Visual-Attribute-Based Emotion Regulation of Angry Driving Behaviors

Li, Wenbo; Zhang, Bingbing; Wang, Peizhi; Sun, Chen; Zeng, Guanzhong; Tang, Qiuyang; Guo, Gang; Cao, Dongpu

doi:10.1109/mits.2021.3050890

Cited by 30 publications

(22 citation statements)

References 64 publications

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“…The emotions of the driver need to be induced by the appropriate stimulus in order to collect the emotion data. Video-audio clips have been proved to be reliable to elicit the emotions of the driver [3], [4]. In this paper, forty-two clips were manually selected, referring to the methods of previous research [4].…”

Section: B Stimulus Selectionmentioning

confidence: 99%

“…In the process of analyzing the data of SAM, in order to elicit the emotions with the maximum strength, the average score of each clip was calculated, and the clip with the highest average score and small variation was selected. The normalized score of valence, arousal, and dominance of each clip was clustered with the Kmeans method, thus the emotion clusters were identified based on SAM data [3], [4]. Besides, the hit rate, intensity value, and success index was defined according to DES result to select the clips which were effective to induce the emotions of the driver [3], [4].…”

Section: B Stimulus Selectionmentioning

confidence: 99%

“…The normalized score of valence, arousal, and dominance of each clip was clustered with the Kmeans method, thus the emotion clusters were identified based on SAM data [3], [4]. Besides, the hit rate, intensity value, and success index was defined according to DES result to select the clips which were effective to induce the emotions of the driver [3], [4]. The selection results of SAM and DES were analyzed to be consistent.…”

Section: B Stimulus Selectionmentioning

confidence: 99%

“…† These authors contributed equally *Corresponding author network [1], [2]. Thus, intelligent vehicles have become multi-intelligence mobile terminal that carries rich functions and services [3]. This multi-intelligence mobile terminal will bring about tremendous changes in the interaction system of automotive smart cockpits.…”

Section: Introductionmentioning

confidence: 99%

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CogEmoNet: A Cognitive-Feature-Augmented Driver Emotion Recognition Model for Smart Cockpit

Zeng

Zhang

et al. 2022

IEEE Trans. Comput. Soc. Syst.

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Driver's emotion recognition is vital to improving driving safety, comfort, and acceptance of intelligent vehicles. This paper presents a cognitive-feature-augmented driver emotion detection method which is based on emotional cognitive process theory and deep networks. Different from the traditional methods, both the driver's facial expression and cognitive process characteristics (age, gender, and driving age) were used as the inputs of the proposed model. Convolutional techniques were adopted to construct the model for drivers emotion detection simultaneously considering the drivers facial expression and cognitive process characteristics. A driver's emotion data collection was carried out to validate the performance of the proposed method. The collected dataset consists of 40 drivers' frontal facial videos, their cognitive process characteristics and self-reported assessments on driver emotions. Another two deep networks were also used to compare recognition performance. The results prove that proposed method can achieve well detection results for different databases on the discrete emotion model and dimensional emotion model, respectively.

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Section: B Stimulus Selectionmentioning

confidence: 99%

Section: B Stimulus Selectionmentioning

confidence: 99%

Section: B Stimulus Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

CogEmoNet: A Cognitive-Feature-Augmented Driver Emotion Recognition Model for Smart Cockpit

Zeng

Zhang

et al. 2022

IEEE Trans. Comput. Soc. Syst.

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“…Driving style and driving behavior have attracted research interest for driving safety [51,52] and avoiding collision [53]. Wenbo et al [54] investigated the driver anger regulation in visual attributes. Driver facial expression mirrors driver emotional state [55]; therefore, it is important for emotion recognition and ensuring driving safety.…”

Section: Driver Facial Expression Recognitionmentioning

confidence: 99%

On-Road Driver Emotion Recognition Using Facial Expression

Xiao

Zeng

et al. 2022

Applied Sciences

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With the development of intelligent automotive human-machine systems, driver emotion detection and recognition has become an emerging research topic. Facial expression-based emotion recognition approaches have achieved outstanding results on laboratory-controlled data. However, these studies cannot represent the environment of real driving situations. In order to address this, this paper proposes a facial expression-based on-road driver emotion recognition network called FERDERnet. This method divides the on-road driver facial expression recognition task into three modules: a face detection module that detects the driver’s face, an augmentation-based resampling module that performs data augmentation and resampling, and an emotion recognition module that adopts a deep convolutional neural network pre-trained on FER and CK+ datasets and then fine-tuned as a backbone for driver emotion recognition. This method adopts five different backbone networks as well as an ensemble method. Furthermore, to evaluate the proposed method, this paper collected an on-road driver facial expression dataset, which contains various road scenarios and the corresponding driver’s facial expression during the driving task. Experiments were performed on the on-road driver facial expression dataset that this paper collected. Based on efficiency and accuracy, the proposed FERDERnet with Xception backbone was effective in identifying on-road driver facial expressions and obtained superior performance compared to the baseline networks and some state-of-the-art networks.

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