Vehicle Tracking in Wireless Sensor Networks via Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2002.09671v1
• Date: Sat, 22 Feb 2020 10:01:49 GMT
• Title: Vehicle Tracking in Wireless Sensor Networks via Deep Reinforcement Learning
• Authors: Jun Li, Zhichao Xing, Weibin Zhang, Yan Lin, and Feng Shu
• Abstract summary: Decentralized vehicle tracking strategy is conceived for improving both tracking accuracy and energy saving. Two deep reinforcement learning (DRL) aided solutions are proposed relying on the dynamic selection of the activation area radius.
• Score: 15.252556567830215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vehicle tracking has become one of the key applications of wireless sensor networks (WSNs) in the fields of rescue, surveillance, traffic monitoring, etc. However, the increased tracking accuracy requires more energy consumption. In this letter, a decentralized vehicle tracking strategy is conceived for improving both tracking accuracy and energy saving, which is based on adjusting the intersection area between the fixed sensing area and the dynamic activation area. Then, two deep reinforcement learning (DRL) aided solutions are proposed relying on the dynamic selection of the activation area radius. Finally, simulation results show the superiority of our DRL aided design.

Related papers

• Leveraging Event Streams with Deep Reinforcement Learning for End-to-End UAV Tracking [1.8297494098768172]
  We present our proposed approach for active tracking to increase the autonomy of Unmanned Aerial Vehicles (UAVs) using event cameras, low-energy imaging sensors. The proposed tracking controller is designed to respond to visual feedback from the mounted event sensor, adjusting the drone movements to follow the target.
  arXiv  Detail & Related papers  (2024-10-03T07:56:40Z)
• Enhancing Track Management Systems with Vehicle-To-Vehicle Enabled Sensor Fusion [0.0]
  This paper proposes a novel Vehicle-to-Vehicle (V2V) enabled track management system. The core innovation lies in the creation of independent priority track lists, consisting of fused detections validated through V2V communication. The proposed system considers the implications of falsification of V2X signals which is combated through an initial vehicle identification process using detection from perception sensors.
  arXiv  Detail & Related papers  (2024-04-26T20:54:44Z)
• FENet: Focusing Enhanced Network for Lane Detection [0.0]
  This research pioneers networks augmented with Focusing Sampling, Partial Field of View Evaluation, Enhanced FPN architecture and Directional IoU Loss. Experiments demonstrate our Focusing Sampling strategy, emphasizing vital distant details unlike uniform approaches. Future directions include collecting on-road data and integrating complementary dual frameworks to further breakthroughs guided by human perception principles.
  arXiv  Detail & Related papers  (2023-12-28T17:52:09Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• Reinforcement Learning Approaches for Traffic Signal Control under Missing Data [5.896742981602458]
  In real-world urban scenarios, missing observation of traffic states may frequently occur due to the lack of sensors. We propose two solutions: the first one imputes the traffic states to enable adaptive control, and the second one imputes both states and rewards to enable adaptive control and the training of RL agents.
  arXiv  Detail & Related papers  (2023-04-21T03:26:33Z)
• NVRadarNet: Real-Time Radar Obstacle and Free Space Detection for Autonomous Driving [57.03126447713602]
  We present a deep neural network (DNN) that detects dynamic obstacles and drivable free space using automotive RADAR sensors. The network runs faster than real time on an embedded GPU and shows good generalization across geographic regions.
  arXiv  Detail & Related papers  (2022-09-29T01:30:34Z)
• SRRT: Exploring Search Region Regulation for Visual Object Tracking [58.68120400180216]
  We propose a novel tracking paradigm, called Search Region Regulation Tracking (SRRT) SRRT applies a proposed search region regulator to estimate an optimal search region dynamically for each frame. On the large-scale LaSOT benchmark, SRRT improves SiamRPN++ and TransT with absolute gains of 4.6% and 3.1% in terms of AUC.
  arXiv  Detail & Related papers  (2022-07-10T11:18:26Z)
• A Deep Reinforcement Learning Approach for Traffic Signal Control Optimization [14.455497228170646]
  Inefficient traffic signal control methods may cause numerous problems, such as traffic congestion and waste of energy. This paper first proposes a multi-agent deep deterministic policy gradient (MADDPG) method by extending the actor-critic policy gradient algorithms.
  arXiv  Detail & Related papers  (2021-07-13T14:11:04Z)
• Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles with Joint Radar-Data Communications [69.24726496448713]
  We propose an intelligent optimization framework based on the Markov Decision Process (MDP) to help the AV make optimal decisions. We then develop an effective learning algorithm leveraging recent advances of deep reinforcement learning techniques to find the optimal policy for the AV. We show that the proposed transferable deep reinforcement learning framework reduces the obstacle miss detection probability by the AV up to 67% compared to other conventional deep reinforcement learning approaches.
  arXiv  Detail & Related papers  (2021-05-28T08:45:37Z)
• Complex-valued Convolutional Neural Networks for Enhanced Radar Signal Denoising and Interference Mitigation [73.0103413636673]
  We propose the use of Complex-Valued Convolutional Neural Networks (CVCNNs) to address the issue of mutual interference between radar sensors. CVCNNs increase data efficiency, speeds up network training and substantially improves the conservation of phase information during interference removal.
  arXiv  Detail & Related papers  (2021-04-29T10:06:29Z)
• Object Tracking through Residual and Dense LSTMs [67.98948222599849]
  Deep learning-based trackers based on LSTMs (Long Short-Term Memory) recurrent neural networks have emerged as a powerful alternative. DenseLSTMs outperform Residual and regular LSTM, and offer a higher resilience to nuisances. Our case study supports the adoption of residual-based RNNs for enhancing the robustness of other trackers.
  arXiv  Detail & Related papers  (2020-06-22T08:20:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.