Early anticipation of driver’s maneuver in semiautonomous vehicles using deep learning

Gite, Shilpa; Agrawal, Himanshu; Kotecha, Ketan

doi:10.1007/s13748-019-00177-z

Cited by 15 publications

(23 citation statements)

References 49 publications

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“…Moreover, [53] have used 70:30 split applied for multiple video datasets video containing faces. Additionally, in the earlier implementation papers of the authors, the same dataset split a generated remarkable results [21,44]. In the machine learning literature, it can be observed that different sample ratios have been used depending on the size of the dataset.…”

Section: A Datasetmentioning

confidence: 96%

“…An essential difference between these two algorithms is that while CNN is a core component of feed-forward propagation of visual data in the architecture, RNNs are more powerful to get the following sequence in the data because it is a sequential model [37]. A fusion of LSTM and RNN as a collective deep learning approach is used to classify the driver's actions, represented in the DMT algorithm [44], to model spatial-temporal dependencies in the continuous video data. It works well in a different contextual environment applicable for human activity recognition applications [40,41,45].…”

Section: Machine Learning and Deep Learning For Action Recognitionmentioning

confidence: 99%

“…It is to classify driver action from the video input data. Driver's Movement Tracking (DMT) is proposed in [44] to track driver's movement by context fusion of inside and outside video data and RNN-LSTM framework. RNNs and CNNs can be combined for more efficiency in the classification.…”

Section: Rnn_cnn Model In Admtmentioning

confidence: 99%

“…So the eye region features are also extracted from the videos once the face detection is performed with the Viola-Jones detector [52], as shown in figure 2. DMT proposed fast and accurate eye tracking with effective localization of the iris center [44]. Subsequently, iris boundary and eye gaze are tracked.…”

Section: Rnn_cnn Model In Admtmentioning

confidence: 99%

“…Gite et al [44] presented a Driver Movement Tracking (DMT) approach consisting of activities involving three steps: preprocessing techniques, STIP, tracking of the eye gaze, and classification of actions using deep learning algorithms. The movement tracking process, which is done by STIPs, is then followed by the eye gaze estimation of the driver.…”

Section: Advanced Driver Movement Tracking (Admt)mentioning

confidence: 99%

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ADMT: Advanced Driver’s Movement Tracking System Using Spatio-Temporal Interest Points and Maneuver Anticipation Using Deep Neural Networks

et al. 2021

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Assistive driving is a complex engineering problem and is influenced by several factors such as the sporadic nature of the quality of the environment, the response of the driver, and the standard of the roads on which the vehicle is being driven. The authors track the driver's anticipation based on his head movements using Spatio-Temporal Interest Point (STIP) extraction and enhance the anticipation of action accuracy well before using the RNN-LSTM framework. This research tries to tackle a fundamental problem of lane change assistance by developing a novel model called Advanced Driver's Movement Tracking (ADMT). ADMT uses customized convolution-based deep learning networks by using Recurrent Convolutional Neural Network (RCNN). STIP with eye gaze extraction and RCNN performed in ADMT on brain4cars dataset for driver movement tracking. Its performance is compared with the traditional machine learning and deep learning models, namely Support Vector Machines (SVM), Hidden Markov Model (HMM), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and provided an increment of almost 12% in the prediction accuracy and 44% in the anticipation time. ADMT systems outperformed all of the models in terms of both the accuracy of the system and the previously mentioned time of anticipation that is discussed at length in the paper. Thus it assists the driver with additional anticipation time to access the typical reaction time for better preparedness to respond to undesired future behavior. The driver is then assured of a safe and assisted driving experience with the proposed system. INDEX TERMS RCNN; advanced driver movement tracking system, spatio-temporal interest points, eye gaze tracking, deep neural networks.

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Section: A Datasetmentioning

confidence: 96%

Section: Machine Learning and Deep Learning For Action Recognitionmentioning

confidence: 99%

Section: Rnn_cnn Model In Admtmentioning

confidence: 99%

Section: Rnn_cnn Model In Admtmentioning

confidence: 99%

Section: Advanced Driver Movement Tracking (Admt)mentioning

confidence: 99%

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ADMT: Advanced Driver’s Movement Tracking System Using Spatio-Temporal Interest Points and Maneuver Anticipation Using Deep Neural Networks

et al. 2021

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Driver intention prediction based on multi-dimensional cross-modality information interaction

Xue,

Xu,

Qiao

et al. 2024

Multimedia Systems

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Driver intention prediction allow drivers to perceive possible dangers in the fastest time and has become one of the most important research topics in the field of self-driving in recent years. In this study, we propose a driver intention prediction method based on multi-dimensional crossmodality information interaction. First, an efficient video recognition network is designed to extract channel-temporal features of in-side (driver) and out-side (road) videos respectively, in which we design a cross-modality channel-spatial weight mechanism to achieve information interaction between the two feature extraction networks corresponding respectively to the two modalities, and we also introduce a contrastive learning module by which we force the two feature extraction networks to enhance structural knowledge interaction. Then, the obtained representations of in-and out-side videos are fused using a Res-Layer based module to get a preliminary prediction which is then corrected by incorporating the GPS information to obtain a final decision. Besides, we employ a multi-task framework to train the entire network. We validate the proposed method on the public dataset Brain4Car, and the results show that the proposed method achieves competitive results in accuracy while balancing performance and computation.

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Lane-change intention prediction using eye-tracking technology: A systematic review

Pan

Zhang²,

Zhang³

et al. 2022

Applied Ergonomics

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Early anticipation of driver’s maneuver in semiautonomous vehicles using deep learning

Cited by 15 publications

References 49 publications

ADMT: Advanced Driver’s Movement Tracking System Using Spatio-Temporal Interest Points and Maneuver Anticipation Using Deep Neural Networks

ADMT: Advanced Driver’s Movement Tracking System Using Spatio-Temporal Interest Points and Maneuver Anticipation Using Deep Neural Networks

Driver intention prediction based on multi-dimensional cross-modality information interaction

Lane-change intention prediction using eye-tracking technology: A systematic review

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