In this paper, we address the problem of combining automatic lane-keeping and driver's steering for either obstacle avoidance or lane-change maneuvers for passing purposes or any other desired maneuvers, through a closed-loop control strategy. The automatic lane-keeping control loop is never opened, and no on/off switching strategy is used. During the driver's maneuver, the vehicle lateral dynamics are controlled by the driver himself through the vehicle steering system. When there is no driver's steering action, the vehicle center of gravity tracks the center of the traveling lane thanks to the automatic lane-keeping system. At the beginning (end) of the maneuver, the lane-keeping task is released (resumed) safely and smoothly. The performance of the proposed closed-loop structure is shown both by means of simulations and through experimental results obtained along Italian highways. Index Terms-automatic lane-keeping, driver's steering, lanechange, two-degrees-of-freedom (2-DOF) control, vehicle lateral control.

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Set-membership identification of a Linear Parameter Varying (LPV) model describing the vehicle lateral dynamics is addressed in the paper. The model structure, chosen as much as possible on the ground of physical insights into the vehicle lateral behavior, consists of two single-input single-output LPV models relating the steering angle to the yaw rate and to the sideslip angle. A set of experimental data obtained by performing a large number of manoeuvres is used to identify the vehicle lateral dynamics model. Prior information on the error bounds on the output and the time-varying parameters measurements are taken into account. Comparison with other vehicle lateral dynamics models is discussed.

Abstract-In this note, we present a two-stage procedure for deriving parameters bounds in Hammerstein models when the output measurement errors are bounded. First, using steady-state input-output data, parameters of the nonlinear part are tightly bounded. Then, for a given input transient sequence we evaluate tight bounds on the unmeasurable inner signal which, together with noisy output measurements are used for bounding the parameters of the linear dynamic block.

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The sparse linear regression problem is difficult to handle with usual sparse optimization models when both predictors and measurements are either quantized or represented in low-precision, due to nonconvexity. In this paper, we provide a novel linear programming approach, which is effective to tackle this problem. In particular, we prove theoretical guarantees of robustness, and we present numerical results that show improved performance with respect to the state-of-the-art methods. * Corresponding author. The authors are with the