Relevant bibliographies by topics / Driving behavior recognition

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Driving behavior recognition'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Driving behavior recognition"

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Liu, Wenlong, Hongtao Li, and Hui Zhang. "Dangerous Driving Behavior Recognition Based on Hand Trajectory." Sustainability 14, no. 19 (2022): 12355. http://dx.doi.org/10.3390/su141912355.

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Dangerous driving behaviors in the process of driving will produce road traffic safety hazards, and even cause traffic accidents. Common dangerous driving behavior includes: eating, smoking, fetching items, using a handheld phone, and touching a control monitor. In order to accurately identify the dangerous driving behaviors, this study first uses the hand trajectory data to construct the dangerous driving behavior recognition model based on the dynamic time warping algorithm (DTW) and the longest common sub-sequence algorithm (LCS). Secondly, 45 subjects’ hand trajectory data were obtained by
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David, Ruth, Sandra Rothe, and Dirk Söffker. "State Machine Approach for Lane Changing Driving Behavior Recognition." Automation 1, no. 1 (2020): 68–79. http://dx.doi.org/10.3390/automation1010006.

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Research in understanding human behavior is a growing field within the development of Advanced Driving Assistance Systems (ADASs). In this contribution, a state machine approach is proposed to develop a driving behavior recognition model. The state machine approach is a behavior model based on the current state and a given set of inputs. Transitions to different states occur or we remain in the same state producing outputs. The transition between states depends on a set of environmental and driving variables. Based on a heuristic understanding of driving situations modeled as states, as well a
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Ma, Lijing, Shiru Qu, Lijun Song, Junxi Zhang, and Jie Ren. "Human-like car-following modeling based on online driving style recognition." Electronic Research Archive 31, no. 6 (2023): 3264–90. http://dx.doi.org/10.3934/era.2023165.

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<abstract><p>Incorporating human driving style into car-following modeling is critical for achieving higher levels of driving automation. By capturing the characteristics of human driving, it can lead to a more natural and seamless transition from human-driven to automated driving. A clustering approach is introduced that utilized principal component analysis (PCA) and k-means clustering algorithm to identify driving style types such as aggressive, moderate and conservative at the timestep level. Additionally, an online driving style recognition technique is developed based on the
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Li, Hao, Junyan Han, Shangqing Li, Hanqing Wang, Hui Xiang, and Xiaoyuan Wang. "Abnormal Driving Behavior Recognition Method Based on Smart Phone Sensor and CNN-LSTM." International Journal of Science and Engineering Applications 11, no. 1 (2022): 1–8. http://dx.doi.org/10.7753/ijsea1101.1001.

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Accurate identification of abnormal driving behavior is very important to improve driver safety. Aiming at the problem that threshold or traditional machine learning methods are mostly used in existing studies, it is difficult to accurately identify abnormal driving behavior of vehicles, a method of abnormal driving behavior recognition based on smartphone sensor data and convolutional neural network (CNN) combined with long and short-term memory (LSTM) was proposed. Smartphone sensors are used to collect vehicle driving data, and data sets of various driving behaviors are constructed by prepr
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Zhao, Dengfeng, Yudong Zhong, Zhijun Fu, Junjian Hou, and Mingyuan Zhao. "A Review for the Driving Behavior Recognition Methods Based on Vehicle Multisensor Information." Journal of Advanced Transportation 2022 (October 7, 2022): 1–16. http://dx.doi.org/10.1155/2022/7287511.

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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 driv
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Wei, Jiaxuan, Shuang Yu, and Yu Wang. "Intelligent Driving Behavior Recognition and Legal Liability Issues Using Deep Learning Convolutional Networks." International Journal of Information Technologies and Systems Approach 18, no. 1 (2025): 1–20. https://doi.org/10.4018/ijitsa.382479.

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This work aimed to develop a driving behavior recognition and liability assistance determination method applicable to practical traffic safety management and criminal liability determination scenarios. First, an improved deep neural network was designed, which integrated multi-scale 3D convolutional structures and attention mechanisms to efficiently extract driving behavior features from both spatial and temporal dimensions. Next, a subset of six typical driving behaviors was constructed based on the Drive&Act public dataset, followed by sample labeling and feature preprocessing. Finally,
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Sun, Longxiang, Huanchao Feng, Min Zhang, Anmengdie Li, and Jinglei Zhang. "Distracted Driving Behavior of Operation Recognition Method Based on YOLOv5 and BPNN." Journal of Scientific and Engineering Research 9, no. 4 (2022): 17–26. https://doi.org/10.5281/zenodo.10518762.

