The success in the adoption of autonomous vehicles is dependent on their ability to solve rarely occurring safety-critical corner cases. Vehicular communications (V2X) aim at improving safety and efficiency of autonomous driving by adding the capability of explicit inter-vehicular information exchange. We argue that V2X enables another important function, namely the support of ethical driving decisions. In this article, we explain our envisioned methodology of an ethical cooperative driving interaction protocol design (including what to communicate and how to act based on the received information). Cooperative manoeuvring empowered by the ethical V2X is a new research direction we promote in the autonomous driving research agenda. © 2024 IEEE.

Formal models for the safety validation of autonomous vehicles have become increasingly important. To this end, we present a safety framework for longitudinal automated driving. This framework enables the calculation of minimum safe inter-vehicular distances for arbitrary ego vehicle control policies in a computationally efficient manner. We use this framework to enhance and generalize the Responsibility-Sensitive Safety (RSS) [1] model and models based on it, which fail to cover situations where the ego vehicle has a higher decelerating capacity than its preceding vehicle. © 2024 IEEE.

Connected and automated vehicles (CAVs) will be a key component of future cooperative intelligent transportation systems (C-ITS). Since the adoption of C-ITS is not foreseen to happen instantly, not all of its elements are going to be connected at the early deployment stages. We consider a scenario where vehicles approaching a traffic light are connected to each other, but the traffic light itself is not cooperative. Information about indented trajectories such as decisions on how and when to accelerate, decelerate and stop, is communicated among the vehicles involved. We provide an optimization-based procedure for efficient and safe passing of traffic lights (or other temporary road blockage) using vehicle-to-vehicle communication (V2V). We locally optimize objectives that promote efficiency such as less deceleration and larger minimum velocity, while maintaining safety in terms of no collisions. The procedure is computationally efficient as it mainly involves a gradient decent algorithm for one single parameter. © 2023 by the authors.

We argue that an information exchange between vehicles via the vehicular communications is the foundation for ethical driving. In other words - autonomous vehicles must be cooperative to be able to resolve ethical dilemmas in a multi-vehicle scenario. We show this by exploring the minimal setting of a longitudinal driving in a formation of three vehicles. © 2023 IEEE.

We propose a novel non-negative spherical relaxation for optimization problems over binary matrices with injectivity constraints, which in particular has applications in multi-matching and clustering. We relax respective binary matrix constraints to the (high-dimensional) non-negative sphere. To optimize our relaxed problem, we use a conditional power iteration method to iteratively improve the objective function, while at same time sweeping over a continuous scalar parameter that is (indirectly) related to the universe size (or number of clusters). Opposed to existing procedures that require to fix the integer universe size before optimization, our method automatically adjusts the analogous continuous parameter. Furthermore, while our approach shares similarities with spectral multi-matching and spectral clustering, our formulation has the strong advantage that we do not rely on additional post-processing procedures to obtain binary results. Our method shows compelling results in various multi-matching and clustering settings, even when compared to methods that use the ground truth universe size (or number of clusters). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

This paper provides a safety analysis for emergency braking scenarios involving consecutive vehicles which utilize adaptive cruise control (ACC) with a constant-distance policy together with vehicle-to-vehicle (V2V) communication. We identify analytically, how the minimum safe inter-vehicle distance(IVD) that allows avoiding rear-end collision can be shortened with the use of electronic emergency brake lights and derive the explicit dependency of such IVDs on V2V communication time delay. We further show how these results can be used to compute probabilities of safe braking in the presence of packet losses.

A platoon comprises a string of consecutive highly automated vehicles traveling together. Platooning allows for increased road utilization and reduced fuel consumption due to short inter-vehicular distances. Safety in terms of guaranteeing no rear-end collisions is of utmost importance for platooning systems to be deployed in practice. We compare how safely emergency braking can be handled by emerging V2V communications on the one hand and by radar-based measurements of existing AEBS on the other. We show that even under conservative assumptions on the V2V communications, such an approach significantly outperforms AEBS with an ideal radar sensor in terms of allowed inter-vehicle distances and response times. Furthermore, we design two emergency braking strategies for platooning based on V2V communications. The first braking strategy assumes centralized coordination by the leading vehicle and exploits necessary optimal conditions of a constrained optimization problem, whereas the second -- the more conservative solution -- assumes only local information and is distributed in nature. Both strategies are also compared with the AEBS.

Formal models for the safety validation of autonomous vehicles have become increasingly important. To this end, we present a safety framework for longitudinal automated driving. This framework allows calculating minimum safe inter-vehicular distances for arbitrary ego vehicle control policies. We use this framework to enhance the Responsibility-Sensitive Safety (RSS) model and models based on it, which fail to cover situations where the ego vehicle has a higher decelerating capacity than its preceding vehicle. For arbitrary ego vehicle control policies, we show how our framework can be applied by substituting real (possibly computationally intractable) controllers with upper bounding functions. This comprises a general approach for longitudinal safety, where safety guarantees for the upper-bounded system are equivalent to those for the original system but come at the expense of larger inter-vehicular distances. 

In this concise yet comprehensive Open Access textbook, future inventors are introduced to the key concepts of Cyber-Physical Systems (CPS). Using modeling as a way to develop deeper understanding of the computational and physical components of these systems, one can express new designs in a way that facilitates their simulation, visualization, and analysis. Concepts are introduced in a cross-disciplinary way. Leveraging hybrid (continuous/discrete) systems as a unifying framework and Acumen as a modeling environment, the book bridges the conceptual gap in modeling skills needed for physical systems on the one hand and computational systems on the other. In doing so, the book gives the reader the modeling and design skills they need to build smart, IT-enabled products. Starting with a look at various examples and characteristics of Cyber-Physical Systems, the book progresses to explain how the area brings together several previously distinct ones such as Embedded Systems, Control Theory, and Mechatronics. Featuring a simulation-based project that focuses on a robotics problem (how to design a robot that can play ping-pong) as a useful example of a CPS domain, Cyber-Physical Systems: A Model-Based Approach demonstrates the intimate coupling between cyber and physical components, and how designing robots reveals several non-trivial control problems, significant embedded and real-time computation requirements, and a need to consider issues of communication and preconceptions. © The Editor(s) (if applicable) and The Author(s) 2021

Performing multiple experiments is common when learning internal mechanisms of complex systems. These experiments can include perturbations of parameters or external disturbances. A challenging problem is to efficiently incorporate all collected data simultaneously to infer the underlying dynamic network. This paper addresses the reconstruction of dynamic networks from heterogeneous datasets under the assumption that the underlying networks share the same Boolean structure across all experiments. Parametric models are derived for dynamical structure functions, which describe causal interactions between measured variables. Multiple datasets are integrated into one regression problem with additional demands on group sparsity to assure network sparsity and structure consistency. To acquire structured group sparsity, we propose a sampling-based method, together with extended versions of l1-methods and sparse Bayesian learning. The performance of the proposed methods is benchmarked in numerical simulation. In summary, this paper presents efficient methods on network reconstruction from multiple experiments, and reveals practical experience that could guide applications. © 2020 Elsevier Ltd.