Driver`s Steering Behaviour Identification and Modelling in Near Rear-End collision

Hassan, N.S.; Zamzuri, Hairi; Wahid, Nurbaiti; Zulkepli, Khairul Akmal; Azmi, Muhammad Zulfaqar

doi:10.12928/telkomnika.v15i1.6133

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…29 Its advantage is that it requires less training time during a modeling process. 30 In the LM algorithm, the training process optimizes the weights through iterations based on the input-output time series. 28 The back propagation (BP) algorithm consisted of the forward and backward paths.…”

Section: Time Delay Neural Network Modelingmentioning

confidence: 99%

Modeling of occupant’s head movement behavior in motion sickness study via time delay neural network

et al. 2019

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Passengers are more susceptible to experiencing motion sickness (MS) than drivers. The difference in the severity of MS is due to their different head movement behavior during curve driving. When negotiating a curve, the passengers tilt their heads towards the lateral acceleration direction while the drivers tilt their heads against it. Thus, to reduce the passengers’ level of MS, they need to reduce their head’s tilting angle towards the lateral acceleration direction. Designing MS minimization strategies is easier if the correlation between the head movement and lateral acceleration is known mathematically. Therefore, this paper proposes the utilization of a time delay neural network (TDNN) to model the correlation of the occupant’s head movement and lateral acceleration. An experiment was conducted to gather real-time data for the modeling process. The results show that TDNN manages to model the correlation by producing a similar output response to the actual response. Thus, it is expected that the correlation model could be used as an occupant’s head movement predictor tool in future studies of MS.

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Section: Time Delay Neural Network Modelingmentioning

confidence: 99%

Modeling of occupant’s head movement behavior in motion sickness study via time delay neural network

et al. 2019

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“…Katzourakis et al (2013) proposed a method to quantify driver's arm neuromuscular admittance during steering in real-time driving. A deep learning-based method to identify driver's steering behaviour during near rear-end collision maneuvers is proposed in Hassan et al (2017). As well, the time-dependent concept of driver decoupling/recoupling using a steer-by-wire system combined with override recognition by counter steering above a certain threshold is proposed in Heesen et al (2014).…”

Section: Specific Driving Patternmentioning

confidence: 99%

A systematic review on sensor-based driver behaviour studies: coherent taxonomy, motivations, challenges, recommendations, substantial analysis and future directions

Al-Hussein

Kiah

Por

et al. 2021

PeerJ Computer Science

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In the plan and development of Intelligent Transportation Systems (ITS), understanding drivers behaviour is considered highly valuable. Reckless driving, incompetent preventive measures, and the reliance on slow and incompetent assistance systems are attributed to the increasing rates of traffic accidents. This survey aims to review and scrutinize the literature related to sensor-based driver behaviour domain and to answer questions that are not covered so far by existing reviews. It covers the factors that are required in improving the understanding of various appropriate characteristics of this domain and outlines the common incentives, open confrontations, and imminent commendations from former researchers. Systematic scanning of the literature, from January 2014 to December 2020, mainly from four main databases, namely, IEEEXplore, ScienceDirect, Scopus and Web of Science to locate highly credible peer-reviewed articles. Amongst the 5,962 articles found, a total of 83 articles are selected based on the author’s predefined inclusion and exclusion criteria. Then, a taxonomy of existing literature is presented to recognize the various aspects of this relevant research area. Common issues, motivations, and recommendations of previous studies are identified and discussed. Moreover, substantial analysis is performed to identify gaps and weaknesses in current literature and guide future researchers into planning their experiments appropriately. Finally, future directions are provided for researchers interested in driver profiling and recognition. This survey is expected to aid in emphasizing existing research prospects and create further research directions in the near future.

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“…The ANN model was trained by applying Levenberg-Marquardt (LM), the most widely used algorithm for curve fitting problems [17]. The advantage of using LM is that it requires less training time [18]. The activation function used between the input and hidden layers was the sigmoid function, while the activation function used between the hidden and output layers was a linear function.…”

Section: Modelling Via Artificial Neural Networkmentioning

confidence: 99%

Artificial neural network for modelling of the correlation between lateral acceleration and head movement in a motion sickness study

Saruchi

Ariff

Zamzuri

et al. 2018

IET Intelligent Transport Systems

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Driver`s Steering Behaviour Identification and Modelling in Near Rear-End collision

Abstract: This paper studies and identifies driver`s steering manoeuvre behaviour in near rear-end collision. Time-To-Collision (TTC)

Cited by 7 publications

References 8 publications

Modeling of occupant’s head movement behavior in motion sickness study via time delay neural network

Modeling of occupant’s head movement behavior in motion sickness study via time delay neural network

A systematic review on sensor-based driver behaviour studies: coherent taxonomy, motivations, challenges, recommendations, substantial analysis and future directions

Artificial neural network for modelling of the correlation between lateral acceleration and head movement in a motion sickness study

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