A Machine Learning Framework for Automated Accident Detection Based on Multimodal Sensors in Cars

Pour, Hawzhin Hozhabr; Li, Frédéric; Wegmeth, Lukas; Trense, Christian; Doniec, Rafał; Grzegorzek, Marcin; Wismüller, Roland

doi:10.3390/s22103634

Cited by 28 publications

(11 citation statements)

References 58 publications

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“…In this context, the probability of death occurring on the road is higher in secondary traffic accidents than in primary ones, making it crucial to quickly identify accidents on the road to prevent subsequent secondary accidents. Consequently, in the field of artificial intelligence, technologies are being actively developed to quickly detect traffic accidents or accurately classify types of accidents [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Cross-Modality Interaction-Based Traffic Accident Classification

Oh,

Ban

2024

Applied Sciences

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Traffic accidents on the road lead to serious personal and material damage. Furthermore, preventing secondary accidents caused by traffic accidents is crucial. As various technologies for detecting traffic accidents in videos using deep learning are being researched, this paper proposes a method to classify accident videos based on a video highlight detection network. To utilize video highlight detection for traffic accident classification, we generate information using the existing traffic accident videos. Moreover, we introduce the Car Crash Highlights Dataset (CCHD). This dataset contains a variety of weather conditions, such as snow, rain, and clear skies, as well as multiple types of traffic accidents. We compare and analyze the performance of various video highlight detection networks in traffic accident detection, thereby presenting an efficient video feature extraction method according to the accident and the optimal video highlight detection network. For the first time, we have applied video highlight detection networks to the task of traffic accident classification. In the task, the most superior video highlight detection network achieves a classification performance of up to 79.26% when using video, audio, and text as inputs, compared to using video and text alone. Moreover, we elaborated the analysis of our approach in the aspects of cross-modality interaction, self-attention and cross-attention, feature extraction, and negative loss.

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Section: Introductionmentioning

confidence: 99%

Cross-Modality Interaction-Based Traffic Accident Classification

Oh,

Ban

2024

Applied Sciences

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“…Detect anomalies in driver behavior to prevent an accident, personalized behavior measures driving style through face detection or Internet of Things (IoT) technologies [10][11][12][13][14],…”

Section: Introductionmentioning

confidence: 99%

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

Doniec¹,

Konior²,

Sieciński³

et al. 2023

Preprint

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To drive safely, the driver must be aware of the surroundings, pay attention to the road traffic, and be ready to adapt to new circumstances. Most studies on driving safety focus on detecting anomalies in driver behavior and monitoring the cognitive capabilities of drivers. In our study, we proposed a classifier for basic activities in driving a car, based on a similar approach that could be applied to the recognition of basic activities in daily life, that is, using electrooculographic (EOG) signals and a one-dimensional convolutional neural network (1D CNN). Our classifier achieved an accuracy of 80% for the 16 primary and secondary activities. The accuracy related to primary activities in driving, including crossroad, parking, roundabout was 97.9%, 96.8%, 97.4%, and 99.5%, respectively. The F1 score for secondary driving actions (0.99) was higher than for primary driving activities (0.93–0.94). Furthermore, using the same algorithm, it was possible to distinguish four secondary activities related to activities of daily life and secondary when driving a car.

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“…Neural network technology learns textual region features autonomously through training on copious annotated data [32], thereby accurately detecting and extracting textual information. However, to enhance detection performance and stability, optimization and adjustments are still required with respect to specific vehicle models, screen sizes, and lighting conditions [33,34].…”

Section: Introductionmentioning

confidence: 99%

A Non-Intrusive Automated Testing System for Internet of Vehicles App Based on Deep Learning

Zhang

Sun

2023

Electronics

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In the non-intrusive automated testing system for Internet of Vehicles (IoV) applications, automatic recognition of text and icons on vehicle central control screens is of paramount importance. However, the detection and recognition of content on vehicle central control screens are inherently complex. Additionally, during non-intrusive vehicle central control screen image testing, there is a deficiency of suitable datasets and detection methods. This deficiency renders information within vehicle application images difficult to be accurately extracted by the detection network. To address this problem, this study first constructs a dataset tailored for text detection and recognition on vehicle screens. This dataset encompasses a variety of vehicle central control images, enabling the generic text detection and recognition network to more effectively identify and interpret text within vehicle screens. Subsequently, this research proposes an enhanced Fully Convolutional Networks for Text Detection (FOTS) method for vehicle central control screen text detection and recognition. This method elevates the semantic expression capabilities of features by sharing vehicle central control screen text detection and recognition features. Furthermore, it improves multi-scale feature processing capabilities through the utilization of a feature transformation module. Validation through visual and quantitative experiments demonstrates that the proposed method can effectively accomplish text detection and recognition tasks on vehicle screens. This achievement bears significant implications for the field of automated testing in IoV applications.

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A Machine Learning Framework for Automated Accident Detection Based on Multimodal Sensors in Cars

Cited by 28 publications

References 58 publications

Cross-Modality Interaction-Based Traffic Accident Classification

Cross-Modality Interaction-Based Traffic Accident Classification

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

A Non-Intrusive Automated Testing System for Internet of Vehicles App Based on Deep Learning

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