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This paper addresses the driver–automation shared driving control for lane keeping and obstacle avoidance of automated vehicles in highway traffic. The proposed shared control framework is established from a novel cooperative trajectory planning algorithm and a fuzzy steering controller. Based on polynomial functions, the cooperative trajectory planning is formulated by judiciously exploiting the information on the maneuver decision, the conflict management, and the driver monitoring. As a result, the planned trajectory of the vehicle is continuously adapted according to the driver's actions and intentions. By means of Lyapunov stability arguments, sufficient conditions in terms of linear matrix inequalities are given to design a Takagi–Sugeno fuzzy model-based controller. This robust steering controller provides a necessary assistive torque to track the planned vehicle trajectory. The new shared driving control framework allows reducing effectively the driver–automation conflict issue while offering the driver more freedom to swerve within a predefined lane. The advantages of the proposed approach are evaluated using both objective and subjective results, experimentally obtained from several human drivers and an advanced interactive dynamic driving simulator.

IF=5.673; International audience; Safety systems claim an in-depth study of vehicle motion and tire-ground interaction for the design of a partially automated driving vehicle. This article addresses the quasi-linear parameter-varying Luenberger interconnected fuzzy observer to estimate both longitudinal and lateral vehicle dynamics simultaneously. In a different manner from the commonplace state of the art of vehicle state observers that consider single driving motion, the proposed approach considers the coupled dynamics with the tire-ground interaction to estimate the most important states while reducing the complexity related to the observability and conservatism. This consideration leads to a nonlinear parameter-dependent interconnected model with unmeasured premise variables. Then, the Takagi-Sugeno fuzzy form is considered to deal with the nonlinearities of the vehicle longitudinal and lateral speeds and the slip velocities as well as the steering angle. The concept of “input to state stability” is exploited using fuzzy nonquadratic Lyapunov stability arguments to guarantee the boundness of the estimation errors. A refinement has been proposed through the so-called Polya's theorem aiming to further reduce the conservativeness. Finally, performances and effectiveness of the suggested approach are evaluated through hardware experiments performed with the well-known Sherpa dynamic car simulator under real-world driving situations.

IF=3.882; International audience; This paper presents a novel shared control concept for lane keeping assist (LKA) systems of intelligent vehicles. The core idea is to combine system perception with robust control so that the proposed strategy can successfully share the control authority between human drivers and the LKA system. This shared control strategy is composed of two parts, namely an operational part and a tactical part. Two local optimal-based controllers with two predefined objectives (i.e., lane keeping and conflict management) are designed in the operational part. The control supervisor in the tactical part aims to provide a decision-making signal which allows for a smooth transition between two local controllers. The control design is based on a human-in-the-loop vehicle system to improve the mutual driver-automation understanding, thus reducing or avoiding the conflict. The closed-loop stability of the whole driver-vehicle system can be rigorously guaranteed using the Lyapunov stability argument. In particular, the control design is formulated as an LMI optimization which can be easily solved with numerical solvers. The effectiveness of the proposed shared control method is clearly demonstrated through various hardware experiments with human drivers.

Recent research efforts on automated driving reveal the need to address driver’s interaction with vehicle automation. This paper focuses on the design of an override mode for automated driving systems, a topic little studied in literature. In this paper, a shared control framework for driver’s override of automatic steering control is proposed. This framework formulates the control transfer between the driver and the system as a constrained optimization problem which is solved online by a model predictive controller. At a higher level in the framework, the system detects driver’s lane-change intention so that the low-level controller can actively assist the driver in a lane-change maneuver. The whole framework was evaluated in a user test based on a driving simulator. The test results show that the proposed shared control framework has the advantage of allowing the driver to regain control with ease while ensuring the smoothness of control transition.

