Object Detection with Deep Neural Networks for Reinforcement Learning in the Task of Autonomous Vehicles Path Planning at the Intersection

D. A. Yudin, A. Skrynnik, A. Krishtopik, I. Belkin, A. I. Panov

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

Abstract: Among a number of problems in the behavior planning of an unmanned vehicle the central one is movement in difficult areas. In particular, such areas are intersections at which direct interaction with other road agents takes place. In our work, we offer a new approach to train of the intelligent agent that simulates the behavior of an unmanned vehicle, based on the integration of reinforcement learning and computer vision. Using full visual information about the road intersection obtained from aerial photographs, it is studied automatic detection the relative positions of all road agents with various architectures of deep neural networks (YOLOv3, Faster R-CNN, RetinaNet, Cascade R-CNN, Mask R-CNN, Cascade Mask R-CNN). The possibilities of estimation of the vehicle orientation angle based on a convolutional neural network are also investigated. Obtained additional features are used in the modern effective reinforcement learning methods of Soft Actor Critic and Rainbow, which allows to accelerate the convergence of its learning process. To demonstrate the operation of the developed system, an intersection simulator was developed, at which a number of model experiments were carried out.

Original languageEnglish
Pages (from-to)283-295
Number of pages13
JournalOptical Memory and Neural Networks (Information Optics)
Volume28
Issue number4
DOIs
Publication statusPublished - 1 Oct 2019
Externally publishedYes

Keywords

  • behavior control
  • computer vision
  • deep neural network
  • intersection simulator
  • reinforcement learning
  • vehicle recognition

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