Lane Change Prediction With an Echo State Network and Recurrent Neural Network in the Urban Area

Griesbach, Karoline; Beggiato, Matthias; Hoffmann, Karl Heinz

doi:10.1109/tits.2021.3058035

Cited by 15 publications

(4 citation statements)

References 12 publications

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“…Hence, a method to solve this problem is to combine ANN with another ML algorithm or to combine different types of ANN. Griesbach et al (2021) used a recurrent neural network (RNN) in combination with long short-term memory (LSTM) for lane-changing predictions. The RNN is implemented with LSTM cells, based on the study of Su et al (2018).…”

Section: Input Variable Literaturementioning

confidence: 99%

A review on machine learning-based models for lane-changing behavior prediction and recognition

David

Söffker

2023

Front. Future Transp.

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A major aspect in the development of advanced driving assistance systems (ADASs) is the research in developing human driving behavior prediction and recognition models. Recent contributions focus on developing these models for estimating different driving behaviors like lane or speed change. Thus, the models are incorporated into the ADAS to generate warnings and hints for safe maneuvers. Driving behavior recognition and prediction models are generally developed based on machine learning (ML) algorithms and are proven to generate accurate estimations. Previous review research contributions tend to focus on ML-based models for the prediction and recognition of speed change, trajectory change, and even driving styles. Due to high number of driving errors occurring during a lane change, a state-of-art review of different ML-based models for lane-changing behavior prediction and recognition is helpful to present a comparison between different models in terms of structure, influencing input variables, and performance. This enables the integration of the most efficient model for the development of ADASs to avoid accidents during a lane change. First, definitions and terms related to the model’s task and evaluation metrics used to evaluate the model’s performance are described to improve the readability. Then, the different input variables of the models affecting the lane-changing behaviors are presented. Next, a review of the models developed based on well-known approaches, such as artificial neural network (ANN), hidden Markov model (HMM), and support vector machine (SVM), using different input variables is given. Three lane-changing behaviors are focused on here: left/right lane change and lane keeping. The advantages and disadvantages of the different ML models with a comparison are summarized as well. Finally, the improvements required in the future are discussed.

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Section: Input Variable Literaturementioning

confidence: 99%

A review on machine learning-based models for lane-changing behavior prediction and recognition

David

Söffker

2023

Front. Future Transp.

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“…Some researchers used LSTM to research the identification of driving behavior. For example, Griesbach et al [34] applied LSTM to predict lane change behavior, and the accuracy reached 90%. Zhang et al [94] constructed a DeepConvLSTM model based on RNN framework to identify different driving behaviors by introducing LSTM gating mechanism, with an accuracy of 95.19%, far better than the random forest model (87.39%).…”

Section: Recurrent Neuralmentioning

confidence: 99%

“…Meanwhile, it can be organically integrated with the classifier to realize end-to-end data learning and significantly improve the recognition accuracy. Some researchers try to adopt deep neural network (DNN) [20,21], convolutional neural network (CNN) [22][23][24][25][26][27][28][29], and recurrent neural network (RNN) [30][31][32][33][34][35][36][37] to construct a driving behavior recognition model, which has achieved good results. In recent years, with the widespread application of on-board sensors and CAN bus technology in cars, driving behavior data in the natural driving process have been collected and stored, which provides massive data samples for the construction of deep learning model, and the recognition methods of driving behavior are gradually evolving to deep learning model.…”

Section: Introductionmentioning

confidence: 99%

A Review for the Driving Behavior Recognition Methods Based on Vehicle Multisensor Information

Zhao

Zhong

Zhi-jun

et al. 2022

Journal of Advanced Transportation

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The frequent traffic accidents lead to a large number of casualties and large related financial losses every year; this serious state is owed to several factors; among those, driving behavior is one of the most imperative subjects to discuss. Driving behaviors mainly include behavior characteristics such as car-following, lane change, and risky driving behavior such as distraction, fatigue, or aggressive driving, which are of great help to various tasks in traffic engineering. An accurate and reliable method of driving behavior recognition is of great significance and guidance for vehicle driving safety. In this paper, the vehicle multisensor information, vehicle CAN bus data acquisition system, and typical feature extraction methods are summarized at first. And then, several driving behavior recognition models based on machine learning and deep learning are reviewed. Through a detailed analysis of the features of random forests, support vector machines, convolutional neural networks, and recurrent neural networks used to build driving behavior recognition models, the following findings are obtained: the driving behavior model constructed by traditional machine learning model is relatively mature but it is greatly affected by feature extraction, data scale, and model structure, which affects the accuracy of the final driving behavior recognition. Deep learning model based on a neural network has achieved high accuracy in identifying driving behavior, and it may gradually become the mainstream of constructing the driving behavior model with the development of big data, artificial intelligence technology, and computer hardware. Finally, this paper points out some content that needs to be further explored, to provide reference and inspiration for scholars in this field to continue to study the driving behavior recognition model in depth.

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“…21 These ESN architecture features make them a particularly good fit for the kind of heterogeneous data fusion required here. In recent times, ESNs have been used for a wide range of applications, such as traffic management, 22 soil temperature modelling, 23 power grid voltage insulator damage classification, 24 waterflood performance prediction 25 and wind speed forecasting. 26 The general ESN architecture has three principal features.…”

Section: Echo State Networkmentioning

confidence: 99%

Heterogeneous data fusion for the improved non-destructive detection of steel-reinforcement defects using echo state networks

Wootton

Day

Haycock

2022

Structural Health Monitoring

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The degradation of roads is an expensive problem: in the United Kingdom alone, £27 billion was spent on road repairs between 2013 and 2019. One potential cost-saver is the early, non-destructive detection of faults. There are many available techniques, each with its own benefits and drawbacks. This paper builds upon the successful processing of magnetic flux leakage (MFL) data by echo state networks (ESNs) for damage diagnostics, by augmenting ESNs with the depth of concrete cover as part of a data fusion approach. This fusion-based ESN outperformed a number of non-fusion ESN comparators and a previously used analytical technique. Additionally, the fusion ESN had an optimal threshold value whose standard deviation was three times smaller than that of the nearest alternative technique, potentially prompting a move towards automated defect detection in ‘real-world’ applications.

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Lane Change Prediction With an Echo State Network and Recurrent Neural Network in the Urban Area

Cited by 15 publications

References 12 publications

A review on machine learning-based models for lane-changing behavior prediction and recognition

A review on machine learning-based models for lane-changing behavior prediction and recognition

A Review for the Driving Behavior Recognition Methods Based on Vehicle Multisensor Information

Heterogeneous data fusion for the improved non-destructive detection of steel-reinforcement defects using echo state networks

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