Field operational test of a seatbelt reminder system: Effects on driver behaviour and acceptance

The long-term effects on driver behaviour and acceptance of a seatbelt reminder system were examined in an on-road study. The system was capable of detecting seatbelt use in all seating positions and produced a two-stage visual and auditory warning if occupants were unrestrained. The effects of this system were evaluated alone and in combination with two other intelligent transport systems: intelligent speed adaptation and a following distance warning system. Twenty-three fleet car drivers drove an instrumented vehicle (SafeCar) for at least 16,500 km as part of their everyday driving. The results revealed that driver and passenger interaction with the seatbelt reminder system led to large and significant decreases in the percentage of trips where occupants were unbelted, in the percentage of total driving time spent unbelted, and in the time taken to fasten a seatbelt in response to system warnings. The seatbelt reminder system was rated by drivers as being useful, effective and socially acceptable, and led to a decrease in drivers’ subjective workload. These results were found even though the baseline pre-exposure seatbelt wearing compliance rates among participants were high.     

Sensorimotor representation and knowledge-based reasoning for spatial exploration and localisation

We investigate a hybrid system for autonomous exploration and navigation, and implement it in a virtual mobile agent, which operates in virtual spatial environments. The system is based on several distinguishing properties. The representation is not map-like, but based on sensorimotor features, i.e. on combinations of sensory features and motor actions. The system has a hybrid architecture, which integrates a bottom-up processing of sensorimotor features with a top-down, knowledge-based reasoning strategy. This strategy selects the optimal motor action in each step according to the principle of maximum information gain. Two sensorimotor levels with different behavioural granularity are implemented, a macro-level, which controls the movements of the agent in space, and a micro-level, which controls its eye movements. At each level, the same type of hybrid architecture and the same principle of information gain are used for sensorimotor control. The localisation performance of the system is tested with large sets of virtual rooms containing different mixtures of unique and non-unique objects. The results demonstrate that the system efficiently performs those exploratory motor actions that yield a maximum amount of information about the current environment. Localisation is typically achieved within a few steps. Furthermore, the computational complexity of the underlying computations is limited, and the system is robust with respect to minor variations in the spatial environments.     

Modélisation pour un environnement multimédia de l'apprentissage individualisé de la lecture

The aim of our research project is to study what contribution the new technologies can bring to teaching and learning. So, we have chosen a pluridiciplinary approach, combining the exploration of computer potentialities with the integration of research studies about the learning application. The application is a tutor module of AMICAL project (Architecture Multi-agent Interactive Compagnon pour l'Apprentissage de la Lecture). The issue is to determine and to explain the knowledge at work in a teaching process. To begin with, we shall explain the different decision-making processes, which characterize the teaching act. In a second part, we shall describe the processes of knowledge determination and modelling.     

Evolutionary autonomous agents and the naturalization of phenomenology

The phenomenological goal of grounding the content of conceptual thought in the background understanding of everyday, skillful coping was approached using evolutionary autonomous agent (EAA) methodology. The behavior of an EAA evolved to perform a specified motor task was identified with skillful coping. Changes in the dynamics of the EAA controller occurred when the EAA encountered an unexpected obstacle with loss of longer time scale components in its hierarchical temporal organization. These temporal changes are consistent with the phenomenological changes which we experience with breakdown during equipment use with our adoption of a more immediate, determinate stance. Since this latter experience is the basis of conceptual thought, the EAA paradigm goes some way in providing a naturalized explanation for the grounding of the content of conceptual thought in everyday, skillful coping in a manner that is physiologically plausible and phenomenologically accurate.     

A cognitively enhanced collaborative control architecture for an intelligent wheelchair: Formalization,implementation and evaluation

For people with mobility impairment, powered wheelchairs are able to extend independent transportation. However, most users find it difficult to operate the powered wheelchairs effectively. This can be due to variety of physical or cognitive impairments. Herein lies the motivation of developing intelligent wheelchairs that provide the user, assistance in control and navigation as and when required, supporting retention of residual skills. For better Human-Robot Interaction, such intelligent wheelchairs need to be cognitively enhanced. This paper propose a Cognitive Collaborative Architecture (CCA) for an intelligent wheelchair which is designed to assist the user as a team member. Whenever user finds it difficult to drive or control the wheelchair, CCA transfers control to the robotic agent which continues the human navigation strategy providing ‘assistance-as-required’.     