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<strong>Abstract</strong> In order to accurately identify the driver's distracted driving behavior, reduce the injury of traffic accidents to the personnel in the vehicle and improve the driving safety level, this paper takes the driver's distracted driving behavior as the research object, build a distracted driving experimental environment based on real vehicles, and collected 27902 distracted driving image data of 20 drivers, The combined recognition model of YOLOv5 and BPNN was constructed, the video frame image was input into the trained YOLOv5 model, the boundary box data of the driver's
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Wang, Kaili, Jinglei Zhang, and Yida Zhang. "Recognition of Driver Cognitive Distraction Behavior based on Numerical Data." Journal of Scientific and Engineering Research 10, no. 12 (2023): 59–65. https://doi.org/10.5281/zenodo.10466087.

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<strong>Abstract </strong>In recent years, with the continuous development of the automotive industry, the number of motor vehicles has also been increasing, which has brought about worrying traffic safety issues. As an important factor in the road traffic system, drivers are a significant factor in causing traffic accidents. Dangerous driving behaviors such as distracted driving and angry driving seriously affect traffic safety. Among them, distracted driving can be divided into different types such as cognitive distracted driving, visual distracted driving, and operational distracted driving
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Darwish, Karam, and Majd Ali. "Driving Behaviors Recognition Using Deep Neural Networks." Embedded Selforganising Systems 10, no. 5 (2023): 9–12. http://dx.doi.org/10.14464/ess.v10i5.592.

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Road accidents are skyrocketing, and traffic safety is a severe problem around the world. Many road traffic deaths are related to drivers’ unsafe behaviors. In this paper, we propose two different deep-learning models which classify the driver’s actions in a 60-second time frame into two main categories: Normal and Aggressive driving based on GPS data collected at 1 Hz, which is later preprocessed and passed to the proposed models to identify dominant driving behavior in each time frame. The models achieved an accuracy of 93.75 percent in real-world tests, which proves the efficiency of this m
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Tukaram, Ghodake Sanket, Jadhav Tushar Anil, Pund Mayuri Vishwanath, Pund Raviraj Vishwanath, and Dandekar Pooja Kantilal. "Discriminative Transfer Learning for Driving Pattern Recognition in Unlabeled Scenes." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 1925–29. http://dx.doi.org/10.22214/ijraset.2022.47750.

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Abstract: Driving behavior has a large impact on behaviour of driver. The lack of a labeled data problem in a driving scene substantially hinders the driving pattern recognition accuracy. However, modeling driving behavior under the dynamic driving conditions is complex, making a quantitative analysis of the the driving behaviour. In this paper, the Driver behaivor dataset was collected from dataset repository. Then, we have to implement the pre-processing techniques. Then, the system is developed the machine learning algorithm such as Random forest and Support Vector Machine algorithm. The ex
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Dissertations / Theses on the topic "Driving behavior recognition"

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Zouaoui-Elloumi, Salma. "Reconnaissance de comportements de navires dans une zone portuaire sensible par approches probabiliste et événementielle : application au Grand Port Maritime de Marseille." Phd thesis, Ecole Nationale Supérieure des Mines de Paris, 2012. http://pastel.archives-ouvertes.fr/pastel-00737678.

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Cette thèse s'est déroulée dans le cadre du projet SECMAR qui visait à sécuriser le Grand Port Maritime de Marseille. Notre objectif était d'aider les personnels du port à identifier les comportements menaçant des navires afin de pouvoir agir efficacement en cas de danger réel. A ce titre, nous avons développé un système d'analyse et de reconnaissance de comportements de navires formé de deux sous-modules complémentaires. Le premier est construit à partir de l'approche probabiliste Modèle de Markov Cachée et traite principalement des comportements nominaux des gros bateaux qui se caractérisent
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Jhuang, Kai-Lun, and 莊鎧綸. "Region Feature Based Deep Learning for Driving Behavior Recognition." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/283z9s.

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Chang, Li-Jsin, and 張立欣. "Intelligent Dangerous Driving Behavior Recognition and Driver Pupil Localization." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/q8svhq.

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碩士<br>國立交通大學<br>電控工程研究所<br>107<br>Safe driving has drawn large attention over the world. CAA South Central Ontario noted that 30% of car accidents came from distracted driving. Nowadays, most of advanced driving assistance systems (ADAS) focus on vehicle equipment rather than driver’s behavior. Therefore, the solution consisting of driving behavior detection, fatigue behavior detection and driver pupil estimation is proposed in this paper. In order to catch images from drivers, camera is set on right front of drivers. With the deep learning, face and hand detection technologies is used to get
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Books on the topic "Driving behavior recognition"

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Hilliges, Otmar. Input Recognition. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0004.

