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Communication Sched...
Communication Scheduling by Deep Reinforcement Learning for Remote Traffic State Estimation with Bayesian Inference
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- Peng, Bile, 1985 (författare)
- Technische Universität Braunschweig
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- Xie, Yuhang (författare)
- Technische Universität Braunschweig
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- Seco-Granados, G. (författare)
- Universitat Autonoma de Barcelona (UAB)
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- Wymeersch, Henk, 1976 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Jorswieck, Eduard A. (författare)
- Technische Universität Braunschweig
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(creator_code:org_t)
- 2022
- 2022
- Engelska.
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Ingår i: IEEE Transactions on Vehicular Technology. - 0018-9545 .- 1939-9359. ; 71:4, s. 4287-4300
- Relaterad länk:
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https://research.cha... (primary) (free)
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https://research.cha...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Traffic awareness is the prerequisite of autonomous driving. Given the limitation of on-board sensors (e.g., precision and price), remote measurement from either infrastructure or other vehicles can improve traffic safety. However, the wireless communication carrying the measurement result undergoes fading, noise and interference and has a certain probability of outage. When the communication fails, the vehicle state can only be predicted by Bayesian filtering with a low precision. Higher communication resource utilization (e.g., transmission power) reduces the outage probability and hence results in an improved estimation precision. The power control subject to an estimate variance constraint is a difficult problem due to the complicated mapping from transmit power to vehicle-state estimate variance. In this paper, we develop an estimator consisting of several Kalman filters (KFs) or extended Kalman filters (EKFs) and an interacting multiple model (IMM) to estimate and predict the vehicle state. We propose to apply deep reinforcement learning (DRL) for the transmit power optimization. In particular, we consider an intersection and a lane-changing scenario and apply proximal policy optimization (PPO) and soft actor-critic (SAC) to train the DRL model. Testing results show satisfactory power control strategies confining estimate variances below given threshold. SAC achieves higher performance compared to PPO.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Telekommunikation (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Telecommunications (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
Nyckelord
- Optimization
- Estimation
- Autonomous driving
- Bayesian filtering
- Noise measurement
- resource allocation
- deep reinforcement learning
- interacting multiple model
- Vehicle dynamics
- Bayes methods
- Time measurement
- Sensors
- proximal policy optimization
- soft actorcritic
- power control
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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