision mechanism (TODM) based on cooperative game and deep reinforcement learning (DRL). A joint optimization problem is presented to minimize both the overall task processing delay (OTPD) and overall task energy consumption (OTEC). The approach considers task vehicles (TaVs) and service vehicles (SeVs) as participants in a cooperative game, jointly devising offloading strategies to achieve resource optimization. Additionally, a proximate policy optimization (PPO) algorithm is designed to ensure robustness. Simulation experiments confirm the convergence of the proposed algorithm. Compared with benchmark algorithms, the presented scheme effectively reduces delay and energy consumption while ensuring task completion.

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Section: Radar Sensing and Communication In The Iovmentioning

confidence: 99%