Journal of Auto-vehicle Safety Association (자동차안전학회지)

Korean Auto-vehicle Safety Association (한국자동차안전학회)
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Attention-LSTM based Lane Change Possibility Decision Algorithm for Urban Autonomous Driving

도심 자율주행을 위한 어텐션-장단기 기억 신경망 기반 차선 변경 가능성 판단 알고리즘 개발

  • 이희성 (서울대학교 공과대학 기계공학부) ;
  • 이경수 (서울대학교 공과대학 기계공학부)
  • Received : 2022.06.06
  • Accepted : 2022.08.01
  • Published : 2022.09.30
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Abstract

Lane change in urban environments is a challenge for both human-driving and automated driving due to their complexity and non-linearity. With the recent development of deep-learning, the use of the RNN network, which uses time series data, has become the mainstream in this field. Many researches using RNN show high accuracy in highway environments, but still do not for urban environments where the surrounding situation is complex and rapidly changing. Therefore, this paper proposes a lane change possibility decision network by adopting Attention layer, which is an SOTA in the field of seq2seq. By weighting each time step within a given time horizon, the context of the road situation is more human-like. A total 7D vectors of x, y distances and longitudinal relative speed of side front and rear vehicles, and longitudinal speed of ego vehicle were used as input. A total 5,614 expert data of 4,098 yield cases and 1,516 non-yield cases were used for training, and the performance of this network was tested through 1,817 data. Our network achieves 99.641% of test accuracy, which is about 4% higher than a network using only LSTM in an urban environment. Furthermore, it shows robust behavior to false-positive or true-negative objects.

Keywords

Acknowledgement

본 논문은 산업통상자원부 자율주행기술개발혁신사업(20018101, TCar 기반 자율주행 인지예측지능제어 차량부품시스템 통합평가 기술개발)의 지원을 받아 수행하였습니다.

References

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