Spatial Attention for Autonomous Decision-making in Highway Scene

Shuwei Zhang, Yutian Wu, Harutoshi Ogai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Automated decision making is still a significant challenge to realize fully autonomous driving. A common method that encoding surrounding vehicles in a grid map is used to describe observation space for decision making algorithm. It preserves vehicles spatial characteristics. But commonly in human driving, distinct position and speed surrounding vehicles contribute differently to make decision. We introduce a spatial attention module to calculate weights for each vehicle and integrate the attention mechanism into Deep Q network to make decision actions. The agent, ego vehicle, is trained in a simulated highway environment. Simulation results show the proposed method can get significant performance gains compared with other deep reinforcement learning methods by using two kinds of metrics.

Original languageEnglish
Title of host publication2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1435-1440
Number of pages6
ISBN (Electronic)9781728110899
Publication statusPublished - 2020 Sep 23
Event59th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2020 - Chiang Mai, Thailand
Duration: 2020 Sep 232020 Sep 26

Publication series

Name2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2020

Conference

Conference59th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2020
CountryThailand
CityChiang Mai
Period20/9/2320/9/26

Keywords

  • decision making
  • deep reinforcement learning
  • highway
  • spatial attention

ASJC Scopus subject areas

  • Control and Optimization
  • Instrumentation
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Decision Sciences (miscellaneous)
  • Industrial and Manufacturing Engineering

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