Recent years have witnessed rapid advancement of automated driving technologies. In this context, driver-vehicle cooperation as a new interaction paradigm offers an opportunity to improve the driving performance through the exploitation of human-automation synergy. This paper presents the implementation and evaluation of a new cooperation principle between the driver and the automated driving system. Within a use case concerning highway merging management, we describe the scenario modelling, system functional and HMI design. We evaluate the cooperation principle and the designed HMI through a user study on driving simulator. Based on test results, we discuss user’s perception of the cooperation principle and the potential of this principle to handle highway merging situations. Through this paper, we also aim to demonstrate the strengths of driving simulation in terms of interaction design for automated driving systems.

Driver distraction is an important factor of accidents. Not only in manual driving, the driver state information is also interesting to consider in automated driving systems in order to provide the driver with a suitable assistance level in respect to his evolving needs. The concepts of adaptive automation and human machine cooperation suggest that the authority of the automation should be adapted in real time according to the situation. However, contrary to existing studies that demonstrated the benefits for continuous fixed haptic feedback in the lane keeping task, evidence regarding the potential benefits of online adaptation of the level of haptic feedback is still lacking. In this framework a study is conducted in order to investigate the effects of online adjusting the authority level of a lane keeping assist system to match the driver’s distraction state while engaging in a demanding secondary task. The study took place in the SHERPA-lamih driving simulator. A comparison has been made between manual driving as a baseline, a Lane keeping Assist (LKA) providing a fixed and continuous haptic feedback and an Adaptive Lane Keeping Assist (ALKA). The analysis accounted for the driving performance and effort along with the subjective ratings of comfort, safety, control and workload. The results were consistent with the previous studies that showed the benefits of fixed haptic feedback under normal driving conditions. Moreover, the study established the benefits of adapting the level of haptic authority when the drivers were engaged in a secondary task. Furthermore, some design issues are highlighted for the design of effective adaptive automation.

International audience; This paper addresses the shared lateral control for both lane-keeping and obstacle avoidance tasks of a driver steering assist system (DSAS). In this work, we propose a novel approach to deal with the interactions between the human (driver) and the machine (DSAS) by introducing into the vehicle system a fictive nonlinear term representing the driver activity. In this way, the actions of the DSAS are computed according to the driver behaviors (actions and intentions). Based on Takagi-Sugeno control technique together with Lyapunov stability tools, the designed controller is able to handle a large range of variation of vehicle longitudinal speed. In particular, this controller can deal with the system state constraints and also the control input saturation. As will be discussed later, the consideration of these constraints into the control design improves significantly the closed-loop performance under various driving situations. The interests of the proposed method are validated by simulations.

This paper presents a new shared control method for lane keeping assist (LKA) systems of intelligent vehicles. The proposed method allows the LKA system to effectively share the control authority with a human driver by avoiding or minimizing the conflict situations between these two driving actors. To realize the shared control scheme, the unpredictable driver-automation interaction is explicitly taken into account in the control design via a fictive driver activity variable. This latter is judiciously introduced into the driver–road–vehicle system to represent the driver's need for assistance in accordance with his/her real-time driving activity. Using Lyapunov stability arguments, Takagi–Sugeno fuzzy model-based design conditions are derived to handle not only the time-varying driver activity variable, but also a large variation range of vehicle speed. Both simulation and hardware experiments are presented to demonstrate that the proposed control strategy together with a linear matrix inequality design formulation provide an effective tool to deal with the challenging shared steering control issue. In particular, a fuzzy output feedback control scheme is exploited to achieve the shared control goal without at least two important vehicle sensors. These physical sensors are widely employed in previous works to measure the lateral speed and the steering rate for the control design and real-time implementation. The successful results of this idea of sensor-reduction control has an obvious interest from practical viewpoint.