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Sensing of user input lies at the core of HCI research. Deciding which input mechanisms to use and how to implement them such that they work in a way that is easy to use, robust to various environmental factors and accurate in reconstruction of the users intent is a tremendously challenging problem. The main difficulties stem from the complex nature of human behavior which is highly non-linear, dynamic and context dependent and can often only be observed partially. Due to these complexities, research has turned its attention to data-driven techniques in order to build sophisticated and robust
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Scherer, Klaus, Marcello Mortillaro, and Marc Mehu. Facial Expression Is Driven by Appraisal and Generates Appraisal Inference. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190613501.003.0019.

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Emotion researchers generally concur that most emotions in humans and animals are elicited by the appraisals of events that are highly relevant for the organism, generating action tendencies that are often accompanied by changes in expression, autonomic physiology, and feeling. Scherer’s component process model of emotion (CPM) postulates that individual appraisal checks drive the dynamics and configuration of the facial expression of emotion and that emotion recognition is based on appraisal inference with consequent emotion attribution. This chapter outlines the model and reviews the accrued
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Troisi, Alfonso. Nepotism. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199393404.003.0014.

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Nepotism is a social habit that is commonly condemned because it threatens our confidence in meritocracy and offends our sense of fair play. Yet, nepotism has been a common practice in different cultures throughout ancient, modern, and contemporary history. This chapter explores the biological bases of this powerful human inclination to help one’s own and to introduce the reader to those evolutionary theories that account for nepotistic behaviors: kin selection and reciprocal altruism. The chapter briefly reviews the physiological and psychological mechanisms that allow kin recognition and the
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Felde, Andrea Kronstad, Tor Halvorsen, Anja Myrtveit, and Reidar Øygard. Democracy and the Discourse on Relevance Within the Academic Profession at Makerere University. African Minds, 2021. http://dx.doi.org/10.47622/9781928502272.

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Democracy and the Discourse of Relevanceis set against the backdrop of the spread of neoliberal ideas and reforms since the 1980s, accepting also that these ideas are rooted in a longer history. It focuses on how neoliberalism has worked to transform the university sector and the academic profession. In particular, it examines how understandings of, and control over, what constitutes relevant knowledge have changed. Taken as a whole, these changes have sought to reorient universities and academics towards economic development in various ways. This includes the installation of strategies for ho
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Book chapters on the topic "Driving behavior recognition"

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Yu, Jianuo, Zhen Xue, Wenbo Yu, and He Huang. "Temporal Difference Enhancement Network for Driving Behavior Recognition." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4399-5_20.

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Lu, Dang-Nhac, Thi-Thu-Trang Ngo, Hong-Quang Le, Thi-Thu-Hien Tran, and Manh-Hai Nguyen. "MDBR: Mobile Driving Behavior Recognition Using Smartphone Sensors." In Computational Collective Intelligence. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67077-5_3.

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Wang, Shun, Fang Zhou, Song-Lu Chen, and Chun Yang. "Recurrent Graph Convolutional Network for Skeleton-Based Abnormal Driving Behavior Recognition." In Pattern Recognition. ICPR International Workshops and Challenges. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68790-8_43.

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Xu, Chaonan, Yong Zhang, Da Guo, Wei Wang, and Baoling Liu. "System Design of Driving Behavior Recognition Based on Semi-supervised Learning." In Human Centered Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15127-0_54.

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Dominguez-Jimenez, Juan Antonio, Kiara Coralia Campo-Landines, and Sonia Helena Contreras-Ortiz. "A Methodology for Driving Behavior Recognition in Simulated Scenarios Using Biosignals." In Communications in Computer and Information Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31019-6_31.

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Shi, Xin, Fen Li, Guangqiang Lu, Yanjing Xie, Fangyan Dong, and Kewei Chen. "A Recognition Algorithm for Distracted Driving Behavior Based on CBAM-EfficientNetB0." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-5318-8_12.

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Yang, Xiaorun, Fei Ding, Dengyin Zhang, and Min Zhang. "Vehicular Trajectory Big Data: Driving Behavior Recognition Algorithm Based on Deep Learning." In Communications in Computer and Information Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8086-4_30.

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Feng, Jiahao, Jinsong Ye, and Lei Zhou. "Recognition of Abnormal Driving Behavior of Highway Vehicles Based on Data Characteristics." In Proceedings of the 2022 2nd International Conference on Education, Information Management and Service Science (EIMSS 2022). Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-024-4_29.

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Ortiz, Esteban, and José Ignacio Morejón. "Social Enterprises and B-Corps in Ecuador." In The International Handbook of Social Enterprise Law. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14216-1_26.

