Static output feedback control for lane change maneuver

International audienceThis paper addresses the lateral control of a vehicle during lane change maneuvers. The proposed design procedure aims to answer the questions of control using cost-effective sensors implementation, adaptation to measured variables and robustness to unmeasured varying parameters. This is achieved through a static output feedback controller with preview information. The only used measurements are the lateral displacement at sensor location and the yaw angle relative to the lane centerline. The vehicle lateral model is augmented with an integral action, the error signal and the preview reference signal. The controller is synthesized using the LMI framework thanks to a relaxation method that removes the nonlinear terms. Simulations are conducted for various scenarios showing the ability of the design method to handle different performance objectives

Adaptive Cooperative Highway Platooning and Merging

As low-cost reliable sensors are introduced to market, research efforts in autonomous driving are increasing. Traffic congestion is a major problem for nearly all metropolis'. Assistive driving technologies like cruise control and adaptive cruise control are widely available today. While these control systems ease the task of driving, the driver still needs to be fully alert at all times. While these existing structures are helpful in alleviating the stress of driving to a certain extent, they are not enough to improve traffic flow. Two main causes of congestion are slow response of drivers to their surroundings, and situations like highway ramp merges or lane closures. This thesis will address both of these issues. A modified version of the widely available adaptive cruise control systems, known as cooperative adaptive cruise control, can work at all speeds with additional wireless communication that improves stability of the controller. These structures can tolerate much smaller desired spacing and can safely work in stop and go traffic. This thesis proposes a new control structure that combines conventional cooperative adaptive cruise control with rear end collision check. This approach is capable of avoiding rear end collisions with the following car, as long as it can still maintain the safe distance with the preceding vehicle. This control structure is mainly intended for use with partially automated highways, where there is a risk of being rear-ended while following a car with adaptive cruise control. Simulation results also shows that use of bidirectional cooperative adaptive cruise control also helps to strengthen the string stability of the platoon. Two different control structures are used to accomplish this task: MPC and PD based switching controller. Model predictive control (MPC) structure works well for the purpose of bidirectional platoon control. This control structure can adapt to the changes in the plant with the use of a parameter estimator. Constraints are set to make sure that the controller outputs are always within the boundaries of the plant. Also these constraints assures that a certain gap will always be kept with the preceding vehicle. PD based switching controller offers an alternative to the MPC structure. Main advantage of this control structure is that it is designed to be robust to certain level of sensor noise. Both these control structures gave good simulation results. The thesis makes use of the control structures developed in the earlier chapters to continue developing structures to alleviate traffic congestions. Two merging schemes are proposed to find a solution to un-signaled merging and lane closures. First problem deals with situations where necessary levels of communication is not present to inform surrounding drivers of merging intention. Second structure proposes a merging protocol for cases where two platoons are approaching a lane closure. This structure makes use of the modified cooperative adaptive cruise control structures proposed earlier in the thesis

Predictive Maneuver Planning and Control of an Autonomous Vehicle in Multi-Vehicle Traffic with Observation Uncertainty

Autonomous vehicle technology is a promising development for improving the safety, efficiency and environmental impact of on-road transportation systems. However, the task of guiding an autonomous vehicle by rapidly and systematically accommodating the plethora of changing constraints, e.g. of avoiding multiple stationary and moving obstacles, obeying traffic rules, signals and so on as well as the uncertain state observation due to sensor imperfections, remains a major challenge. This dissertation attempts to address this challenge via designing a robust and efficient predictive motion planning framework that can generate the appropriate vehicle maneuvers (selecting and tracking specific lanes, and related speed references) as well as the constituent motion trajectories while considering the differential vehicle kinematics of the controlled vehicle and other constraints of operating in public traffic. The main framework combines a finite state machine (FSM)-based maneuver decision module with a model predictive control (MPC)-based trajectory planner. Based on the prediction of the traffic environment, reference speeds are assigned to each lane in accordance with the detection of objects during measurement update. The lane selection decisions themselves are then incorporated within the MPC optimization. The on-line maneuver/motion planning effort for autonomous vehicles in public traffic is a non-convex problem due to the multiple collision avoidance constraints with overlapping areas, lane boundaries, and nonlinear vehicle-road dynamics constraints. This dissertation proposes and derives some remedies for these challenges within the planning framework to improve the feasibility and optimality of the solution. Specifically, it introduces vehicle grouping notions and derives conservative and smooth algebraic models to describe the overlapped space of several individual infeasible spaces and help prevent the optimization from falling into undesired local minima. Furthermore, in certain situations, a forced objective selection strategy is needed and adopted to help the optimization jump out of local minima. Furthermore, the dissertation considers stochastic uncertainties prevalent in dynamic and complex traffic and incorporate them with in the predictive planning and control framework. To this end, Bayesian filters are implemented to estimate the uncertainties in object motions and then propagate them into the prediction horizon. Then, a pair-wise probabilistic collision condition is defined for objects with non-negligible geometrical shape/sizes and computationally efficient and conservative forms are derived to efficiently and analytically approximate the involved multi-variate integrals. The probabilistic collision evaluation is then applied within a vehicle grouping algorithms to cluster the object vehicles with closeness in positions and speeds and eventually within the stochastic predictive maneuver planner framework to tighten the chanced-constraints given a deterministic confidence margin. It is argued that these steps make the planning problem tractable for real-time implementation on autonomously controlled vehicles