This paper is devoted to the development of a novel control law for an autonomous vehicle. The control goal is to perform lane keeping under multiple constraints, namely actuator saturation of the steering system, roads with unknown curvature, and uncertain lateral wind force. Such system constraints will be explicitly considered in the control design procedure. To achieve this goal, we propose a novel constrained Takagi-Sugeno control method using non-quadratic Lyapunov stability framework. The obtained non-parallel distributed compensation controller is not only able to handle various system constraints but also a large variation range of vehicle speed. Especially, by means of the well-known Taylor’s approximation method we can significantly reduce the numerical complexity of the proposed control method to ease the real-time control implementation. The control synthesis conditions are expressed in terms of linear matrix inequalities which can be efficiently solved with available solvers. The effectiveness of the proposed method is demonstrated with different simulation scenarios.

International audience; This paper describes the design of a new haptic shared steering control framework for automated driving systems. In this framework, the shared control problem is formulated as a constrained optimization problem which is solved online by a model predictive controller. Without driver's intervention, the system assumes automatic lane-keeping control. When the driver takes over control, by adapting the weight on the stage cost and implementing dynamic constraints, the framework ensures seamless control transfer from the system to the driver while conveying potential hazards through haptic feedback. Simulation results are presented to demonstrate the ability of this framework to handle control allocation and hazard warning.

IF=7.168; International audience; This paper addresses the shared lateral control between a human driver and a lane keeping assist system of intelligent vehicles for both lane keeping and obstacle avoidance. This control issue is very challenging in today's automotive industry due to the human-machine interaction involved in the control design. In this paper, we propose a new approach to consider such an interaction via a fictive driver activity parameter introduced into the road-vehicle system. Hence, the steering assistance actions can be computed according to the driver's real-time behaviors. The Takagi-Sugeno fuzzy control approach is proposed to deal with the time-varying driver activity parameter and vehicle speed. Especially, the concept of robust invariant set is exploited using Lyapunov arguments to handle theoretically both system state and control input limitations. Considering these system constraints in the control design procedure aims to improve the driver's safety and comfort. Experimental tests with a human driver and an advanced interactive dynamic driving simulator are conducted to show the effectiveness of the proposed method.

The work presented in this paper describes and discusses the principles of a haptic shared control between a human driver and an Electronic copilot (E-copilot) for a vehicle. The aim of the sharing control is to allow the driver to momentarily take control over the E-copilot without deactivating it nor being constrained, in order to deal with a specific situation such as avoiding an obstacle that has not been detected by the E-copilot. As the E-copilot acts simultaneously on the steering system with the driver, both have to be aware of one another's actions, which means bi-directional communication is essential. In this work, to achieve this goal, we consider the haptical interactions through the steering wheel. The torque applied by the driver on the steering system is used by the E-copilot to take into account the driver's actions while the E-copilot assistance torque is felt by the driver and used by him to understand the system's behavior. This low communication level strongly improves the cooperation between the driver and the E-copilot. The system takes into account the drivers actions thanks to a driver lane keeping model that is added to the road vehicle one in the controller synthesis step. This allows to introduce driver's interaction control variables in such a way that the E-copilot can consider conflicting objectives between the driver and the lane keeping task, and thus handle them. In order to highlight the assets of the approach, a comparison of the behaviors of a simple lane keeping E-copilot to that of a cooperative proposed here is given at the end of this paper. This comparison is achieved through computer simulations and experimental tests with a human driver carried out in the SHERPA-LAMIH interactive dynamic driving simulator. The results of these tests confirm the improvement of the level of cooperation between the human driver and the E-copilot and show that the cooperative E-copilot gives more authority to the human driver especially in hazardous situations.

This article shows the role that Fuzzy Space Windowing (FSW) and Multiple Correspondence Analysis (MCA) may play in the prospect of analyzing two different data sets separately, first and together, afterwards. This methodology is illustrated with a car-following example with 123 individuals. The first data set contains each individual's psychological profile, obtained through tests and questionnaires (23 variables); the second data set contains the signals acquired from real-life driving situations with an instrumented vehicle (6 time variables). The MCA of the first set yields individual typologies, for example, that conscientious drivers declare fewer errors in driving than impulsive and stressed drivers, or that drivers with high cognitive levels have a tendency to declare more violations than other drivers do. The MCA of the second set highlights inter-driver differences (e.g., speed, headway and pedal use). Other factors, such as infrastructure, also have an impact on driving behavior. For example, drivers have a tendency to drive under the speed limit on urban roads and over the speed limit on highways. A third and final MCA is then performed on the most informative variables found with the two previous MCAs.

