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Bai Xue, Martin Frönzle, Hengjun Zhao, Naijun Zhan, Arvind Easwaran.  2019.  Probably Approximate Safety Verification of Hybrid Dynamical Systems. 21st International Conference on Formal Engineering Methods.

In this paper we present a method based on linear programming that facilitates reliable safety verification of hybrid dynamical systems over the infinite time horizon subject to perturbation inputs. The verification algorithm applies the probably approximately correct (PAC) learning framework and consequently can be regarded as statistically formal verification in the sense that it provides formal safety guarantees expressed using error probabilities and confidences. The safety of hybrid systems in this framework is verified via the computation of so-called PAC barrier certificates, which can be computed by solving a linear programming problem. Based on scenario approaches, the linear program is constructed by a family of independent and identically distributed state samples. In this way we can conduct verification of hybrid dynamical systems that existing methods are not capable of dealing with. Some preliminary experiments demonstrate the performance of our approach.

Anirudh Unni, Benedikt Kretzmeyer, Klas Ihme, Frank Köster, Meike Jipp, Jochem W. Rieger.  2018.  Demonstrating brain-level interactions between working memory load and frustration while driving using functional near-infrared spectroscopy. 2nd International Neuroergonomics Conference.

Mental workload is a popular concept in ergonomics as it provides an intuitive explanation why exceedingly cognitive task demands result in a decrease in task performance and increase the risk of fatal incidents while driving. At the same time, affective states such as frustration, also play a role in traffic safety as they increase the tendency for speedy and aggressive driving and may even degrade cognitive processing capacities. To reduce accidents due to dangerous effects of degraded cognitive processing capacities and affective biases causing human errors, it is necessary to continuously assess multiple user states simultaneously to better understand potential interactions. In two previous studies, we measured brain activity with functional near-infrared spectroscopy (fNIRS) for separate brain based prediction of working memory load (WML) (Unni et al., 2017) and frustration levels (Ihme et al. submitted) while driving. Here, we report results from a study designed to predict simultaneously manipulated WML and frustration using data driven machine learning approaches from whole-head fNIRS brain activation measurements. 

Alexander Trende, Anirudh Unni, Lars Weber, Jochem Rieger, Andreas Lüdtke.  2019.  An investigation into human-autonomous vs. human-human vehicle interaction in time-critical situations. 12th Pervasive Technologies Related to Assistive Environments Conference. :303-304.

We performed a driving simulator study to investigate merging decisions with respect to an interaction partner in time-critical situations. The experimental paradigm was a two-alternative forced choice, where the subjects could choose to merge before human vehicles or highly automated vehicles (HAV). Under time pressure, subjects showed a significantly higher gap acceptance during merging situations when interacting with HAV. This confirmed our original hypothesis that when interacting with HAV, drivers would exploit the HAV's technological advantages and defensive programming in time-critical situations.

Julian Schindler, Frank Köster.  2016.  A Model-Based Approach for Performing Successful Multi-Driver Scenarios. Driving Simulation Conference.

When designing driving simulator studies, sometimes high efforts have to be spent to make them successful. Some drivers may not behave as desired, leading to situations unforeseen by the developers. When looking at multi-driver studies, where multiple drivers need to interact with each other in one virtual environment, the probability of performing a successful study is even lower, as the behaviour of the human drivers cannot be fully controlled. While [Oel15b] already proposed guidelines for the creation of such scenarios, this paper describes how the probability of success can be monitored and even enhanced during scenario execution. Therefore, it describes an approach where the probability of success is modelled and where the scenario is dynamically adapted to provide higher rates of success.

Karsten Lemmer, Werner Damm, Janos Stzipanovits, Shankar Sastry, Claire Tomlin, Frank Köster, Meike Jipp.  2019.  Societal and Technological Research Challenges for Highly Automated Road Transportation Systems in Germany and the US: Diversities and Synergy Potentials. Workshop on Societal and Technological Research Challenges for Highly Automated Road Transportation Systems in Germany and the US: Diversities and Synergy Potentials.
Severin Kacianka, Amjad Ibrahim, Alexander Pretschner, Alexander Trende, Andreas Lüdtke.  2019.  Extending Causal Models from Machines into Humans. 4th Causation, Responsibility, & Explanations in Science & Technology Workshop.

Causal Models are increasingly suggested as a mean to reason about the behavior of cyber-physical systems in socio-technical contexts. They allow us to analyze courses of events and reason about possible alternatives. Until now, however, such reasoning is confined to the technical domain and limited to single systems or at most groups of systems. The humans that are an integral part of any such socio-technical system are usually ignored or dealt with by “expert judgment”. We show how a technical causal model can be extended with models of human behavior to cover the complexity and interplay between humans and technical systems. This integrated socio-technical causal model can then be used to reason not only about actions and decisions taken by the machine, but also about those taken by humans interacting with the system. In this paper we demonstrate the feasibility of merging causal models about machines with causal models about humans and illustrate the usefulness of this approach with a highly automated vehicle example.

Amjad Ibrahim, Severin Kacianka, Alexander Pretschner, Charles Hartsell, Gabor Karsai.  2019.  Practical Causal Models for Cyber-Physical Systems. NASA Formal Methods. :211–227.

