Vehicle safety and road safety are two important issues. They are related to each other and road accidents are mostly caused by driver distraction. Issues related to driver distraction like eating, drinking, talking to a passenger, using IVIS (In-Vehicle Information System) and thinking something unrelated to driving are some of the main reasons for road accidents. Driver distraction can be categorized into 3 different types: visual distraction, manual distraction and cognitive distraction. Visual distraction is when driver's eyes are off the road and manual distraction is when the driver takes one or both hands off the steering wheel and places the hand/s on something that is not related to the driving safety. Cognitive distraction whereas happens when a driver's mind is not on the road. It has been found that cognitive distraction is the most dangerous among the three because the thinking process can induce a driver to view and/or handle something unrelated to the safety information while driving a vehicle. This study proposes a physiological measurement to detect driver cognitive distraction. Features like lips, eyebrows, mouth movement, eye movement, gaze rotation, head rotation and blinking frequency are used for the purpose. Three different sets of experiments were conducted. The first experiment was conducted in a lab with faceLAB cameras and served as a pilot study to determine the correlation between mouth movement and eye movement during cognitive distraction. The second experiment was conducted in a real traffic environment using faceAPI cameras to detect movement on lips and eyebrows. The third experiment was also conducted in a real traffic environment. However, both faceLAB and faceAPI toolkits were combined to capture more features. A reliable and stable classification algorithm called Dynamic Bayesian Network (DBN) was used as the main algorithm for analysis. A few more others algorithms like Support Vector Machine (SVM), Logistic Regression (LR), AdaBoost and Static Bayesian Network (SBN) were also used for comparison. Results showed that DBN is the best algorithm for driver cognitive distraction detection. Finally a comparison was also made to evaluate results from this study and those by other researchers. Experimental results showed that lips and eyebrows used in this study are strongly correlated and have a significant role in improving cognitive distraction detection.

This thesis investigates the developpment of gender agreement in determiners and adjectives in the spontaneous speech of L2 French by five groups of Swedish learners: beginners at the university, secondary school students, university students, teacher candidates and PhD students. Different types of determiners are examined, such as definite and the indefinite articles. Adjectival agreement is studied in different positions in relation to the noun, such as the attributive anteposition, the attributive postposition and the predicative position. The aim is to identify the developmental sequence of gender agreement through a longitudinal study of learners at different levels of acquisition. The analysis is based on spoken language, i.e. 81 interviews belonging to the InterFra-corpus, Stockholm University. Our data also includes 8 oral productions from a control group of native speakers. The study is in three parts: one for the agreement between determiners and nouns, another for the agreement between adjectives and nouns and, finally, a study considering agreement between all three items within the noun phrase, i.e. determiner, noun and adjective (Det-N-Adj). A sequence of acquisition for gender agreement on determiners and adjectives is proposed based on the productions of four learner groups and compared to the six developmental stages suggested by Bartning and Schlyter (2004). Results have showed that there is an acquisition order of gender agreement in different parts of the nominal phrase, according to the type of determiner and the positions of the adjective. A qualitative analysis has shown a random use of gender agreement on determiners and some nouns are used with both genders on the determiner. Also, the type-token ratio is very low at the beginning of the acquisition, which partly explains the high accuracy rate (100 %). The study considering agreement between all three constituents within the noun phrase revealed that advanced learners have higher accuracy rate for gender agreement on adjectives within the noun phrase with the presence of a determiner that marks gender distinction (i.e. a non-elided, singular determiner). Results also showed that the feminine form of the adjectives remains difficult at higher acquisitional levels.