An LSTM-based driving operation suggestion method for riding comfort-oriented critical zone

Zeng, Lingqiu; Zhang, Hongtao; Han, Qingwen; Tang, Yonghui; Ye, Lei; Wu, Yuping; Zu, Hui

doi:10.1007/s12652-021-03327-1

Cited by 12 publications

(4 citation statements)

References 21 publications

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“…The RNN may also instantaneously offer relevant responses or suggestions to assist with driving. An LSTM is created by Zeng et al [59] to measure the level of comfort while riding using three inputs: velocity, longitudinal acceleration, and yaw rate. The data are initially divided into the designated zones, with the peak period and traffic flow taken into account as the input for the LSTM neural network.…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Recognition of Driving Behavior in Electric Vehicle’s Li-Ion Battery Aging

et al. 2023

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In the foreseeable future, electric vehicles (EVs) will play a key role in the decarbonization of transport systems. Replacing vehicles powered by internal combustion engines (ICEs) with electric ones reduces the amount of CO2 being released into the atmosphere on a daily basis. The Achilles heel of electrical transportation lies in the car battery management system (BMS) that brings challenges to lithium-ion (Li-ion) battery optimization in finding the trade-off between driving and battery health in both the long- and short-term use. In order to optimize the state-of-health (SOH) of the EV battery, this study focuses on a review of the common Li-ion battery aging process and behavior detection methods. To implement the driving behavior approaches, a study of the public dataset produced by real-world EVs is also provided. This research clarifies the specific battery aging process and factors brought on by EVs. According to the battery aging factors, the unclear meaning of driving behavior is also clarified in an understandable manner. This work concludes by highlighting some challenges to be researched in the future to encourage the industry in this area.

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Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Recognition of Driving Behavior in Electric Vehicle’s Li-Ion Battery Aging

et al. 2023

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“…A novel data-driven modelling methodology was proposed for the lateral stability description of articulated steering vehicles and a Recurrent Neural Network (RNN) model was built to accurately quantify vehicle lateral stability [22]. A long short-term memory (LSTM) network, which is a time cycle neural network, was used to evaluate real-time bus riding comfort and provide driving suggestions [23]. A CNN-LSTM method was proposed to meet the prediction requirements and provide an effective method for the safe operation of unmanned systems [24].…”

Section: Related Workmentioning

confidence: 99%

Driving Intention Inference Based on a Deep Neural Network with Dropout Regularization from Adhesion Coefficients in Active Collision Avoidance Control Systems

et al. 2022

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Driving intention, which can assist drivers to avoid dangerous emergence for the advanced driver assistant systems (ADAS), can be hardly described accurately for complex traffic environments. At present, driving intention can be mainly obtained by deep neural networks with neuromuscular dynamics and electromyography (EMG) signals of drivers. This method needs numerous drivers’ signals and neural networks with a complex structure. This paper proposes a driving intention direct inference method, namely direct inference from the road surface condition. A driving intention safety distance model based on a deep neural network with dropout regularization was built in an active collision avoidance control system of electric vehicles. Driving intention can be inferred by a deep neural network with dropout regularization from adhesion coefficients between the tire and road. Simulations using rapid control prototyping (RCP) and a hardware-in-the-loop (HIL) simulator were performed to demonstrate the effectiveness of the proposed driving intention safety distance model based on a deep neural network with dropout regularization. The proposed driving intention safety distance model can guarantee the safe driving of electric vehicles.

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“…Predictions for bus public transportation that explore passengers are still few [32] with low reliability and capability [33]. Low prediction accuracy results in poor performance of the prediction model which greatly affects the performance of public transportation buses [34]. The accuracy of predicting the number of passengers using deep learning [35] directly influences decisions in the operation and management of public bus transportation [36].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based LSTM Model for Predicting the Number of Passengers for Public Transport Bus Operators

Siswanto,

Manongga,

Sembiring

et al. 2024

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The bus public transportation system has low reliability and ability to predict the number of passengers. The accuracy of predicting the number of passengers by public transport bus operators is still weak, which results in failure to implement solutions by operators. A prediction model with LSTM based on deep learning is proposed to predict passengers for 4 bus public transportation operators (Go Bus, New Zealand Bus, Pavlovich, and Ritchies) which are evaluated by MSLE, MAPE, and SMAPE with variations in epoch, batch size, and neurons. The dataset is a CSV performance report on Auckland Transport (AT) New Zealand metro patronage buses (01/01/2019-07/31/2023). The best prediction model was obtained from the lowest evaluation value and relatively fast time at variations of epoch 60, batch size 16, and neurons 32. The prediction results on training and testing data improved with the suitability of the model tuning. The proposed prediction model performs predictions 12 months later for 4 predictions simultaneously with predicted fluctuations occurring simultaneously. Strong negative correlation on New Zealand Bus-Pavlovich, strong positive correlation on Go Bus with Ritchies and Pavlovich. Predictions that are less closely related and dependent are New Zealand Bus against Go Bus, Pavlovich, and Ritchies. The proposed prediction modeling can be used as a basis for creating operator policies and strategies to deal with passenger fluctuations and for the development of new prediction models.

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An LSTM-based driving operation suggestion method for riding comfort-oriented critical zone

Cited by 12 publications

References 21 publications

Recognition of Driving Behavior in Electric Vehicle’s Li-Ion Battery Aging

Recognition of Driving Behavior in Electric Vehicle’s Li-Ion Battery Aging

Driving Intention Inference Based on a Deep Neural Network with Dropout Regularization from Adhesion Coefficients in Active Collision Avoidance Control Systems

Deep Learning Based LSTM Model for Predicting the Number of Passengers for Public Transport Bus Operators

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