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Driver's Foot Trajectory Tracking for Safe Maneuverability Using New Modified reLU-BiLSTM Deep Neural Network

Conference Paper


Abstract


  • Driver's foot behaviour is unpredictable and can suddenly change the nature of driving and dynamics under the influence of different factors that stimulates the driving style. Such effects result in sudden variations in foot dynamics and trajectory between accelerator and brake pedals inducing vagueness in smart active control system. This paper is an extension to the intrusive approach where driver's foot trajectory and shifting between pedals are monitored using XSENS motion capture system. The main objective is to predict the foot patterns associated with acceleration and braking. The experiments were conducted on 10 young subjects on MATHWORKS driver-in-loop (DIL) simulator, interfaced with Unreal Engine 4 studio. A new modified bidirectional long short-term memory (Bi-LSTM) deep neural network based on a rectified linear unit layer was designed, trained, tested and compared with traditional machine learning algorithms on 3D time-series foot orientation data for the sequence-to-sequence classification. The results show that the proposed classifier performs well and successfully recognizes the driver's foot behaviour with overall accuracy of 99.8%. Such identified patterns will help in determining the foot posture and the degree of intention in pressing the particular pedal. Moreover, the patterns will be useful for early intervention by smart systems to cope with the longitudinal mistakes made during driving. The limitations of the current work and directions for future work are explored.

Publication Date


  • 2020

Citation


  • Ansari, S., Du, H., & Naghdy, F. (2020). Driver's Foot Trajectory Tracking for Safe Maneuverability Using New Modified reLU-BiLSTM Deep Neural Network. In IEEE Transactions on Systems, Man, and Cybernetics: Systems Vol. 2020-October (pp. 4392-4397). doi:10.1109/SMC42975.2020.9283169

Scopus Eid


  • 2-s2.0-85098843259

Web Of Science Accession Number


Start Page


  • 4392

End Page


  • 4397

Volume


  • 2020-October

Abstract


  • Driver's foot behaviour is unpredictable and can suddenly change the nature of driving and dynamics under the influence of different factors that stimulates the driving style. Such effects result in sudden variations in foot dynamics and trajectory between accelerator and brake pedals inducing vagueness in smart active control system. This paper is an extension to the intrusive approach where driver's foot trajectory and shifting between pedals are monitored using XSENS motion capture system. The main objective is to predict the foot patterns associated with acceleration and braking. The experiments were conducted on 10 young subjects on MATHWORKS driver-in-loop (DIL) simulator, interfaced with Unreal Engine 4 studio. A new modified bidirectional long short-term memory (Bi-LSTM) deep neural network based on a rectified linear unit layer was designed, trained, tested and compared with traditional machine learning algorithms on 3D time-series foot orientation data for the sequence-to-sequence classification. The results show that the proposed classifier performs well and successfully recognizes the driver's foot behaviour with overall accuracy of 99.8%. Such identified patterns will help in determining the foot posture and the degree of intention in pressing the particular pedal. Moreover, the patterns will be useful for early intervention by smart systems to cope with the longitudinal mistakes made during driving. The limitations of the current work and directions for future work are explored.

Publication Date


  • 2020

Citation


  • Ansari, S., Du, H., & Naghdy, F. (2020). Driver's Foot Trajectory Tracking for Safe Maneuverability Using New Modified reLU-BiLSTM Deep Neural Network. In IEEE Transactions on Systems, Man, and Cybernetics: Systems Vol. 2020-October (pp. 4392-4397). doi:10.1109/SMC42975.2020.9283169

Scopus Eid


  • 2-s2.0-85098843259

Web Of Science Accession Number


Start Page


  • 4392

End Page


  • 4397

Volume


  • 2020-October