Risk Estimation System Using Driver Model Based on Bayesian Networks

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  • IGA Hiroaki
    Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology
  • HATAKEYAMA Yutaka
    Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology
  • TOU Houen
    Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology
  • TAKAHASHI Hiroshi
    Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology
  • HIROTA Kaoru
    Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology

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Other Title
  • ベイジアンネットに基づくドライバモデルを利用した危険度推定システム
  • ベイジアン ネット ニ モトズク ドライバ モデル オ リヨウシタ キケンド スイテイ システム

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Abstract

A risk estimation system based on Bayesian Networks using a driver model is proposed to achieve a collision evasion function and a risk estimation function in traffic situations for safe driving support systems. The proposed system uses input traffic and vehicle information to evaluate subsequent driving operations, such as acceleration and steering, using Bayesian networks. The vehicle trajectory is then forecasted using a dynamical physical model. Next, the risk of collision with other vehicles is calculated based on the output probabilities of the Bayesian network and the predictions of the dynamical model by considering the trajectories of other vehicles and the possible trajectories of the car itself. In the scene of the intersection, the effectiveness of the proposed system is shown by comparing the simulations for the scene where the accident occurs and for the scene where it does not, and then specifying the difference between the risk transition coefficients for both cases. Also, it is shown that the risk decreases after performing an evasive action. In addition, the proposed system is applied to a real environment data. In the future, the proposed system can be applied to a system that prevents traffic accidents by giving the optimal evasion driving operation to the automatic control system of the vehicle.

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