International audience; This article introduces a functional architecture of an automated driving system defined by taking into account the theoretical knowledge on the driving task modeling and the principles of humanmachine cooperation. The necessary functionalities to ensure the interaction and cooperation between the system and the driver who share the driving task on the operational, tactical and strategic levels were gathered. A multi-level cooperative interaction concept to continuously share control between the driver and the system is introduced. The architecture modules have been implemented and tested in an experiment on a driving simulator with an expert driver. Results show an improved interaction with the driver thanks to the continuous and dynamic interference management.

This paper describes an adaptive vehicle longitudinal trajectory prediction method for automated highway driving applications. A major strength of this method is that it can cope with highly dynamic situations in which the constant acceleration (CA) assumption cannot guarantee long term prediction accuracy. In this method, a quintic polynomial is used to model the longitudinal dynamics of a vehicle that is maneuvering. The decision to switch to it from the CA model is formulated as a maneuver detection problem. A maneuver is detected through monitoring measurement innovations of a Kalman filter that tracks target longitudinal states. The longitudinal jerk, as a dynamic characteristic of a maneuver is also estimated from measurement innovations. Finally the estimated jerk and context information are incorporated into the quintic polynomial model. The overall approach was tested on recorded human driving data from a simulator in a dynamic highway merging scenario. The results show the proposed method has higher prediction accuracy than the CA based method in such a dynamic scenario.

In most human component system studies performed in simulators, several factors (or independent variables) (at least two, i.e., individual and time) and many variables (or dependent variables) are present. Large and complex databases have to be analyzed. Instead of using rather automatic procedures, this article suggest that, for a very first analysis at least, the human being must be present and he/she must choose a method being adapted to the data, which is different to run a method supposing that the data fit such or such model. This article suggests starting the analysis while keeping both the multifactorial (MF) and multivariate (MV) aspects. To achieve this aim, with the possibility to show nonlinear relationships, a MFMV exploration of the experimental database is performed using the pair (fuzzy space windowing, Multiple Correspondence Analysis). Then may come an inference analysis. This long (due to multiple large graphical views) but rich procedure is illustrated and discussed using a car driving study example.

This paper explains the key role played by windowing in the preliminary analysis of multifactor and multivariate (MFMV) databases. The explanation is based on the general case of a database featuring quantitative or qualitative measurement variables and a hyperparallelepipedic structure in which the directions correspond to the factors. In order to maintain the MFMV aspects of this data structure, the windowing approach as described in this paper does not reduce the information as much as most of the basic non-windowing summarizing procedures using the standard statistical indicators. First, the data in each cell of the hyperparallelepiped are transformed into membership values that can be averaged over factors, such as time or individuals. Then, these membership values may be potentially investigated into with several graphic techniques; for this paper, multiple correspondence analysis (MCA) was chosen. The presentation fall into two parts. First, a didactic example based on a simulated data set describes the approach in comparison with more traditional approaches, and then a real data set, with multidimensional signals recorded for 34 subjects in 15 experimental overtaking situations, is used to demonstrate the power of the “space windowing/MCA” pair on a large real database. Next, the discussion section weighs out the pros and the cons of using space windowing to perform a preliminary analysis of a large MFMV database in studies of human component systems.

