A Stochastic Model Predictive Control Approach for Driver-Aided Intersection Crossing With Uncertain Driver Time Delay

We investigate the problem of coordinating human-driven vehicles in road intersections without any traffic lights or signs by issuing speed advices. The vehicles in the intersection are assumed to move along an a priori known path and to be connected via vehicle-to-vehicle communication. The challenge arises with the uncertain driver reaction to a speed advice, especially in terms of the driver reaction time delay, as it might lead to unstable system dynamics. For this control problem, a distributed stochastic model predictive control concept is designed which accounts for driver uncertainties. By optimizing over scenarios, which are sequences of independent and identically distributed samples of the uncertainty over the prediction horizon, we can give probabilistic guarantees on constraint satisfaction. Simulation results demonstrate that the scenario-based approach is able to avoid collisions in spite of uncertainty while the non-stochastic baseline controller is not.Comment: Submitted to European Control Conference 2019 (ECC19

Optimal vehicle dynamics control and state estimation for a low cost GNSS-based collision avoidance system

The context of this dissertation is the investigation and experimental evaluation of a global navigation satellite system (GNSS) based automotive collision avoidance system in the scope of the project Galileo above. While the application of collision avoidance comprises a variety of research topics, this thesis focuses on the issues of navigation, vehicle state estimation and vehicle guidance at the handling limits. The main aim of this thesis is to investigate and implement a concept for conducting autonomous evasion maneuvers based on a low-cost GNSS receiver (absolute horizontal position accuracy of about 4 m) and inertial sensors. In previous publications, centimeter-precision high-cost navigation systems (absolute horizontal position accuracy in the centimeter range) have commonly been employed for this purpose. To solve the navigation task, a navigation concept based on relative positioning is introduced that is appropriate for evasion maneuvers having high horizontal accelerations greater than 7 m/s^2. Vehicle state estimation is related to determining key drive dynamic vehicle states (especially the longitudinal and lateral velocity at the center of gravity) which can generally not be measured using series production sensors but are required for the considered control concept. The estimator is designed in such a way that these vehicle states can be determined appropriately even at the handling limits. Furthermore, the concept allows for being adaptive with respect to uncertainties in the tire-road contact. As the evasion path respectively trajectory is considered to be given over a finite time horizon and physical constraints like actuator limitations and the tire-road friction limit are taken into account, a model predictive control scheme is employed to guide the vehicle autonomously. Besides the discussion of the proposed algorithms, experimental results are presented and evaluated

Optimal vehicle dynamics control and state estimation for a low cost GNSS-based collision avoidance system

The context of this dissertation is the investigation and experimental evaluation of a global navigation satellite system (GNSS) based automotive collision avoidance system in the scope of the project Galileo above. While the application of collision avoidance comprises a variety of research topics, this thesis focuses on the issues of navigation, vehicle state estimation and vehicle guidance at the handling limits. The main aim of this thesis is to investigate and implement a concept for conducting autonomous evasion maneuvers based on a low-cost GNSS receiver (absolute horizontal position accuracy of about 4 m) and inertial sensors. In previous publications, centimeter-precision high-cost navigation systems (absolute horizontal position accuracy in the centimeter range) have commonly been employed for this purpose. To solve the navigation task, a navigation concept based on relative positioning is introduced that is appropriate for evasion maneuvers having high horizontal accelerations greater than 7 m/s^2. Vehicle state estimation is related to determining key drive dynamic vehicle states (especially the longitudinal and lateral velocity at the center of gravity) which can generally not be measured using series production sensors but are required for the considered control concept. The estimator is designed in such a way that these vehicle states can be determined appropriately even at the handling limits. Furthermore, the concept allows for being adaptive with respect to uncertainties in the tire-road contact. As the evasion path respectively trajectory is considered to be given over a finite time horizon and physical constraints like actuator limitations and the tire-road friction limit are taken into account, a model predictive control scheme is employed to guide the vehicle autonomously. Besides the discussion of the proposed algorithms, experimental results are presented and evaluated

Towards Learning-Based Control of Connected and Automated Vehicles:Challenges and Perspectives

The exploitation of communication technologies enables connected and automated vehicles (CAVs) to operate more collaboratively, that is, by exchanging or even negotiating future trajectories and control actions. That way, CAVs (or agents) can establish a networked control system such as to safely automate road traffic in a collaborative fashion. A rich body of literature is available, e.g., on intersection automation, automated lane change or lane merging scenarios. These control concepts, though, are most tailored to the particular application and are in general not applicable to multiple scenarios. This chapter conveys the challenges and perspectives of modeling and optimization-based control techniques for the safe coordination of multiple connected agents in road traffic scenarios. Along these lines, the perspective of generalizing controller design to serve multiple use cases simultaneously instead of designing separate controllers for every use case is discussed. Moreover, the opportunities of learning-based control in case of model uncertainties and mixed-traffic scenarios, involving connected and non-connected agents, are outlined