Unlike faults in classical systems, faults in Cyber-Physical Systems will often be caused by the system's interaction with its physical environment and social context, rendering these faults harder to diagnose. To complicate matters further, knowledge about the behavior and failure modes of a system are often collected in different models. We show how three of those models, namely attack trees, fault trees, and timed failure propagation graphs can be converted into Halpern-Pearl causal models, combined into a single holistic causal model, and analyzed with actual causality reasoning to detect and explain unwanted events. Halpern-Pearl models have several advantages over their source models, particularly that they allow for modeling preemption, consider the non-occurrence of events, and can incorporate additional domain knowledge. Furthermore, such holistic models allow for analysis across model boundaries, enabling detection and explanation of events that are beyond a single model. Our contribution here delineates a semi-automatic process to (1) convert different models into Halpern-Pearl causal models, (2) combine these models into a single holistic model, and (3) reason about system failures. We illustrate our approach with the help of an Unmanned Aerial Vehicle case study.

Hauer, Florian, Raphael Stern, Alexander Pretschner.  2019.  Selecting Flow Optimal System Parameters for Automated Driving Systems . 22nd International Conference on Intelligent Transportation Systems.

Driver assist features such as adaptive cruise control (ACC) and highway assistants are becoming increasingly prevalent on commercially available vehicles. These systems are typically designed for safety and rider comfort. However, these systems are often not designed with the quality of the overall traffic flow in mind. For such a system to be beneficial to the traffic flow, it must be string stable and minimize the inter-vehicle spacing to maximize throughput, while still being safe. We propose a methodology to select autonomous driving system parameters that are both safe and string stable using the existing control framework already implemented on commercially available ACC vehicles. Optimal parameter values are selected via model-based optimization for an example highway assistant controller with path planning.

Sven Hallerbach, Yiqun Xia, Ulrich Eberle, Frank Köster.  2018.  Simulation-based Identification of Critical Scenarios for Cooperative and Automated Vehicles. WCX World Congress Experience.

One of the major challenges for the automotive industry will be the release and validation of cooperative and automated vehicles. The immense driving distance that needs to be covered for a conventional validation process requires the development of new testing procedures. Further, due to limited market penetration in the beginning, the driving behavior of other human traffic participants, regarding a mixed traffic environment, will have a significant impact on the functionality of these vehicles.In this paper, a generic simulation-based toolchain for the model-in-the-loop identification of critical scenarios will be introduced. The proposed methodology allows the identification of critical scenarios with respect to the vehicle development process. The current development status of cooperative and automated vehicle determines the availability of testable simulation models, software, and components.The identification process is realized by a coupled simulation framework. A combination of a vehicle dynamics simulation that includes a digital prototype of the cooperative and automated vehicle, a traffic simulation that provides the surrounding environment, and a cooperation simulation including cooperative features, is used to establish a suitable comprehensive simulation environment. The behavior of other traffic participants is considered in the traffic simulation environment.The criticality of the scenarios is determined by appropriate metrics. Within the context of this paper, both standard safety metrics and newly developed traffic quality metrics are used for evaluation. Furthermore, we will show how the use of these new metrics allows for investigating the impact of cooperative and automated vehicles on traffic. The identified critical scenarios are used as an input for X-in-the-Loop methods, test benches, and proving ground tests to achieve an even more precise comparison to real-world situations. As soon as the vehicle development process is in a mature state, the digital prototype becomes a “digital twin” of the cooperative and automated vehicle.

Werner Damm, Martin Fränzle, Andreas Lüdtke, Jochem W. Rieger, Alexander Trende, Anirudh Unni.  2019.  Integrating Neurophysiological Sensors and Driver Models for Safe and Performant Automated Vehicle Control in Mixed Traffic. IEEE Intelligent Vehicles Symposium.

In the future, mixed traffic Highly Automated Vehicles (HAV) will have to resolve interactions with human operated traffic. A particular problem for HAVs is the detection of human states influencing safety, critical decisions, and driving behavior of humans. We demonstrate the value proposition of neurophysiological sensors and driver models for optimizing performance of HAVs under safety constraints in mixed traffic applications.

X. Koutsoukos, G. Karsai, A. Laszka, H. Neema, B. Potteiger, P. Volgyesi, Y. Vorobeychik, J. Sztipanovits.  2018.  SURE: A Modeling and Simulation Integration Platform for Evaluation of Secure and Resilient Cyber–Physical Systems. Proceedings of the IEEE. 106:93-112.
The exponential growth of information and communication technologies have caused a profound shift in the way humans engineer systems leading to the emergence of closed-loop systems involving strong integration and coordination of physical and cyber components, often referred to as cyber-physical systems (CPSs). Because of these disruptive changes, physical systems can now be attacked through cyberspace and cyberspace can be attacked through physical means. The paper considers security and resilience as system properties emerging from the intersection of system dynamics and the computing architecture. A modeling and simulation integration platform for experimentation and evaluation of resilient CPSs is presented using smart transportation systems as the application domain. Evaluation of resilience is based on attacker-defender games using simulations of sufficient fidelity. The platform integrates 1) realistic models of cyber and physical components and their interactions; 2) cyber attack models that focus on the impact of attacks to CPS behavior and operation; and 3) operational scenarios that can be used for evaluation of cybersecurity risks. Three case studies are presented to demonstrate the advantages of the platform: 1) vulnerability analysis of transportation networks to traffic signal tampering; 2) resilient sensor selection for forecasting traffic flow; and 3) resilient traffic signal control in the presence of denial-of-service attacks.