In most human component system studies performed in simulators, several factors (at least two, i.e. individual and time) and many variables are present. This article suggests starting the analysis while keeping both the multifactorial (MF) and multivariate (MV) aspects. To achieve this aim, with the possibility to show nonlinear relationships, a MFMV exploration of the experimental database is performed using the pair (fuzzy space windowing, Multiple Correspondence Analysis). Then may come an inference analysis. This long (due to multiple large graphical views) but rich procedure is illustrated and discussed using a car driving study example.

The continuous increasing number of car embedded systems requires to wonder about the compatibility of the use of such systems while the driving. Doesn 't their use exceed the driver capacity? To bring answer elements to this question, this paper presents a methodology for the development of a real-time workload estimator. This methodology is based on the simultaneous analysis of objective and subjective data by the mean of an exploratory multivariate approach. Objective data are composed of driving performance indicators while subjective data are results from subjective workload assessments. The data were collected during an experimentation set on a driving situation which was designed to induce several levels of task demand.

Since a few years ago, the tendency has been towards increasing the number of in-vehicle Driver Assistance Systems (DAS). These systems can be more or less useful for the driving but before being integrated in a wide range of vehicles, the compatibility with the driving task must be discussed. A previous study, using on-line subjective workload assessment has shown the limits of such an approach. This paper describes a second experiment whose goal is the design of a workload indicator based on objective and subjective measures by the study of the driving performance when the driver becomes overloaded.

The development of ADAS (Advance Driver Assistance Systems) increasing security of the driver implies the use of a rigorous method for their design and evaluation. Driving simulators allow rapid prototyping of ADAS systems and ease their evaluation. However, the conducted experimental tests lead to huge amount of heterogeneous data and thus require a complex data analysis process. In this paper, we propose the use of a statistical exploratory data analysis method to explore large experimental data bases in order to put forward the most significant information they include. This method is illustrated on an application to the evaluation of a collision avoidance system in car driving.

International audience; This paper is devoted to the development of a shared lateral control strategy for a Driver Steering Assist System (DSAS) that can share the authority with the driver. Up to now, this control issue is still an open research subject in automotive industry due to the complex interactions according to different driving situations between the Human (driver) and the Machine (DSAS). In this work, such interactions are handled by introducing into the vehicle system a fictive time-varying term representing the driver activity. In this way, the actions of the DSAS are computed in function of the driver behaviors (actions and intentions). Using Takagi-Sugeno control technique in the framework of Lyapunov stability theorem, the designed controller is able to handle a large range of variation of vehicle longitudinal speed. Moreover, the proposed controller requires only measured output signals for the design procedure and implementation. The effectiveness of the proposed method is demonstrated with different driving scenarios.

The LAMIH particularly the research group Human-Machine Systems, has studied human-machine cooperation for many years, in a variety of contexts where safety is essential. Our approach is multi-disciplinary and uses different models and methods to elaborate symbolic and formal representations of human-machine cooperation. Our principal objective is to propose and evaluate a semi-formal framework for modeling cooperative activities between human or artificial agents, each of which has a different level of ability, reliability or adaptability. In the present study, human-machine cooperation was analyzed in order to define and evaluate a system capable of taking the full control of an automobile so as to avoid traffic accidents.

This second article proposes a data analysis example where the time units are related to the eye behavior, i.e. fixations, slow movements, saccades and blinks. The measurement variables are given through an oculometer, i.e. horizontal and vertical position (in relation to the scene) of the cornea reflect. Each type of time unit is characterized using both physiological and behavioral eye specificities but systematically yields sets of membership values to space windows, the windowing of the initial quantitative scales being performed using fuzzy limits. The study of the membership values is realised using the correspondence analysis, the table rows corresponding to individual time windows and the columns to space windows. An inference analysis is performed then.

