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Technology from Highly Automated Driving to Improve Active Pedestrian Protection Systems
Technical Paper
2017-01-1409
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Highly Automated Driving (HAD) opens up new middle-term perspectives in mobility and is currently one of the main goals in the development of future vehicles. The focus is the implementation of automated driving functions for structured environments, such as on the motorway. To achieve this goal, vehicles are equipped with additional technology. This technology should not only be used for a limited number of use cases. It should also be used to improve Active Safety Systems during normal non-automated driving.
In the first approach we investigate the usage of machine learning for an autonomous emergency braking system (AEB) for the active pedestrian protection safety. The idea is to use knowledge of accidents directly for the function design. Future vehicles could be able to record detailed information about an accident. If enough data from critical situations recorded by vehicles is available, it is conceivable to use it to learn the function design. Furthermore the system behaviour can be evaluated for the recorded accidents.
Active Safety Systems need to know if the driver is able to solve a critical situation or if an intervention of the vehicle is necessary. Driver behaviour is difficult to model. It is not feasible to compute every possible future state of the driver. Based on the work from Eidehall, a stochastic approach is implemented to calculate how long a driver is able to drive collision free, based on information about other traffic participants and a high-precision digital map. This approach is described in the second part of the paper.
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Schratter, M., Cantu, S., Schaller, T., Wimmer, P. et al., "Technology from Highly Automated Driving to Improve Active Pedestrian Protection Systems," SAE Technical Paper 2017-01-1409, 2017, https://doi.org/10.4271/2017-01-1409.Data Sets - Support Documents
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References
- Aeberhard N. , Rauch S. , Bahram M. , Tanzmeister G. , Thomas J. , Pilat Y. , Homm F. , Huber W. , Kaempchen N. Experience, Results and Lessons Learned from Automated Driving on Germany's Highway IEEE Intelligent transportation systems magazine 2015
- Huelsen M. Knowledge-Based Driver Assistance Systems Wiesbaden Springer Fachmedien 2014
- Liebner M. and Klanner F. Driver Intent Inference and Risk Assessment Handbook of Driver Assistance Systems Springer 2015
- Goldhammer M. , Köhler S. , Doll K. and Sick B. Camera Based Pedestrian Path Prediction by Means of Polynomial Least-squares Approximation and Multilayer Perceptron Neural Networks SAI Intelligent Systems Conference London 2015
- Liebner M. , Ruhhammer C. , Klanner F. and Stiller C. Generic driver intent inference based on parametric models Netherlands IEEE Conference on Intelligent Transportation Systems, Proceedings 2013
- Kompass K. , Schratter M. and Schaller T. Active Safety towards Highly Automated Driving Automated Driving -Safer and More Efficient Future Driving Switzerland Springer International Publishing 2016 371 386
- Schaller T. Active Safety on the Road Towards Highly Automated Driving 8th Graz Symposium Virtuelles Fahrzeug Graz 2015
- Gasser T. M. and Westhoff D. BASt-study: Definitions of Automation and Legal Issues in Germany Transportation Research Board 2012
- DESTATIS Road Accidents 2013 Statistisches Bundesamt Wiesbaden 2014
- Schubert A. , Erbsmehl C. and Hannawald L. Standardized precrash-scenarios in digital format on the basis of the VUFO simulation, Verkehrsunfallforschung an der TU Dresden GmbH Expert Symposium on Accident Research Hannover 2012
- Hummel T. , Kühn M. , Bende J. and Lang A. An investigation of their potential safety benefits based on an analysis of insurance claims in Germany Berlin UDV (German Insurers Accident Research) 2011
- Heinrich T. , Ortlepp J. , Schmiele J. and Voß H. Infrastrukturgestützte Fahrerassistenz UDV (German Insurers Accident Research) Berlin 2011
- Kates R. , Jung O. , Helmer T. , Ebner A. , Gruber C. and Kompass K. Stochastic simulation of critical traffic situations for the evaluation of preventive pedestrian protection systems VDI Verlag GmbH 2010
- Domsch C. Leistungssteigerung präventiver Schutzsysteme München 5. Tagung Fahrerassistenz, Fahrzeugsicherheit 2012
- Louppe G. , Wehenkel L. , Sutera A. and Geurts P. Understanding variable importances in forests of randomized trees Advances in Neural Information Processing Systems 26 2013
- Eidehall A. and Petersson L. Statistical Threat Assessment for General Road Scenes Using Monte Carlo Sampling IEEE Transactions on Intelligent Transportation Systems 2009
- Käfer E. Situationsklassifikation und Bewegungsprognose in Verkehrssituationen mit mehreren Fahrzeugen University Bielefeld Kornwestheim 2013
- Quigley M. , Conley K. , Gerkey B. P. , Faust J. , Foote T. , Leibs J. , Wheeler R. and Ng A. Y. ROS: an open-source Robot Operating System ICRA Workshop on Open Source Software www.ros.org 2009