https://sam.ensam.eu:443 The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material. Thu, 12 Mar 2026 14:16:53 GMT 2026-03-12T14:16:53Z http://hdl.handle.net/10985/14040 Dealing with the right of way in advanced traffic simulation. A characterization of drivers’ behaviours in a multi-agent approach REFFET, Bérénice; MATHIEU, Philippe; KEMENY, Andras; VAILLEAU, Benjamin; NONGAILLARD, Antoine; MOHELLEBI, Hakim With the emergence of ADAS and autonomous vehicle, the need of simulation software to test advanced systems and models is unavoidable. The objective of this work is to characterize, in a multi-agent approach, the traffic vehicles behaviours, and to model them in a versatile traffic simulator, serving as a testing tool for integration in the traffic module of the SCANeR StudioTM simulation software. The algorithm will bring more natural interactions between vehicles in simulation and allow the emergence of new relevant situations for autonomous vehicles, observable in real, such as collision risk situations or even accidents, which have for now to be scripted in scenarios and do not occur naturally. Sun, 01 Jan 2017 00:00:00 GMT http://hdl.handle.net/10985/14040 2017-01-01T00:00:00Z REFFET, Bérénice MATHIEU, Philippe KEMENY, Andras VAILLEAU, Benjamin NONGAILLARD, Antoine MOHELLEBI, Hakim With the emergence of ADAS and autonomous vehicle, the need of simulation software to test advanced systems and models is unavoidable. The objective of this work is to characterize, in a multi-agent approach, the traffic vehicles behaviours, and to model them in a versatile traffic simulator, serving as a testing tool for integration in the traffic module of the SCANeR StudioTM simulation software. The algorithm will bring more natural interactions between vehicles in simulation and allow the emergence of new relevant situations for autonomous vehicles, observable in real, such as collision risk situations or even accidents, which have for now to be scripted in scenarios and do not occur naturally. http://hdl.handle.net/10985/14057 Solving the Constrained Problem in Model Predictive Control Based Motion Cueing Algorithm with a Neural Network Approach RENGIFO, Carolina; PAILLOT, Damien; MOHELLEBI, Hakim; KEMENY, Andras; CHARDONNET, Jean-Rémy Because of the critical timing requirement, one major issue regarding model predictive control-based motion cueing algorithms is the calculation of real-time optimal solutions. In this paper, a continuous-time recurrent neural network-based gradient method is applied to compute the optimal control action in real time for an MPCbased MCA.We demonstrate that by implementing a saturation function for the constraints in the decision variables and a regulation for the energy function in the network, a constrained optimization problem can be solved without using any penalty function. Simulation results are included to compare the proposed approach and substantiate the applicability of recurrent neural networks as a quadratic programming solver. A comparison with another QP solver shows that our method can find an optimal solution much faster and with the same precision. Mon, 01 Jan 2018 00:00:00 GMT http://hdl.handle.net/10985/14057 2018-01-01T00:00:00Z RENGIFO, Carolina PAILLOT, Damien MOHELLEBI, Hakim KEMENY, Andras CHARDONNET, Jean-Rémy Because of the critical timing requirement, one major issue regarding model predictive control-based motion cueing algorithms is the calculation of real-time optimal solutions. In this paper, a continuous-time recurrent neural network-based gradient method is applied to compute the optimal control action in real time for an MPCbased MCA.We demonstrate that by implementing a saturation function for the constraints in the decision variables and a regulation for the energy function in the network, a constrained optimization problem can be solved without using any penalty function. Simulation results are included to compare the proposed approach and substantiate the applicability of recurrent neural networks as a quadratic programming solver. A comparison with another QP solver shows that our method can find an optimal solution much faster and with the same precision. http://hdl.handle.net/10985/16315 Feasibility Analysis For Constrained Model Predictive Control Based Motion Cueing Algorithm RENGIFO, Carolina; MOHELLEBI, Hakim; PAILLOT, Damien; KEMENY, Andras; CHARDONNET, Jean-Rémy This paper deals with motion control for an 8-degree-of-freedom (DOF) high performance driving simulator. We formulate a constrained optimal control that defines the dynamical behavior of the system. Furthermore, the paper brings together various methodologies for addressing feasibility issues arising in implicit model predictive control-based motion cueing algorithms. The implementation of different techniques is described and discussed subsequently. Several simulations are carried out in the simulator platform. It is observed that the only technique that can provide ensured closed-loop stability by assuring feasibility over all prediction horizons is a braking law that basically saturates the control inputs in the constrained form. Tue, 01 Jan 2019 00:00:00 GMT http://hdl.handle.net/10985/16315 2019-01-01T00:00:00Z RENGIFO, Carolina MOHELLEBI, Hakim PAILLOT, Damien KEMENY, Andras