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Étude et modélisation du conducteur pour la conception de systèmes d’assistance à la conduite / Driver study and modeling for driving assistance systems developementAbrashov, Sergey 21 March 2017 (has links)
Le confort et la sécurité de conduite sont les principaux critères de vente de l’industrie automobile actuelle. De nombreux projets de recherche sont mis en place afin de les améliorer et pour faire face aux mesures de législation et de contrôle mises en place pour réduire le nombre d’accidents routiers. Les mesures semblent efficaces : en France,par exemple, le nombre des accidents mortels diminue de 11% en moyenne chaque année.D’après de récentes études, 90% de ces accidents ont pour cause le facteur humain et il devient nécessaire de prendre en compte le conducteur pendant la phase de conception des systèmes de sécurité et d’aide à la conduite. Une assistance à la conduite basée sur le partage du contrôle du véhicule entre le conducteur et l’automate est un des axes de recherche privilégiés de l’industrie, notamment pour améliorer la sécurité.Il est maintenant devenu possible de récupérer une très grande quantité d’information sur l’environnement et de réaliser une interaction intelligente entre les différents acteurs du trafic. Les techniques existantes permettent même la conduite partagée entre le véhicule et le conducteur et, dans un horizon plus lointain, d’envisager un véhicule complètement autonome. Dans les situations de conduite automatisée, un algorithme adéquat est nécessaire pour remplacer le conducteur.L’intérêt principal de cette recherche se situe au niveau de l’interaction entre le conducteur et l’algorithme d’assistance ou de conduite automatisée. Il est indispensable de connaître et de comprendre le comportement du conducteur dans sa façon de conduire,de contrôler le véhicule et de prendre une décision. Par conséquent, un modèle adapté aux besoins est nécessaire. En plus de la nécessité de disposer d’un modèle suffisamment riche pour décrire le comportement de différents conducteurs dans les situations routières les plus fréquentes, il est indispensable de disposer d’une méthode de synthèse des systèmes d’assistance sur la base de ces modèles. / Driving comfort and safety are the main points of interest for the automotive industry. Many research projects were realized in order to improve them and to reduce the number of road accidents. The measures seem to be effective : in France, for example, the number of fatal accidents decreases by 11% on average each year. According to recent studies, 90% of these accidents are caused by the human factor. As a consequence, it becomes necessary to take the driver into account during the design of driving assistance systems. An assistance based on the control sharing between the driver and the automatic pilot is one of the main topics of research and a way to improve safety. It has now become possible to recover a very large amount of information on the environment and to achieve intelligent interaction between the various actors in the traffic. Existing technologies even allow imagining a completely autonomous driving in a more distant horizon. In such a situation, an adequate algorithm is required to replace the human driver.The main interest of this research is the interaction between the driver and the driver assistance algorithm. It is essential to know and to understand the behavior of the humanin his / her way to control the vehicle and to make a decision. Therefore, his model is necessary. Moreover, it is essential to develop a design method for such assistance systems on the basis of these driver models.
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Development of Personalized Lateral and Longitudinal Driver Behavior Models for Optimal Human-Vehicle Interactive ControlSchnelle, Scott C. January 2016 (has links)
No description available.
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Driver Modeling Based on Driving Behavior and Its Evaluation in Driver IdentificationMiyajima, Chiyomi, Nishiwaki, Yoshihiro, Ozawa, Koji, Wakita, Toshihiro, Itou, Katsunobu, Takeda, Kazuya, Itakura, Fumitada January 2007 (has links)
No description available.
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Adaptive and Passive Non-Visual Driver Assistance Technologies for the Blind Driver Challenge®D'Angio, Paul Christopher 31 May 2012 (has links)
This work proposes a series of driver assistance technologies that enable blind persons to safely and independently operate an automobile on standard public roads. Such technology could additionally benefit sighted drivers by augmenting vision with suggestive cues during normal and low-visibility driving conditions. This work presents a non-visual human-computer interface system with passive and adaptive controlling software to realize this type of driver assistance technology. The research and development behind this work was made possible through the Blind Driver Challenge® initiative taken by the National Federation of the Blind.
The instructional technologies proposed in this work enable blind drivers to operate an automobile through the provision of steering wheel angle and speed cues to the driver in a non-visual method. This paradigm imposes four principal functionality requirements: Perception, Motion Planning, Reference Transformations, and Communication. The Reference Transformation and Communication requirements are the focus of this work and convert motion planning trajectories into a series of non-visual stimuli that can be communicated to the human driver.
This work proposes two separate algorithms to perform the necessary reference transformations described above. The first algorithm, called the Passive Non-Visual Interface Driver, converts the planned trajectory data into a form that can be understood and reliably interacted with by the blind driver. This passive algorithm performs the transformations through a method that is independent of the driver. The second algorithm, called the Adaptive Non-Visual Interface Driver, performs similar trajectory data conversions through methods that adapt to each particular driver. This algorithm uses Model Predictive Control supplemented with Artificial Neural Network driver models to generate non-visual stimuli that are predicted to induce optimal performance from the driver. The driver models are trained online and in real-time with a rapid training approach to continually adapt to changes in the driver's dynamics over time.
The communication of calculated non-visual stimuli is subsequently performed through a Non-Visual Interface System proposed by this work. This system is comprised of two non-visual human computer interfaces that communicate driving information through haptic stimuli. The DriveGrip interface is pair of vibro-tactile gloves that communicate steering information through the driver's hands and fingers. The SpeedStrip interface is a vibro-tactile cushion fitted on the driver's seat that communicates speed information through the driver's legs and back. The two interfaces work simultaneously to provide a continuous stream of directions to the driver as he or she navigates the vehicle. / Ph. D.
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