Driver Lane Change Intention Recognition of Intelligent Vehicle Based on Long Short-Term Memory Network

Tang, Liang; Wang, Hengyang; Zhang, Wenhao; Zhong-yi, Mei; Zhang, Lipeng

doi:10.1109/access.2020.3011550

Cited by 42 publications

(22 citation statements)

References 31 publications

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“…Form a TDD (Trajectory-Pooled Deep-Involuntary Descriptor), and then, aggregate the local TDD in the entire video into a global super vector using a Fisher vector, and then, classify and recognise using SVM (Support Vector Machine). According to literature [15], pose information, motion information, and the original image should all be considered important visual cues, and the Markov chain model, which adds cues in order to achieve behavior classification and detection, should be used. C3D network based on 2D convolution was proposed in reference [16] and applied to video behavior recognition.…”

Section: Related Workmentioning

confidence: 99%

Research on Flipped Classroom of University Curriculum Using Eye Movement Analysis and LSTM Neural Network

2022

Wireless Communications and Mobile Computing

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By reversing the arrangement of knowledge transfer and knowledge internalisation, FC (flipped classroom) changed the roles of teachers and students in traditional teaching and replanned the use of classroom time, realizing the innovation of traditional teaching mode. This research uses a network teaching platform to create a network platform supporting environment suitable for FC teaching in universities, allowing for the reform of university classroom teaching modes. An LSTM (Long Short-Term Memory) eye movement analysis algorithm is proposed to solve this problem. By analyzing the sequence of eye movements, this algorithm can predict students’ learning behavior and complete the current learning state detection. The research shows that the design and application of this research have achieved a good teaching effect in practice, which provides an idea for flipped classroom teaching design and curriculum resource design and development.

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Section: Related Workmentioning

confidence: 99%

Research on Flipped Classroom of University Curriculum Using Eye Movement Analysis and LSTM Neural Network

2022

Wireless Communications and Mobile Computing

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“…To tackle time series prediction problems, an intention inference model based on recurrent neural networks (RNNs) is proposed [21]. In highway interweaving areas, the long-short-term memory network (LSTM) is used to predict the future trajectory of the lane-changing vehicle [22].…”

Section: Related Workmentioning

confidence: 99%

Vehicle Interaction Behavior Prediction with Self-Attention

Xin

Lian

et al. 2022

Sensors

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The structured road is a scene with high interaction between vehicles, but due to the high uncertainty of behavior, the prediction of vehicle interaction behavior is still a challenge. This prediction is significant for controlling the ego-vehicle. We propose an interaction behavior prediction model based on vehicle cluster (VC) by self-attention (VC-Attention) to improve the prediction performance. Firstly, a five-vehicle based cluster structure is designed to extract the interactive features between ego-vehicle and target vehicle, such as Deceleration Rate to Avoid a Crash (DRAC) and the lane gap. In addition, the proposed model utilizes the sliding window algorithm to extract VC behavior information. Then the temporal characteristics of the three interactive features mentioned above will be caught by two layers of self-attention encoder with six heads respectively. Finally, target vehicle’s future behavior will be predicted by a sub-network consists of a fully connected layer and SoftMax module. The experimental results show that this method has achieved accuracy, precision, recall, and F1 score of more than 92% and time to event of 2.9 s on a Next Generation Simulation (NGSIM) dataset. It accurately predicts the interactive behaviors in class-imbalance prediction and adapts to various driving scenarios.

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“…In total, it comprises more than 900 different pedestrian trajectories. [68], [77], [78], [84] Next-generation Simulation (NGSIM) public dataset…”

Section: Referencementioning

confidence: 99%

Behavior Prediction of Traffic Actors for Intelligent Vehicle Using Artificial Intelligence Techniques: A Review

Kolekar

Gite²,

Pradhan

et al. 2021

IEEE Access

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Intelligent vehicle technology has made tremendous progress due to Artificial Intelligence (AI) techniques.Accurate behavior prediction of surrounding traffic actors is essential for the safe and secure navigation of the intelligent vehicle. Minor misbehavior of these vehicles on the busy roads may lead to an accident. Due to this, there is a need for vehicle behavior research work in today's era. This research article reviews traffic actors' behavior prediction techniques for intelligent vehicles to perceive, infer, and anticipate other vehicles' intentions and future actions. It identifies the key strategies and methods for AI, emerging trends, datasets, and ongoing research issues in these fields. As per the authors' knowledge, this is the first systematic literature review dedicated to the vehicle behavior study examining existing academic literature published by peer review venues between 2011 and 2021. A systematic review was undertaken to examine these papers, and five primary research questions have been addressed. The findings show that using sophisticated input representation that includes traffic rules and road geometry, artificial intelligence-based solutions applied to behavior prediction of traffic actors for intelligent vehicles have shown promising success, particularly in complex driving scenarios. Finally, the paper summarizes the most widely used approaches in behavior prediction of traffic actors for intelligent vehicles, which the authors believe serves as a foundation for future research in behavior prediction of surrounding traffic actors for secure and accurate intelligent vehicle navigation. INDEX TERMS intelligent driving; deep learning; intelligent vehicles; vehicle behavior prediction; pedestrian behavior prediction. Inclusion criteriaResearch articles must be original research articles instead of review/survey articles. Research articles that are published between2011 and 2021.Research articles that answer at least one research question. Research articles must contain search keywords either in the title, abstract, or full text. The proposed solution in the research article must be evaluated. The solution developed must be aimed to solve problems on behavior prediction of traffic actors. Exclusion criteriaNon-English Research articles Duplicate research articles Research articles with non-availability of full text Research articles that are not relevant to pedestrians and vehicle behavior prediction concerning intelligent vehicle III. RESULTSThis section summarizes the findings of systematic analysis. It answers the research questions posed above based on the results of this review process.

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Driver Lane Change Intention Recognition of Intelligent Vehicle Based on Long Short-Term Memory Network

Cited by 42 publications

References 31 publications

Research on Flipped Classroom of University Curriculum Using Eye Movement Analysis and LSTM Neural Network

Research on Flipped Classroom of University Curriculum Using Eye Movement Analysis and LSTM Neural Network

Vehicle Interaction Behavior Prediction with Self-Attention

Behavior Prediction of Traffic Actors for Intelligent Vehicle Using Artificial Intelligence Techniques: A Review

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