CHARDONNET, Jean-Rémy This paper deals with motion control for an 8-degree-of-freedom (DOF) high performance driving simulator. We formulate a constrained optimal control that defines the dynamical behavior of the system. Furthermore, the paper brings together various methodologies for addressing feasibility issues arising in implicit model predictive control-based motion cueing algorithms. The implementation of different techniques is described and discussed subsequently. Several simulations are carried out in the simulator platform. It is observed that the only technique that can provide ensured closed-loop stability by assuring feasibility over all prediction horizons is a braking law that basically saturates the control inputs in the constrained form. http://hdl.handle.net/10985/20800 Driving simulator study of the relationship between motion strategy preference and self-reported driving behavior RENGIFO, Carolina; MOHELLEBI, Hakim; PAILLOT, Damien; KEMENY, Andras; CHARDONNET, Jean-Rémy Faithful motion restitution in driving simulators normally focuses on track monitoring and maximizing the platform workspace by leaving aside the principal component—the driver. Therefore, in this work we investigated the role of the motion perception model on motion cueing algorithms from a user’s viewpoint. We focused on the driving behavior influence regarding motion perception in a driving simulator. Participants drove a driving simulator with two different configurations: (a) using the platform dynamic model and (b) using a supplementary motion perception model. Both strategies were compared and the participants’ data were classified according to the strategy they preferred. To this end, we developed a driving behavior questionnaire aiming at evaluating the self-reported driving behavior influence on participants’ motion cueing preferences. The results showed significant differences between the participants who chose different strategies and the scored driving behavior in the hostile and violations factors. In order to support these findings, we compared participants’ behaviors and actual motion driving simulator indicators such as speed, jerk, and lateral position. The analysis revealed that motion preferences arise from different reasons linked to the realism or smoothness in motion. Also, strong positive correlations were found between hostile and violation behaviors of the group who preferred the strategy with the supplementary motion perception model, and objective measures such as jerk and speed on different road segments. This indicates that motion perception in driving simulators may depend not only on the type of motion cueing strategy, but may also be influenced by users’ self-reported driving behaviors. Fri, 01 Jan 2021 00:00:00 GMT http://hdl.handle.net/10985/20800 2021-01-01T00:00:00Z RENGIFO, Carolina MOHELLEBI, Hakim PAILLOT, Damien KEMENY, Andras CHARDONNET, Jean-Rémy Faithful motion restitution in driving simulators normally focuses on track monitoring and maximizing the platform workspace by leaving aside the principal component—the driver. Therefore, in this work we investigated the role of the motion perception model on motion cueing algorithms from a user’s viewpoint. We focused on the driving behavior influence regarding motion perception in a driving simulator. Participants drove a driving simulator with two different configurations: (a) using the platform dynamic model and (b) using a supplementary motion perception model. Both strategies were compared and the participants’ data were classified according to the strategy they preferred. To this end, we developed a driving behavior questionnaire aiming at evaluating the self-reported driving behavior influence on participants’ motion cueing preferences. The results showed significant differences between the participants who chose different strategies and the scored driving behavior in the hostile and violations factors. In order to support these findings, we compared participants’ behaviors and actual motion driving simulator indicators such as speed, jerk, and lateral position. The analysis revealed that motion preferences arise from different reasons linked to the realism or smoothness in motion. Also, strong positive correlations were found between hostile and violation behaviors of the group who preferred the strategy with the supplementary motion perception model, and objective measures such as jerk and speed on different road segments. This indicates that motion perception in driving simulators may depend not only on the type of motion cueing strategy, but may also be influenced by users’ self-reported driving behaviors. http://hdl.handle.net/10985/20978 Impact of Human-Centered Vestibular System Model for Motion Control in a Driving Simulator RENGIFO, Carolina; MOHELLEBI, Hakim; KEMENY, Andras; CHARDONNET, Jean-Rémy This study presents a driving simulator experiment to evaluate three different motion cueing algorithms based on model predictive control. The difference among these motion strategies lies in the type of mathematical model used. The first one contains only the dynamic model of the platform, while the others integrate additionally two different vestibular system models. We compare these three strategies to discuss the tradeoffs