A multidisciplinary research approach for experimental applications in road-driver interaction analysis

This doctoral dissertation represents a cluster of the research activities conducted at the DICAM Department of the University of Bologna during a three years Ph.D. course. In relation to the broader research topic of “road safety”, the presented research focuses on the investigation of the interaction between the road and the drivers according to human factor principles and supported by the following strategies: 1) The multidisciplinary structure of the research team covering the following academic disciplines: Civil Engineering, Psychology, Neuroscience and Computer Science Engineering. 2) The development of several experimental real driving tests aimed to provide investigators with knowledge and insights on the relation between the driver and the surrounding road environment by focusing on the behaviour of drivers. 3) The use of innovative technologies for the experimental studies, capable to collect data of the vehicle and on the user: a GPS data recorder, for recording the kinematic parameters of the vehicle; an eye tracking device, for monitoring the drivers’ visual behaviour; a neural helmet, for the detection of drivers’ cerebral activity (electroencephalography, EEG). 4) The use of mathematical-computational methodologies (deep learning) for data analyses from experimental studies. The outcomes of this work consist of new knowledge on the casualties between drivers’ behaviour and road environment to be considered for infrastructure design. In particular, the ground-breaking results are represented by: - the reliability and effectiveness of the methodology based on human EEG signals to objectively measure driver’s mental workload with respect to different road factors; - the successful approach for extracting latent features from multidimensional driving behaviour data using a deep learning technique, obtaining driving colour maps which represent an immediate visualization with potential impacts on road safety

A Context Aware Classification System for Monitoring Driver’s Distraction Levels

Understanding the safety measures regarding developing self-driving futuristic cars is a concern for decision-makers, civil society, consumer groups, and manufacturers. The researchers are trying to thoroughly test and simulate various driving contexts to make these cars fully secure for road users. Including the vehicle’ surroundings offer an ideal way to monitor context-aware situations and incorporate the various hazards. In this regard, different studies have analysed drivers’ behaviour under different case scenarios and scrutinised the external environment to obtain a holistic view of vehicles and the environment. Studies showed that the primary cause of road accidents is driver distraction, and there is a thin line that separates the transition from careless to dangerous. While there has been a significant improvement in advanced driver assistance systems, the current measures neither detect the severity of the distraction levels nor the context-aware, which can aid in preventing accidents. Also, no compact study provides a complete model for transitioning control from the driver to the vehicle when a high degree of distraction is detected. The current study proposes a context-aware severity model to detect safety issues related to driver’s distractions, considering the physiological attributes, the activities, and context-aware situations such as environment and vehicle. Thereby, a novel three-phase Fast Recurrent Convolutional Neural Network (Fast-RCNN) architecture addresses the physiological attributes. Secondly, a novel two-tier FRCNN-LSTM framework is devised to classify the severity of driver distraction. Thirdly, a Dynamic Bayesian Network (DBN) for the prediction of driver distraction. The study further proposes the Multiclass Driver Distraction Risk Assessment (MDDRA) model, which can be adopted in a context-aware driving distraction scenario. Finally, a 3-way hybrid CNN-DBN-LSTM multiclass degree of driver distraction according to severity level is developed. In addition, a Hidden Markov Driver Distraction Severity Model (HMDDSM) for the transitioning of control from the driver to the vehicle when a high degree of distraction is detected. This work tests and evaluates the proposed models using the multi-view TeleFOT naturalistic driving study data and the American University of Cairo dataset (AUCD). The evaluation of the developed models was performed using cross-correlation, hybrid cross-correlations, K-Folds validation. The results show that the technique effectively learns and adopts safety measures related to the severity of driver distraction. In addition, the results also show that while a driver is in a dangerous distraction state, the control can be shifted from driver to vehicle in a systematic manner

Monitoring fatigue and drowsiness in motor vehicle occupants using electrocardiogram and heart rate - A systematic review

