Scenario-based testing in simulation has become a cornerstone of industrial practice for systematically assessing autonomous driving systems across diverse and relevant situations. Generating critical scenarios is central to this methodology, yet it remains challenging due to the inherent uncertainties resulting from scenario parameterization. While parameterization is essential for modeling unpredictable factors, like weather, an excess of parameters hampers testing effectiveness. To address these challenges, this paper introduces a methodology that guides testers in selecting scenario parameters and managing the associated uncertainties. Our approach integrates specification-driven and optimization-based test generation with sensitivity analysis, enabling testers to assess the impact of scenario parameters on scenario criticality. We implemented our approach using well-established industry technologies and evaluated it in a highway case study on three reference search-based scenario generation methods with varying degrees of exploitativeness. Results from our evaluation suggest that reducing the parameter-induced uncertainty can improve the ability of some testing methods to identify critical scenarios while maintaining the diversity of input parameter values.

We introduce a distributed control method for coordinating multiple vehicles in the framework of an automated valet parking (AVP) system. The control functionality is distributed between an infrastructure server, called parking area management (PAM) system, and local autonomous vehicle control units. Via a vehicle-to-infrastructure (V2I) communication interface, model predictive control (MPC) decisions of the vehicles are shared with the coordination unit in the PAM. This unit in turn computes a coupling feedback which is shared with the vehicles. The control system is integrated in an automated test-system to cope with the high test requirements and short development cycles of highly automated systems. Evaluations conducted with the test-system show the functionality of the proposed distributed control method for multi-vehicle coordination. Results indicate safe coordination, and an efficiency increase compared to an uncoordinated method in an AVP simulation environment.

It is well-known that physical robotic assistance to humans is significantly enhanced by including human behavior anticipation into robot planning and control. The challenge arises when the human goal/plan is uncertain or unknown to the robot. In this paper we propose a novel control scheme which dynamically selects between a model-based and a model-free strategy depending on the level of disagreement between the human and the robot. The disagreement is measured in terms of the interaction force. A task specific model-based controller is selected when the human's motion intention coincides with the robot's goal. A model-free control scheme based on the human force as motion prediction source is selected in case of disagreement and when the human goal/plan is unknown. The benefits of this approach are demonstrated in a human user study on human-robot cooperative object transport through a 2D maze in virtual reality.

Points on the boundary of the MIMO broadcast channel (BC) capacity region are achieved by a combination of dirty paper coding (DPC) and linear precoding. The linear precoding determines the covariance matrices of the transmitted signals. Determining the optimum covariance matrices may lead to an undesirably high computational complexity for systems of high dimension. In this paper, an approach to approximate the MIMO BC capacity region is proposed. The proposed method combines DPC with sub-optimum linear precoding matrices that can be computed with low complexity but provide close to optimum performance. Motivated by multiobjective optimization, an efficient algorithm developed for sum-rate maximization is generalized to computing an achievable rate region. Simulation results show that this rate region, which has low complexity in terms of computing the precoding matrices, well approximates the capacity region of the MIMO BC.