Automotive Electronic Systems
Final Report Abstract
With respect to the overall CCC scope of enabling safe change in mixed-criticality systems, project C2 aimed at showing the applicability of the developed concepts in an automotive setting. Two different aspects of change were addressed in this context: On the one hand, change caused by application updates (e.g. for the integration of new features) should be enabled. On the other hand, change can also be required to react to operating conditions, e.g., to fall back to a safe state in case of errors. Several demo applications were developed for the research vehicle MOBILE. Simple updatable driver assistance functions aimed at showing the general applicability of the developed methods. An automated driving function for automated obstacle evasion was implemented to show aspects of scalability. From a scientific point of view, the originally targeted approach in the research unit of creating a viewpoint-oriented system model for the application scenarios was key to several contributions to the state of the art regarding one of the current hot topics in the field of automated driving: ensuring safety of automated vehicle systems in the absence of a safety driver. As safety is an emergent property of cyber-physical systems, the contributions in this context were only possible because of the interdisciplinary collaboration of different projects. From these collaborations, it became clear that the originally planned system viewpoints needed extension in order to capture relevant aspects for safe change in automated driving applications. The formulation of system behavior at the vehicle level, supported by an explicit representation of the system’s capabilities as an additional (functional) system view, was a core contribution. This allows to trace functional safety requirements to measurable metrics in different architectural views, such as control quality metrics or timing requirements. This does not only enable the integration of application updates into an existing system. In consequence, this also enables an assessment of the impact of change at different architectural levels on the actual behavior of the automated vehicle. These findings are also a contribution to the concept of self-awareness, a key property of autonomous systems, as it provides a basis for the system to reason about its current internal state and/or the quality of executed system tasks. In addition, the information about the vehicles’ capabilities can contribute to safe operation at the application level itself. To make use of this information, algorithms in the applications must provide suitable interfaces. In this respect, C2 contributed to model-based fault tolerant trajectory planning and control by establishing a framework which uses functional actuator redundancy (e.g. by using torque vectoring to steer the vehicle) for robustness against single actuator degradation. The contributions regarding self-awareness for automated vehicles currently provide the basis for a proposal for a collaborative research center (Self-Aware Mobility). In addition, the results regarding the architecture framework and the above-mentioned safety aspects have provided major input for two publicly funded research projects (UNICARagil, VVMethoden) and a bilateral project with an industrial partner.
Publications
- Specifying a Middleware for Distributed Embedded Vehicle Control Systems. In Proc.of the 2014 IEEE International Conference on Vehicular Electronics and Safety (ICVES). Hyderabad, India, Dec. 2014, pp. 117–122
A. Reschka, M. Nolte, T. Stolte, J. Schlatow, R. Ernst, M. Maurer
(See online at https://doi.org/10.1109/ICVES.2014.7063734) - Ability and skill graphs for system modeling, online monitoring, and decision support for vehicle guidance systems. In Proc. of the 2015 IEEE Intelligent Vehicle Symposium (IV). Seoul, Korea, Jun. 2015, pp. 933–939
A. Reschka, S. Ulbrich, G. Bagschik, M. Nolte, M. Maurer
(See online at https://doi.org/10.1109/IVS.2015.7225804) - A framework for policy based secure intra vehicle communication. In Proc. of the 2017 IEEE Vehicular Networking Conference (VNC). Torino, Italy, Nov. 2017, pp. 1–8
M. Hamad, M. Nolte, V. Prevelakis
(See online at https://doi.org/10.1109/VNC.2017.8275646) - Model Predictive Control Based Trajectory Generation for Autonomous Vehicles – An Architectural Approach. In Proc. of the 2017 IEEE Intelligent Vehicles Symposium (IV). Los Angeles, CA, USA, Jul. 2017, pp. 798–805
M. Nolte, M. Rose, T. Stolte, M. Maurer
(See online at https://doi.org/10.1109/IVS.2017.7995814) - Self-awareness in autonomous automotive systems. In Proc. of the Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017. Lausanne, Switzerland, Mar. 2017, pp. 1050–1055
J. Schlatow, M. Möstl, R. Ernst, M. Nolte, I. Jatzkowski, M. Maurer, C. Herber, A. Herkersdorf
(See online at https://doi.org/10.23919/DATE.2017.7927145) - Towards a Skill- and Ability-Based Development Process for Self-Aware Automated Road Vehicles. In Proc. of the 2017 IEEE Intern. Conf. on Intelligent Transportation Systems (ITSC). Yokohama, Japan, Nov. 2017, pp. 739–744
M. Nolte, G. Bagschik, I. Jatzkowski, T. Stolte, A. Reschka, M. Maurer
(See online at https://doi.org/10.1109/ITSC.2017.8317814) - Towards model-based integration of component-based automotive software systems. In Proc. of the IECON 2017 – 43rd Annual Conference of the IEEE Industrial Electronics Society. Beijing, China, Oct. 2017, pp. 8425–8432
J. Schlatow, M. Möstl, R. Ernst, M. Nolte, I. Jatzkowski, M. Maurer
(See online at https://doi.org/10.1109/IECON.2017.8217479) - A Deep-Learning Approach for the Detection of Overexposure in Automotive Camera Images. In Proc. of the 2018 IEEE Intern. Conf. on Intelligent Transportation Systems (ITSC). Maui, HI, USA, Nov. 2018, pp. 2030–2035
I. Jatzkowski, D. Wilke, M. Maurer
(See online at https://doi.org/10.1109/ITSC.2018.8569692) - A System’s Perspective Towards an Architecture Framework for Safe Automated Vehicles. In Proc. of the 2018 IEEE International Conference on Intelligent Transportation Systems (ITSC). Maui, HI, USA, Nov. 2018, pp. 2438–2445
G. Bagschik, M. Nolte, S. Ernst, M. Maurer
(See online at https://doi.org/10.1109/ITSC.2018.8569398) - Investigating Functional Redundancies in the Context of Vehicle Automation – A Trajectory Tracking Perspective. In Proc. of the 2018 IEEE Intern. Conf. on Intelligent Transportation Systems (ITSC). Maui, HI, USA, Nov. 2018, pp. 2398–2405
T. Stolte, T. Liao, M. Nee, M. Nolte, M. Maurer
(See online at https://doi.org/10.1109/ITSC.2018.8569243) - Controlling Concurrent Change – A Multiview Approach Toward Updatable Vehicle Automation Systems. In: Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH, Wadern/Saarbrücken, Germany, March 2019, pp. 1–15
M. Möstl, M. Nolte, J. Schlatow, R. Ernst
(See online at https://doi.org/10.4230/OASIcs.ASD.2019.4) - Reference Trajectories for Investigating Fault-Tolerant Trajectory Tracking Control Algorithms for Automated Vehicles. In Proc. of the 9th IFAC International Symposium on Advances in Automotive Control (AAC). Orléans, France, Jun. 2019, pp. 40–47
T. Stolte, L. Qiu, M. Maurer
(See online at https://doi.org/10.1016/j.ifacol.2019.09.007)
