An LSTM-Based Autonomous Driving Model Using a Waymo Open Dataset

Gu, Zhicheng; Li, Zhihao; Di, Xuan; Shi, Rongye

doi:10.3390/app10062046

Cited by 35 publications

(18 citation statements)

References 16 publications

(15 reference statements)

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“…A bidirectional LSTM (BiLSTM) model [ 35 ] presents an improved framework compared with LSTM [ 36 ]. The neuron structure of BiLSTM is similar to that of LSTM, as illustrated in Fig.…”

Section: Methodsmentioning

confidence: 99%

Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019

Huang

Shen

Kuo

et al. 2022

Socio-Economic Planning Sciences

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“…A bidirectional LSTM (BiLSTM) model [ 35 ] presents an improved framework compared with LSTM [ 36 ]. The neuron structure of BiLSTM is similar to that of LSTM, as illustrated in Fig.…”

Section: Methodsmentioning

confidence: 99%

Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019

Huang

Shen

Kuo

et al. 2022

Socio-Economic Planning Sciences

View full text Add to dashboard Cite

“…A bidirectional LSTM (BiLSTM) model [25], [26] presents an improved framework compared with LSTM [27], [28] . The neuron structure of BiLSTM is similar to that of LSTM, as illustrated in Fig.…”

Section: B Bidirectional Long Short-term Memory Neural Networkmentioning

confidence: 99%

Novel Spatiotemporal Feature Extraction Parallel Deep Neural Network for Forecasting Confirmed Cases of Coronavirus Disease 2019

Huang

Shen

Kuo

et al. 2020

Preprint

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The coronavirus disease 2019 pandemic continues as of March 26 and spread to Europe on 10 approximately February 24. A report from April 29 revealed 1.26 million confirmed cases and 125 928 deaths 11 in Europe. This study proposed a novel deep neural network framework, COVID-19Net, which parallelly 12 combines a convolutional neural network (CNN) and bidirectional gated recurrent units (GRUs). Three 13 European countries with severe outbreaks were studied-Germany, Italy, and Spain-to extract 14 spatiotemporal feature and predict the number of confirmed cases. The prediction results acquired from 15 COVID-19Net were compared to those obtained using a CNN, GRU, and CNN-GRU. The mean absolute 16 error, mean absolute percentage error, and root mean square error, which are commonly used model 17 assessment indices, were used to compare the accuracy of the models. The results verified that COVID-19Net 18was notably more accurate than the other models. The mean absolute percentage error generated by COVID-19 19Net was 1.447 for Germany, 1.801 for Italy, and 2.828 for Spain, which were considerably lower than those 20 of the other models. This indicated that the proposed framework can accurately predict the accumulated 21 number of confirmed cases in the three countries and serve as a crucial reference for devising public health 22 strategies. 23Index Terms-Confirmed cases forecasting, COVID-19Net, parallel deep neural network 24 25

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“…There is no predesignated mathematical form and model training purely relies on observations. A growing number of data-driven models employ (deep) neural networks, including ANN (Panwai and Dia, 2007), RNN and LSTM (Zhou et al, 2017a;Huang et al, 2018a;Gu et al, 2020;Shou et al, 2020), deep reinforcement learning like DDPG (Zhu et al, 2018), and Generative Adversarial Imitation Learning (Kuefler et al, 2017;Zhou et al, 2020). Neural networks exploit no explicit traffic models nor make pre-assumptions, and thus can be treated as an uninterpretable black box.…”

Section: Motivationmentioning

confidence: 99%

A Physics-Informed Deep Learning Paradigm for Car-Following Models

Mo,

Di,

Shi

2020

Preprint

Self Cite

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Car-following behavior has been extensively studied using physics-based models, such as the intelligent driver model (IDM) and optimal velocity model (OVM). These models successfully interpret traffic phenomena observed in the real-world but may not fully capture the complex cognitive process of driving. Deep learning models, on the other hand, have demonstrated their power in capturing observed traffic phenomena but require a large amount of driving data to train. This paper aims to develop a family of neural network based car-following models that are informed by physics-based models, which leverage the advantage of both physics-based (being data-efficient and interpretable) and deep learning based (being generalizable) models. We design physics-informed deep learning for car-following (PIDL-CF) architectures encoded with two popular physics-based models -IDM and OVM, on which acceleration is predicted for four traffic regimes: acceleration, deceleration, cruising, and emergency braking. Two types of PIDL-CFM problems are studied, one to predict acceleration only and the other to jointly predict acceleration and discover model parameters. We also demonstrate the superior performance of PIDL with the Next Generation SIMulation (NGSIM) dataset over baselines, especially when the training data is sparse. The results demonstrate the superior performance of neural networks informed by physics over those without. The developed PIDL-CF framework holds the potential for system identification of driving models and for the development of driving-based controls for automated vehicles.

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An LSTM-Based Autonomous Driving Model Using a Waymo Open Dataset

Cited by 35 publications

References 16 publications

Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019

Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019

Novel Spatiotemporal Feature Extraction Parallel Deep Neural Network for Forecasting Confirmed Cases of Coronavirus Disease 2019

A Physics-Informed Deep Learning Paradigm for Car-Following Models

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