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Communication Scheduling by Deep Reinforcement Learning for Remote Traffic State Estimation with Bayesian Inference

Peng, Bile, 1985 (author)
Technische Universität Braunschweig
Xie, Yuhang (author)
Technische Universität Braunschweig
Seco-Granados, G. (author)
Universitat Autonoma de Barcelona (UAB)
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Wymeersch, Henk, 1976 (author)
Chalmers tekniska högskola,Chalmers University of Technology
Jorswieck, Eduard A. (author)
Technische Universität Braunschweig
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 (creator_code:org_t)
2022
2022
English.
In: IEEE Transactions on Vehicular Technology. - 0018-9545 .- 1939-9359. ; 71:4, s. 4287-4300
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • 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.

Subject headings

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)

Keyword

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

Publication and Content Type

art (subject category)
ref (subject category)

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