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This paper deals with the longitudinal and lateral control of an automotive vehicle within the framework of fully automated guidance. The automotive vehicle is a complex system characterized by highly nonlinear longitudinal and lateral coupled dynamics. Consequently, automated guidance must be simultaneously performed with longitudinal and lateral control. This work presents an automated steering strategy based on Nonlinear Model Predictive Control. A nonlinear longitudinal control strategy considering powertrain dynamics is also proposed to cope with the longitudinal speed tracking problem. Finally a simultaneous longitudinal and lateral control strategy helps to improve the combined control performance. This whole control strategy is tested through simulations showing the effectiveness of the present approach.

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Global sensitivity has mainly been analyzed in static models, though most physical systems can be described by differential equations. Very few approaches have been proposed for the sensitivity of dynamic models and the only ones are local. Nevertheless, it would be of great interest to consider the entire uncertainty range of parameters since they can vary within large intervals depending on their meaning. Other advantage of global analysis is that the sensitivity indices of a given parameter are evaluated while all the other parameters can be varied. In this way, the relative variability of each parameter is taken into account, revealing any possible interactions. This paper presents the global sensitivity analysis for dynamic models with an original approach based on the polynomial chaos (PC) expansion of the output. The evaluation of the PC expansion of the output is less expensive compared to direct simulations. Moreover, at each time instant, the coefficients of the PC decomposition convey the parameter sensitivity and then a sensitivity function can be obtained. The PC coefficients are determined using non-intrusive methods. The proposed approach is illustrated with some well-known dynamic systems.

International audienceThis paper presents a new approach to constrained trajectory generation dedicated to Advanced Driver Assistance Systems (ADAS). Based on the information provided by the digital map database of a navigation system, the proposed solution is devoted to a control-oriented trajectory generation approach taking account of constraints which limit the behaviour of a common car, relative to the road to be followed and finally linked to the driver. Characteristically, these trajectories stay within the traffic lane borders; at the same time, they minimise the energy along the path, and finally, they are curvature continuous. The present trajectory generation has been tested on a specific test track and the results show the efficiency of the proposed solution