The problem of data characterization of quantitative and qualitative measurement scales is stated in the context of an exploratory multivariate statistical analysis. An example from a car driving study is considered where the quantitative data correspond to the car and head movements, while the qualitative data correspond to objects being viewed--road, bridge, sign-post, etc. For each of these two sets, the literature is analyzed first in terms of data characterizing methods and relationship obtaining methods. Then we propose to evaluate and compare nine quantitative data characteriing methods: five corresponding to classic statistical indicators, two to crisp space windowing with either two or three windows, and two on fuzzy windowing with either two or three windows. Logically the last method appears as the best (according to our evaluation procedure). Then we propose a bidimensional fuzzy windowing instead of a crisp one to characterize the gaze positions. Finally the multiple correspondence analysis is used to investigate the membership value averages obtained from the characterization stage.

International audience; In this work, an Unknown Input Proportional Multi-Integral Takagi-Sugeno (UIPMIT-S) observer is designed to estimate both the lateral vehicle states and the driver's torque considered as unknown input. The observer design is formulated in LMI framework to lead all the T-S observer sub-models poles in a D-stable region. The LMI constraints related to the D-stable region force the convergence of the estimation errors with a controllable decay rate and limit overshooting in the transient phase. The proposed approach is validated first, in numerical simulations, and after using the LAMIH driving dynamic simulator SHERPA.

This paper deals with the application of techniques coming from Reliability Centred Maintenance to detect time evolutions of a driver's behaviour in a long duration simulated driving task. It describes the problematic raised by such systems, where a human operator is involved in the control loop, and proposes an approach of solution using exploratory data analysis techniques.

This paper presents a proposed method to study the driver's behavior and its awareness defects on motorway. The method is based on the calculation of the “guidance task demand” of car driving. Guidance during driving can be seen as a continuous tuning of the speed and position of the car by the driver. The formalization of guidance demand has been implemented by three time pressures representing the road anticipation and the near space collision avoidance. A preliminary validation of this method is carried out by some tests on a driving simulator.

International audience; In this work, a driving assistance system (DAS) sharing the control of a vehicle with a human driver is proposed. The cooperation degree between the DAS and the human driver is in the center of this work with the aims of reducing conflict between them. A lane keeping driver model is added to the road-vehicle one in order to achieve this goal. Fuzzy Takagi-Sugeno (T-S) optimal controller is driven considering a varying vehicle longitudinal velocity. The system is evaluated regarding both the human-machine interaction point of view in hazardous situation and the lane keeping performance. Firstly, numerical simulations carried out in Matlab/Simulink are illustrated, and after, the proposed DAS is tested on the LAMIH-SHERPA dynamic simulator with a human driver. The obtained results show the effectiveness of the proposed approach.

The continuous increasing number of car embedded systems requires to wonder about the compatibility of the use of such systems while the driving. The richness of their interface, their complexity and their numbers need to ensure that their use doesn't exceed the driver capacity. To bring elements of answer to this question, this paper presents a methodology for the development of a real-time workload estimator. This methodology is based on the simultaneous analysis of objective and subjective data by the mean of an exploratory multivariate approach. Objective data are composed of driving performance indicators while subjective data are results from subjective workload assessments. The data were collected during an experimentation set on a driving situation which was designed to induce several levels of task demand.

Since few years, the tendency is in the increasing of in-vehicle system number. These systems can be more or less useful for the driving but before being integrated in a wide range of vehicle, the compatibility with the driving task must be discussed. A previous study, using on-line subjective workload assessment had shown the limits of such approach. This paper describes a second experiment which goal is the design of an workload indicator based on objective and subjective measures by the study of the driving performance when the driver become overloaded. After a review of the different second tasks used to evaluate the impact of cell phone on driving and the different performance indicators used, the set up of the experiment will be discussed