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AbstractEcuador is at the forefront when it comes to legal innovations. In 2008, it was the first country in the region to recognize rights to nature, and in 2020, it became the fourth country worldwide to incorporate a discussion on the benefit of a BIC corporation status into its legal system.BIC corporations enable Ecuadorian companies to prioritize stakeholder governance, transparency, and sustainability in management as well as corporate behavior, driving change in markets. Since their recognition, for-profit corporations in Ecuador have had the option to transform their governance and prioritize the generation of positive material impacts by committing to solve one, or many, social and environmental problems through their business models.This article explains how this small country in Latin America managed to insert this innovation into its legal system and how the recognition of BIC corporations marks a major milestone in the process of changing systems in the region.
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Leonhardt, Veit, and Gerd Wanielik. "Recognition of Lane Change Intentions Fusing Features of Driving Situation, Driver Behavior, and Vehicle Movement by Means of Neural Networks." In Advanced Microsystems for Automotive Applications 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66972-4_6.

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Conference papers on the topic "Driving behavior recognition"

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Sun, Miao, Fan Liu, Haijun He, Zhuomin Yang, Wen Lou, and Dudu Guo. "Research on Deep Learning-based Driver Fatigue Driving Behavior Recognition Methods." In 2024 3rd International Conference on Big Data, Information and Computer Network (BDICN). IEEE, 2024. http://dx.doi.org/10.1109/bdicn62775.2024.00027.

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Gu, Shuai, and Yufei Yang. "Exploring Lane Change Style Recognition through Analysis of Latent Driving Behavior." In 2024 2nd International Conference on Mechatronics, IoT and Industrial Informatics (ICMIII). IEEE, 2024. http://dx.doi.org/10.1109/icmiii62623.2024.00040.

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Zheng, Yunshuang, Huajun Wang, Bin Wang, and Pengfei Yuan. "Research on Safety Status Recognition Model of Commercial Vehicle Driving Behavior." In 2024 3rd International Conference on Artificial Intelligence, Human-Computer Interaction and Robotics (AIHCIR). IEEE, 2024. https://doi.org/10.1109/aihcir65563.2024.00019.

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Luo, Guofu, Xuyao zhang, Hao Li, et al. "Circuit design for driving distracted behavior recognition based on memristive neural network." In 4th International Conference on Automation Control. Algorithm and Intelligent Bionics, edited by Jing Na and Shuping He. SPIE, 2024. http://dx.doi.org/10.1117/12.3039377.

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Tong, Xiaochun, and Mary Jane C. Samonte. "Research on dangerous driving behavior recognition method based on convolutional neural network." In 3rd International Conference on Image Processing, Object Detection and Tracking (IPODT24), edited by Bin Liu and Lu Leng. SPIE, 2024. http://dx.doi.org/10.1117/12.3050403.

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Zhang, Chenjie, Jialu Li, Hanping Hu, and Xuemei Bai. "Deep learning-based low-light image enhancement method for driving behavior recognition*." In 2024 WRC Symposium on Advanced Robotics and Automation (WRC SARA). IEEE, 2024. http://dx.doi.org/10.1109/wrcsara64167.2024.10685674.

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Wang, Jiapei, Chen Mu, Linjie Di, Meiyun Li, Zhiyuan Sun, and Shumei Liu. "Recognition of Unsafe Driving Behaviours Using SC-GCN." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651505.

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Rui, Jing, Chi Kin Lam, Tao Tan, and Yue Sun. "DLW-YOLO: Improved YOLO for Student Behaviour Recognition." In 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS). IEEE, 2024. http://dx.doi.org/10.1109/docs63458.2024.10704305.

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Nguyen, Huy-Hung, Chi Dai Tran, Long Hoang Pham, et al. "Multi-View Spatial-Temporal Learning for Understanding Unusual Behaviors in Untrimmed Naturalistic Driving Videos." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00709.

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Ma, Zhe, Xiaohui Yang, and Haoran Zhang. "Dangerous Driving Behavior Recognition using CA-CenterNet." In 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE). IEEE, 2021. http://dx.doi.org/10.1109/icbaie52039.2021.9390070.

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Reports on the topic "Driving behavior recognition"

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Pyta, V., Bharti Gupta, Shaun Helman, Neale Kinnear, and Nathan Stuttard. Update of INDG382 to include vehicle safety technologies. TRL, 2020. http://dx.doi.org/10.58446/thco7462.

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Driving is one of the riskiest work tasks, accounting for around one third of fatal crashes in the UK. Organisations are expected to manage work-related road safety (WRRS) in the same way that they manage other health and safety risks. The Health and Safety Executive (HSE) and Department for Transport (DFT) issue joint guidance on this in INDG382 ‘Driving at work: managing work-related road safety’. HSE and DFT were seeking to update INDG382 to include reference to vehicle safety technologies that could enable employers to monitor safety related events or driver behaviours, to support learning
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