A conceptual framework for establishing trust in real world intelligent systems

Intelligent information systems that contain emergent elements often encounter trust problems because results do not get sufficiently explained and the procedure itself can not be fully retraced. This is caused by a control flow depending either on stochastic elements or on the structure and relevance of the input data. Trust in such algorithms can be established by letting users interact with the system so that they can explore results and find patterns that can be compared with their expected solution. Reflecting features and patterns of human understanding of a domain against algorithmic results can create awareness of such patterns and may increase the trust that a user has in the solution. If expectations are not met, close inspection can be used to decide whether a solution conforms to the expectations or whether it goes beyond the expected. By either accepting or rejecting a solution, the user’s set of expectations evolves and a learning process for the users is established. In this paper we present a conceptual framework that reflects and supports this process. The framework is the result of an analysis of two exemplary case studies from two different disciplines with information systems that assist experts in their complex tasks.     

The electric vehicle: A new driving experience involving specific skills and rules

The arrival of the electric vehicle (EV) on the market is one consequence of government measures to improve air quality and reduce CO² emissions. However, the EV has specific properties of use associated with its limited range and relative silence compared to normal vehicles, influencing the mobility behaviours of drivers and requiring them to develop some new driving abilities. This paper examines the behavioural modifications brought about by daily use of an electric vehicle at three different levels of driving activity: strategic, tactical and operational. The study collected and analyzed the self-reported behaviours (via questionnaires and travels dairies) of 36 Parisian private drivers, each of whom drove for six months an electric MINI E prototype. The results of the study show that driving an EV requires a learning phase to acquire the skills and knowledge necessary to operate the vehicle. At the strategic level of driving, drivers take into account the restricted range of the EV, implement a daily charge process, and develop new behaviours related to trip planning. The study also examines driver behaviour at the tactical level, in terms of driver interactions with other road users to deal with the silent nature of the EV, and at the operational level of driving, in terms of braking behaviour to master the regenerative braking function of the EV. The paper discusses the interactions between these three levels of driving activity.     

The impact of auditory continual feedback on take-overs in Level 3 automated vehicles

ObjectiveTo implement auditory continual feedback into the interface design of a Level 3 automated vehicle and to test whether gaze behavior and reaction times of drivers improved in take-over situations.BackgroundWhen required to assume manual control in take-over situations, drivers of Level 3 automated vehicles are less likely than conventional drivers to spot potential hazards, and their reaction time is longer. Therefore, it is crucial that the interface of Level 3 automated vehicles will be designed to improve drivers’ performance in take-over situations.MethodIn two experiments, participants drove a simulated route in a Level 3 automated vehicle for 35 min with one imminent take-over event. Participants’ gaze behavior and performance in an imminent take-over event were monitored under one of three auditory interface designs: (1) Continual feedback. A system that provides verbal driving-related feedback; (2) Persistent feedback. A system that provides verbal driving-related feedback and a persistent beep; and (3) Chatter feedback. A system that provides verbal non-driving-related feedback. Also, there was a control group without feedback.ResultsUnder all three auditory feedback designs, the number of drivers' on-road glances increased compared to no feedback, but none of the designs shortened reaction time to the imminent event.ConclusionIncreasing the number of on-road glances during automated driving does not necessarily improve drivers’ attention to the road and their reaction times during take-overs.ApplicationPossible implications for the effectiveness of auditory continual feedback should be considered when designing interfaces for Level 3 automated vehicles.     

The pattern theory of self in artificial general intelligence: A theoretical framework for modeling self in biologically inspired cognitive architectures

In an attempt to provide a unified account for a vast literature discussing a multiplicity of selves, Gallagher (2013) has proposed a pattern theory of self. Subsequent discussion on this account has led to a concern that the pattern theory, as originally presented, stands as a mere list of aspects that fails to explain how they are related in real-time. We suggest that one way to address these criticisms, and further develop the pattern theory of self is by exploring how it can be used to aid research on self in artificial general intelligence, especially in the context of biologically inspired cognitive architectures. We furthermore propose a conceptual implementation for actualizing such research in regards to the LIDA (Learning Intelligent Decision Agent) cognitive model.     

A neural cognitive architecture

It is difficult to study the mind, but cognitive architectures are one tool. As the mind emerges from the behaviour of the brain, neuropsychological methods are another method to study the mind, though a rather indirect method. A cognitive architecture that is implemented in spiking neurons is a method of studying the mind that can use neuropsychological evidence directly. A neural cognitive architecture, based on rule based systems and associative memory, can be readily implemented, and would provide a good bridge between standard cognitive architectures, such as Soar, and neuropsychology. This architecture could be implemented in spiking neurons, and made available via the Human Brain Project, which provides a good collaborative environment. The architecture could be readily extended to use spiking neurons for subsystems, such as spatial reasoning, and could evolve over time toward a complete architecture. The theory behind this architecture could evolve over time. Simplifying assumptions, made explicit, such as those behind the rule based system, could gradually be replaced by more neuropsychologically accurate behaviour. The overall task of collaborative architecture development would be eased by direct evidence of the actual neural cognitive architectures in human brains. While the initial architecture is biologically inspired, the ultimate goal is a biological cognitive architecture.