The Dresden Method": A toolbox for the holistic evaluation of active safety and automated driving functions

被引：0

作者：

Mai M. ^{[1
]}

Bäumler M. ^{[1
]}

Lehmann M. ^{[1
]}

Siebke C. ^{[1
]}

Blenz K. ^{[1
]}

Prokop G. ^{[1
]}

Bönninger J. ^{[2
]}

Höpping K. ^{[2
]}

机构：

[1] FSD Fahrzeugsystemdaten GmbH, Dresden

来源：

VDI Berichte | 2022年 / 2022卷 / 2387期

关键词：

Clustering algorithms;

D O I：

10.51202/9783181023877-419

中图分类号：

学科分类号：

摘要：

In the past ten years, the Chair of Automobile Engineering (LKT) of TU Dresden and the FSD – Central Agency for PTI have focussed their common research and development efforts on a universal methodology toolbox for the holistic effectiveness and risk evaluation of active safety functions and automated driving functions: “The Dresden Method”. Real world data from several sources (naturalistic driving data, camera based traffic observations, accident data, etc.) are being merged into a common data basis by fusion algorithms, which also allow the supplement of partly missing information by means of artificial intelligence methods. Critical traffic situations are being labelled in the normal traffic data to better identify the relevant traffic behaviour. Self-developed criticality measures are being used for this labelling, which are based on the controllability of a situation for the human behaviour of road users. For a function under test, a representative test scenario catalogue, which covers the field of application of the function in a testable manner, is being extracted from that common data basis by means of self-developed clustering algorithms. This scenario catalogue is used by several evaluation m thods subsequently. The safety evaluation on a macroscopic level is done by means of stochastic traffic simulations using physio-psychological models of road user behaviour, which provide a realistic virtual traffic and accident behaviour for a statistical analysis of the impact on road safety (with a reduced level of detail). The safety evaluation on a microscopic level is done by means of driving simulator studies and real world test drives (with a higher level of detail). This methodology toolbox provides a tool set for function development and validation, assessment, authorisation, periodical technical inspection, and field monitoring. It is the foundation of development for the initiative “Safety of connected and automated road traffic”, a common undertaking of TU Dresden, FSD, and further partners for the generation and supply of evaluation scenarios, methods, and tools for future road safety. © 2022, VDI Verlag GMBH. All rights reserved.

引用

页码：419 / 434

页数：15

共 32 条

[11] Watanabe H.G., Cluster-Linkage Analysis in Traffic Data Clustering for Development of Advanced Driver Assistance Systems, IEEE International Conference on Information and Computer Technologies, San José CA, (2020)
[12] Watanabe H., Maly T., Wallner J., Dirndorfer T., Mai M.G., Methodology of Scenario Clustering for Predictive Safety Functions, Tagung Automatisiertes Fahren, München, (2019)
[13] Prokop G., Tuschen T., Eisenkock N.J., Hochimmersiver Fahrsimulator Zum Szenarienbasierten Testen Automatisierter Fahrfunktionen, Vdi-Tagung Fahrzeugsicherheit, Berlin, (2021)
[14] Blenz K., Mai M.G., Integration Eines Modularen Fahrerverhaltensmodells in Das Simulationsframework Openpass Zur Re-Simulation Des Verkehrsunfallgeschehens Auf Bundesautobahnen, Vdi-Tagung Fahrzeugsicherheit, Berlin, (2021)
[15] Mai M., Wang L.G., Advancement of the car following model of Wiedemann on lower velocity ranges for urban traffic simulation, Transportation Research Part F: Traffic Psychology and Behaviour, 61, pp. 30-37, (2019)
[16] Witt M., Kompass K., Wang L., Kates R., Mai M.G., Driver profiling-Data-based identification of driver behavior dimensions and affecting driver characteristics for multi-agent traffic simulation, Transportation Research Part F: Traffic Psychology and Behaviour, 64, pp. 361-376, (2019)
[17] Schlag B., Mai M., Weller G., Schreiber F., Prokop G., Deutsch D., Grun T., Rehbein P.M., Wie beeinflussen Warnungen unterschiedliche Parameter des Fahrerverhaltens? – 4 experimentelle Studien, Zeitschrift für Verkehrssicherheit, 64, 3, pp. 199-208, (2018)
[18] Ring P., Hammouda M., Wang L., Mai M.G., Modelling anticipative and predicitive driver behavior in the context of prospective safety assessment, Graz Symposium Virtual Vehicle, Graz, (2018)
[19] Mai M., Fahrerverhaltensmodellierung für die prospektive, stochastische Wirksamkeitsbewertung von Fahrerassistenzsystemen der Aktiven Fahrzeugsicherheit, Dissertation, Technische Universität Dresden, (2017)
[20] Mai M., Tuschen T., A physiological based Driver Model for longitudinal Vehicle Guidance and its Challenges in Validation, International Symposium on Future Active Safety Technology Towards zero traffic accidents, Göteborg, (2015)

← 1 2 3 4 →