Introdução: A fadiga é um estado complexo que pode resultar em diminuição da vigilância, frequentemente acompanhada de sonolência. A fadiga durante a condução contribui significativamente para acidentes de trânsito em todo o mundo, destacando-se a necessidade de técnicas de monitorização eficazes. Existem várias tecnologias para aumentar a segurança do condutor e reduzir os riscos de acidentes, como sistemas de deteção de fadiga que podem alertar os condutores à medida que a sonolência se instala. Em particular, a análise dos padrões de frequência cardíaca pode oferecer informações valiosas sobre a condição fisiológica e o nível de vigilância do condutor, permitindo-lhe compreender os seus níveis de fadiga. Esta revisão tem como objetivo estabelecer o estado atual das estratégias de monitorização para ocupantes de veículos, com foco específico na avaliação da fadiga pela frequência cardíaca e variabilidade da frequência cardíaca. Métodos: Realizamos uma pesquisa sistemática da literatura nas bases de dados Web of Science, SCOPUS e Pubmed, utilizando os termos veículo, condutor, monitoração fisiológica, fadiga, sono, eletrocardiograma, frequência cardíaca e variabilidade da frequência cardíaca. Examinamos artigos publicados entre 1 de janeiro de 2018 e 31 de janeiro de 2023. Resultados: Um total de 371 artigos foram identificados, dos quais 71 foram incluídos neste estudo. Entre os artigos incluídos, 57 utilizam o eletrocardiograma (ECG) como sinal adquirido para medir a frequência cardíaca, sendo que a maioria das leituras de ECG foi obtida através de sensores de contacto (n=41), seguidos por sensores vestíveis não invasivos (n=11). Relativamente à validação, 23 artigos não mencionam qualquer tipo de validação, enquanto a maioria se baseia em avaliações subjetivas de fadiga relatadas pelos próprios participantes (n=27) e avaliações feitas por observadores com base em vídeos (n=11). Dos artigos incluídos, apenas 14 englobam um sistema de estimativa de fadiga e sonolência. Alguns relatam um desempenho satisfatórios, no entanto, o tamanho reduzido da amostra limita a abrangência de quaisquer conclusões. Conclusão: Esta revisão destaca o potencial da análise da frequência cardíaca e da instrumentação não invasiva para a monitorização contínua do estado do condutor e deteção de sonolência. Uma das principais questões é a falta de métodos suficientes de validação e estimativa para a fadiga, o que contribui para a insuficiência dos métodos na criação de sistemas de alarme proativos. Esta área apresenta grandes perspetivas, mas ainda está longe de ser implementada de forma fiável.Background: Fatigue is a complex state that can result in decreased alertness, often accompanied by drowsiness. Driving fatigue has become a significant contributor to traffic accidents globally, highlighting the need for effective monitoring techniques. Various technologies exist to enhance driver safety and minimize accident risks, such as fatigue detection systems that can alert drivers as drowsiness sets in. In particular, measuring heart rate patterns may offer valuable insights into the occupant's physiological condition and level of alertness, and may allow them to understand their fatigue levels. This review aims to establish the current state of the art of monitoring strategies for vehicle occupants, specifically focusing on fatigue assessed by heart rate and heart rate variability. Methods: We performed a systematic literature search in the databases of Web Of Science, SCOPUS and Pubmed, using the terms vehicle, driver, physiologic monitoring, fatigue, sleep, electrocardiogram, heart rate and heart rate variability. We examine articles published between 1st of january 2018 and 31st of January 2023. Results: A total of 371 papers were identified from which 71 articles were included in this study. Among the included papers, 57 utilized electrocardiogram (ECG) as the acquired signal for heart rate (HR) measures, with most ECG readings obtained through contact sensors (n=41), followed by non-intrusive wearable sensors (n=11). Regarding validation, 23 papers do not report validation, while the majority rely on subjective self-reported fatigue ratings (n=27) and video-based observer ratings(n=11). From the included papers, only 14 comprise a fatigue and drowsiness estimation system. Some report acceptable performances, but reduced sample size limits the reach of any conclusions. Conclusions: This review highlights the potential of HR analysis and non-intrusive instrumentation for continuous monitoring of driver's status and detecting sleepiness. One major issue is the lack of sufficient validation and estimation methods for fatigue, contributing to the insufficiency of methods in providing proactive alarm systems. This area shows great promise but is still far from being reliably implemented

Driver Yawning Detection Based on Subtle Facial Action Recognition

Various investigations have shown that driver fatigue is the main cause of traffic accidents. Research on the use of computer vision techniques to detect signs of fatigue from facial actions, such as yawning, has demonstrated good potential. However, accurate and robust detection of yawning is difficult because of the complicated facial actions and expressions of drivers in the real driving environment. Several facial actions and expressions have the same mouth deformation as yawning. Thus, a novel approach to detecting yawning based on subtle facial action recognition is proposed in this study to alleviate the abovementioned problems. A 3D deep learning network with a low time sampling characteristic is proposed for subtle facial action recognition. This network uses 3D convolutional and bidirectional long short-term memory networks for spatiotemporal feature extraction and adopts SoftMax for classification. A keyframe selection algorithm is designed to select the most representative frame sequence from subtle facial actions. This algorithm rapidly eliminates redundant frames using image histograms with low computation cost and detects outliers by median absolute deviation. A series of experiments are also conducted on YawDD benchmark and self-collected datasets. Compared with several state-of-the-art methods, the proposed method has high yawning detection rates and can effectively distinguish yawning from similar facial actions

Red, white and blue highways: British travel writing and the American road trip in the late twentieth century

This study locates late-twentieth-century roadlogues (nonfiction, prose accounts of American road trips) by British writers within the tradition of the postwar American highway narrative in travel writing, novels, and film. It exposes the discursive structures and textual constraints underlying seven case studies published in the 1990s by comparing them to texts from various genres in diachronic and synchronic contexts. It contributes to scholarship on the American highway narrative, which largely overlooks British texts. It complements research on British travel writing, which tends to be biased towards pre-twentieth-century texts by travellers whose culture is in a dominant relation to that of travellees. It adds to postcolonial studies through analysis of representations of the other where otherness is reduced and complicated by a history of cultural exchange. The methodology combines several approaches including discourse theory, discourse analysis, narrative theory, feminist criticism, and theories of tourism. Three main areas are considered: identity, in relation to nationality and gender; the road writer's gaze, with regard to vehicles and roads; and intertextuality, on the margins (in maps) and inside roadlogues (in direct and indirect allusions). The study concludes that contemporary British roadlogues are in what is almost a subordinate relation to American highway narratives, evidenced by extensive influence of American texts. However, this subordination is qualified by joint ownership of western and New World myths, vestiges of imperial superiority, and selective deference by British writers. The latter is demonstrated through a consumer approach to American culture afforded by the episodic structure of the road trip and encouraged by the niche-oriented nature of the current market for travel writing. While American writers regard roadscapes with imperial eyes and experience the road trip as a rite of passage, contemporary Britons generally engage in superficial role play and remain untransformed by American highways