when including a vestibular system model in the control loop from the user's viewpoint. The study is conducted in autonomous mode and in free driving mode, as both play an important role in motion cueing validation. A total of 38 individuals participated in the experiment; 19 drove the simulator in free driving mode and the remaining using the autonomous driving mode. For both driving modes, substantial differences is observed. The analysis shows that one of the vestibular system models is suitable for driving simulators, as it thoroughly restores high-frequency accelerations and is well noted by the participants, especially those in the free driving mode. Further tests are needed to analyze the advantages of integrating the chosen vestibular system model in the control design for motion cuieng algorithms. Regarding the autonomous mode, further research is needed to examine the influence of the vestibular system model on the motion performance, as the behavior of the autonomous model may implicitly interfere with subjective assessments. Fri, 01 Jan 2021 00:00:00 GMT http://hdl.handle.net/10985/20978 2021-01-01T00:00:00Z RENGIFO, Carolina MOHELLEBI, Hakim KEMENY, Andras CHARDONNET, Jean-Rémy This study presents a driving simulator experiment to evaluate three different motion cueing algorithms based on model predictive control. The difference among these motion strategies lies in the type of mathematical model used. The first one contains only the dynamic model of the platform, while the others integrate additionally two different vestibular system models. We compare these three strategies to discuss the tradeoffs when including a vestibular system model in the control loop from the user's viewpoint. The study is conducted in autonomous mode and in free driving mode, as both play an important role in motion cueing validation. A total of 38 individuals participated in the experiment; 19 drove the simulator in free driving mode and the remaining using the autonomous driving mode. For both driving modes, substantial differences is observed. The analysis shows that one of the vestibular system models is suitable for driving simulators, as it thoroughly restores high-frequency accelerations and is well noted by the participants, especially those in the free driving mode. Further tests are needed to analyze the advantages of integrating the chosen vestibular system model in the control design for motion cuieng algorithms. Regarding the autonomous mode, further research is needed to examine the influence of the vestibular system model on the motion performance, as the behavior of the autonomous model may implicitly interfere with subjective assessments. http://hdl.handle.net/10985/13054 Head Motion parallax effect on driving performances when using an AR-HUD: Simulation Study on Renault’s CARDs Simulator HALIT, Lynda; KEMENY, Andras; LE GOUGUEC, Armand; MOHELLEBI, Hakim; MERIENNE, Frédéric Augmented Reality information on Head-Up display (AR-HUD) in a car can be relevant for visual aid and for strengthening the safety of the driver. However, some display Parameters are necessary to guarantee the good perception of these information and the driving environment. In this study, we are interested on head motion parallax, and specifically the ones generated with lateral head movements of the driver. In fact, during natural observation this cue physiologically strengthens depth perception and its absence may impact driver perception. Our goal is to demonstrate the impact of the generated movements and the projection distance on driver’s perception, using an AR-HUD. This was investigated in terms of eye-comfort and driver preferences. In this article, we focus on the primary driving task with basic lane marking highlight, and we observe how the different conditions affect subject’s perception especially during curves negotiation. Results show the importance of eye-tracking when using an AR-HUD for alignment accuracy and better comfort which directly driver performance and safety. Thu, 01 Jan 2015 00:00:00 GMT http://hdl.handle.net/10985/13054 2015-01-01T00:00:00Z HALIT, Lynda KEMENY, Andras LE GOUGUEC, Armand MOHELLEBI, Hakim MERIENNE, Frédéric Augmented Reality information on Head-Up display (AR-HUD) in a car can be relevant for visual aid and for strengthening the safety of the driver. However, some display Parameters are necessary to guarantee the good perception of these information and the driving environment. In this study, we are interested on head motion parallax, and specifically the ones generated with lateral head movements of the driver. In fact, during natural observation this cue physiologically strengthens depth perception and its absence may impact driver perception. Our goal is to demonstrate the impact of the generated movements and the projection distance on driver’s perception, using an AR-HUD. This was investigated in terms of eye-comfort and driver preferences. In this article, we focus on the primary driving task with basic lane marking highlight, and we observe how the different conditions affect subject’s perception especially during curves negotiation. Results show the importance of eye-tracking when using an AR-HUD for alignment accuracy and better comfort which directly driver performance and safety.