This paper presents the first experiment performed in the frame of a French research national program in the ground transportation: ARCOS. The aim of this study is the development of an advanced cooperative system able to modify its parameters in function of the driver's behaviour. The first stage of this study consists in the building of an experiment which aims to determine the driver's behaviour during typical traffic situations without assistance tools and, from this study, to determine the variables able to differentiate various classes of comportment during an overtaking. These experimentations have been carried out on SHERPA driving simulator. 39 subjects had to drive on a combination of A-roads and B-roads, with various traffic situations. During these experimentations, drivers were filmed and variables, characteristic of vehicle position in relation to the road and of the driver's actions on the vehicle, were recorded. The Multiple Correspondence Analysis (MCA) was applied on theses variables, before hand cut into space modalities, to classify driving behaviour during different traffic situations and to identify the best set of recorded variables that allows to discriminate those classes. This article presents results of a study on the first part of a "classic" overtaking. The MCA results show that three variables are able to classify all drivers in function of their driving behaviour during this phase. In the future, the analysis of the others situations will allow to define behaviour styles of the drivers in function of traffic behaviour.

Many studies have been conducted to try to put forward measurable indicators that could help in the design of alerting devices used to reliably monitor severe driver drowsiness. However, most of these indicators are either too sensitive to environment modifications (performance indicators), invasive for the driver (EEG, EOG...) or are complex and expensive to measure (Eye blinks, PERCLOS...). This study aims at proposing a set of indicators related to driver performance and physiology that could be efficient for monitoring driver drowsiness and be easy to measure. Thirty two subjects took part to a long duration simulated highway trip (336 km) during which performance and physiological data were recorded The analysis of these data puts forward two sets of indicators : firstly, a set of performance indicators whose evolutions are consistent with those described in literature. They ensure the detection of drowsiness during the experiment. Secondly, a set of physiological indicators : the head movements dispersion. This has revealed itself to be a very good indicator as it evolves in the same way as the performance ones on long lasting periods while being poorly influenced by the road environment, contrary to most performance indicators.

The paper shows that multiple correspondence analysis (MCA) is well suited to characterize the driver-vehicle-environment system. First, the complexity of this system is explained. Then partial models of this system are exposed followed by characterizing the methods. Among these methods, MCA has the required features to reveal what are the most relevant variables that could better characterize the system and to associate variable value tendencies with functioning modalities of the system. An example of MCA applied to characterize four driving situations is given.

This paper presents a new formalism for the design of automated manufacturing systems (AMS). This formalism is based on results from artificial intelligence and cognitive psychology works on human problem solving strategies. It is described in a validation context which is a small tooling model that allows to put forward its assets. A computer support of this formalism is also presented. >

This paper deals with the description of a system performing an automatic data extraction from the visual activity recordings of a driver in a simulated driving context. Main modules of this system are described with their related principles and problems. From the hardware point of view there is the driving simulator, the eye tracker and the developed synchronization device. From the software one, there is the numerical raw data filtering process, the ray tracing module and the decision software. The validation of this system puts forward its good capacity to reproduce the manual data extraction of an expert operator from video recordings. Its first application to a long duration driving experiment points out the large amount of time spared in data extraction thanks to this system.

Future driving assistance systems must be designed in order to guarantee a smooth steering control action of the controller continuously, considering the driver in-the-loop and without generating negative interference. This paper proposes the design of a shared lateral control in the framework of the active safety systems that integrates the coordination of the authority between human driver and automatic copilot. The vehicle steering assist controller is designed using a driver model in order to take into account the driver's intentions in particular during curve negotiation. This approach minimizes controller intervention while the driver is awake and steers correctly. To reduce the complexity in control computations, a simplified linear combination of the system state is determined via an optimal control by solving a Linear Quadratic Regulator (LQR) problem. A decision making algorithm for the control authority shifting between the driver and the electronic copilot is implemented and the trade-off between the accuracy of lane following and ratio of system interference is investigated.

International audience; The work described in this paper is part of a research program named ABV (Low Speed Automation) where the goal is the automation of road vehicle at low speed while ensuring the sharing of driving between the driver and the assistance. This paper focuses on the problem of human-machine cooperation in the specific context of vehicle driving, with a view of shared control between driver and automation, considering the acceptability of the system and the driver distractions and drowsiness.