Automated vehicles require efficient and safe planning to maneuver in uncertain environments. Largely this uncertainty is caused by other traffic participants, e.g., surrounding vehicles. Future motion of surrounding vehicles is often difficult to predict. Whereas robust control approaches achieve safe, yet conservative motion planning for automated vehicles, Stochastic Model Predictive Control (SMPC) provides efficient planning in the presence of uncertainty. Probabilistic constraints are applied to ensure that the maximal risk remains below a predefined level. However, safety cannot be ensured as probabilistic constraints may be violated, which is not acceptable for automated vehicles. Here, we propose an efficient trajectory planning framework with safety guarantees for automated vehicles. SMPC is applied to obtain efficient vehicle trajectories for a finite horizon. Based on the first optimized SMPC input, a guaranteed safe backup trajectory is planned using reachable sets. This backup is used to overwrite the SMPC input if necessary for safety. Recursive feasibility of the safe SMPC algorithm is proved. Highway simulations show the effectiveness of the proposed method regarding performance and safety.

Autonomous vehicles face the challenge of providing safe transportation while efficiently maneuvering in an uncertain environment. Considering surrounding vehicles, two types of uncertainties occur: multiple future maneuvers are possible, and within these maneuvers the vehicle can vary from the predicted ideal maneuver path. Focusing on only one of these uncertainties can either lead to neglecting potential risks or result in overly conservative motion planning. Here, we suggest a Stochastic Model Predictive Control strategy that tackles the possibility of multiple future maneuvers of surrounding vehicles, while also considering uncertainty within the execution of these predicted maneuvers. The proposed control method is a combination of Scenario Model Predictive Control to cope with multiple predicted maneuvers of other vehicles, and Stochastic Model Predictive Control using chance-constraints to take into account vehicle deviations from the predicted maneuver trajectories of the respective maneuver. Adjustable risk parameters permit a violation of safety constraints up to a desired probability, allowing a trade-off between risk and performance. A simulation of a two-lane scenario demonstrates the effectiveness of our method. *The authors gratefully acknowledge the financial support by the BMW Group.

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A current goal for microactuators is to extend their usually small working ranges, which typically result from mechanical connections and restoring forces imposed by cantilevers. In order to overcome this, we present a bistable levitation setup to realise free vertical motion of a magnetic proof mass. By superimposing permanent magnetic fields, we imprint two equilibrium positions, namely on the ground plate and levitating at a predefined height. Energy-efficient switching between both resting positions is achieved by the cooperation of a piezoelectric stack actuator, initially accelerating the proof mass, and subsequent electromagnetic control. A trade-off between robust equilibrium positions and energy-efficient transitions is found by simultaneously optimising the controller and design parameters in a co-design. A flatness-based controller is then proposed for tracking the obtained trajectories. Simulation results demonstrate the effectiveness of the combined optimisation.

In order to satisfy the demand for the high functionality of future microdevices, research on new concepts for multistable microactuators with enlarged working ranges becomes increasingly important. A challenge for the design of such actuators lies in overcoming the mechanical connections of the moved object, which limit its deflection angle or traveling distance. Although numerous approaches have already been proposed to solve this issue, only a few have considered multiple asymptotically stable resting positions. In order to fill this gap, we present a microactuator that allows large vertical displacements of a freely moving permanent magnet on a millimeter-scale. Multiple stable equilibria are generated at predefined positions by superimposing permanent magnetic fields, thus removing the need for constant energy input. In order to achieve fast object movements with low solenoid currents, we apply a combination of piezoelectric and electromagnetic actuation, which work as cooperative manipulators. Optimal trajectory planning and flatness-based control ensure time- and energy-efficient motion while being able to compensate for disturbances. We demonstrate the advantage of the proposed actuator in terms of its expandability and show the effectiveness of the controller with regard to the initial state uncertainty.