A deep learning algorithm for simulating autonomous driving considering prior knowledge and temporal information

Chen, Sikai; Leng, Y.X.; Labi, Samuel

doi:10.1111/mice.12495

Cited by 94 publications

(54 citation statements)

References 56 publications

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“…The method based on deep learning can help drivers make better travel decisions based on projected future traffic scenarios. S. Chen, Leng, and Labi (2019) developed a deep CNN-LSTM algorithm for driving decision prediction, which considered both human prior knowledge and time information. M. Zhou, Yu, and Qu (2020) combined reinforcement learning and the car-following model to improve the driving strategy for connected and automated vehicles.…”

Section: Literature Reviewmentioning

confidence: 99%

Network‐wide traffic speed forecasting: 3D convolutional neural network with ensemble empirical mode decomposition

Zhang

Zhou

Chen

et al. 2020

Computer aided Civil Eng

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Accurate traffic speed forecasting is one of the most critical tasks in proactive traffic management and the deployment of advanced traveler information systems. This paper proposes a hybrid forecasting approach named DeepEnsemble by integrating the three‐dimensional convolutional neural network (3D CNN) with ensemble empirical mode decomposition (EEMD). There are four steps in this hybrid approach. First, EEMD is adopted to decompose the complex traffic speed time series data with noise into several intrinsic mode functions (IMFs) and a residue. Second, a three‐dimensional tensor is established and fed into 3D CNN for prediction. Third, the output of 3D CNN prediction is obtained by a linear combination of the results of all components. Finally, the 3D CNN prediction output, external features, and historical features are fused to predict the network‐wide traffic speed simultaneously. The proposed DeepEnsemble approach is tested on the three‐month traffic speed series data of a real‐world large‐scale urban expressway network with 308 traffic flow detectors in Beijing, China. The experimental results indicate that DeepEnsemble outperforms the state‐of‐the‐art network‐wide traffic speed forecasting models. 3D CNN learns temporal, spatial, and depth information better than 2D CNN. Moreover, forecasting accuracy can be improved by employing EEMD. DeepEnsemble is a promising model with scalability and portability for network‐wide traffic speed prediction and can be further extended to conduct traffic status monitoring and congestion mitigation strategies.

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Section: Literature Reviewmentioning

confidence: 99%

Network‐wide traffic speed forecasting: 3D convolutional neural network with ensemble empirical mode decomposition

Zhang

Zhou

Chen

et al. 2020

Computer aided Civil Eng

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show abstract

“…The sensed information may be complemented by information received from other vehicles, infrastructure, pedestrians, and other sources. When equipped with such capabilities, the autonomous vehicle is termed a connected autonomous vehicle (Chen et al, 2020). In order to build or maintain public trust in AVs, it is essential that AVs is capable of safe operations in a wide gamut of natural or man-made environmental conditions including the traffic stream, and particularly, in the event of system failure.…”

Section: Vehicle Automationmentioning

confidence: 99%

Vehicle Connectivity and Automation: A Sibling Relationship

Chen

et al. 2020

Front. Built Environ.

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The evolution of scientific advances has often been characterized by the amalgamation of two or more technologies. With respect to vehicle connectivity and automation, recent literature suggests that these two emerging transportation technologies can and will jointly and profoundly shape the future of transportation. However, it is not certain how the individual and synergistic benefits to be earned from these technologies is related to their prevailing levels of development. As such, it may be considered useful to revisit the primary concepts of automation and connectivity, and to identify any current and expected future synergies between them. Doing this can help generate knowledge that could be used to justify investments related to transportation systems connectivity and automation. In this discussion paper, we attempt to address some of these issues. The paper first reviews the technological concepts of systems automation and systems connectivity, and how they prospectively, from an individual and collective perspective, impact road transportation efficiency and safety. The paper also discusses the separate and common benefits of connectivity and automation, and their possible holistic effects in terms of these benefits where they overlap. The paper suggests that at the current time, the sibling relationship seems to be lopsided: vehicle connectivity has immense potential to enhance vehicle automation. Automation, on the other hand, may not significantly promote vehicle connectivity directly, at least not in the short term but possibly in the long term. The paper argues that future trends regarding market adoption of these two technologies and their relative pace of advancement or regulation, will shape the future synergies between them.

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“…Then, the fitness values of grasshoppers are calculated by obtaining the accuracy of SVM classification results with RBF kernel of parameters c and γ. Next, update the position of all grasshoppers in the population according to the defined movement equation in (26). Furthermore, if the maximum iteration of GOA is reached, the optimization process is finished.…”

Section: The Overall Process Of Goa-imlstmmentioning

confidence: 99%

“…To address long-term dependencies in decision making, LSTM adapted in autonomous driving. Chen et al [26] developed a deep CNN-LSTM algorithm for selfdriving simulation to control the movement of self-driving vehicles in the driving simulation. However, the memory ability of important features and classification capability of LSTM is poor.…”

Section: Introductionmentioning

confidence: 99%

A Novel Network Architecture of Decision-Making for Self-Driving Vehicles Based on Long Short-Term Memory and Grasshopper Optimization Algorithm

Shi

Fan

et al. 2020

IEEE Access

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Long short-term memory network is one of the most important network architectures of decision-making for self-driving vehicles. Nevertheless, the decision-making accuracy of long short-term memory network is limited, the information of the surrounding vehicles is not taken into consideration, which is critical for the decision-making of the ego vehicle, and the classification capability of long shortterm memory network is poor. In this paper, a novel network architecture called improved long short-term memory network with support vector machine classifier optimized by grasshopper optimization algorithm (GOA-ImLSTM) is proposed. Three improvements are presented in GOA-ImLSTM. Firstly, to consider the information of the surrounding vehicles, a new network architecture, used to extract vital features for selfdriving vehicles, with three parallel long short-term memory network units and a network unit serial connected according to vehicle location is designed. Secondly, to improve classification accuracy, support vector machine with stronger classification capability than softmax is introduced to accomplish the classification task. Thirdly, to promote the classification capability of support vector machine, grasshopper optimization algorithm is employed to optimize the parameters of support vector machine. Moreover, to balance exploration and exploitation ability of grasshopper optimization algorithm, dynamic weights in position movement formula are defined. The experiments indicate that GOA-ImLSTM improves the accuracy of results compared with other decision-making methods for self-driving vehicles on the Next Generation SIMulation. INDEX TERMS grasshopper optimization algorithm, long short-term memory, self-driving decisionmaking, support vector machine.

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A deep learning algorithm for simulating autonomous driving considering prior knowledge and temporal information

Cited by 94 publications

References 56 publications

Network‐wide traffic speed forecasting: 3D convolutional neural network with ensemble empirical mode decomposition

Network‐wide traffic speed forecasting: 3D convolutional neural network with ensemble empirical mode decomposition

Vehicle Connectivity and Automation: A Sibling Relationship

A Novel Network Architecture of Decision-Making for Self-Driving Vehicles Based on Long Short-Term Memory and Grasshopper Optimization Algorithm

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