Comparing two systematic approaches for testing automated driving functions

被引:7
|
作者
Felbinger, Hermann [1 ]
Klueck, Florian [2 ]
Li, Yihao [3 ]
Nica, Mihai [1 ]
Tao, Jianbo [1 ]
Wotawa, Franz [2 ]
Zimmermann, Martin [2 ]
机构
[1] AVL List GmbH, Graz, Austria
[2] Graz Univ Technol, Inst Software Technol, CD Lab Qual Assurance Methodol Autonomous Cyber P, Graz, Austria
[3] Graz Univ Technol, Inst Software Technol, Graz, Austria
关键词
system testing; verifying automated driving functions; combinatorial testing; search-based testing;
D O I
10.1109/iccve45908.2019.8965209
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Thoroughly validating and verifying automated or autonomous driving functions is inevitable for assuring to meet quality criteria for safety-critical systems. In this paper, we discuss two system testing techniques that have been already used for detecting critical situations for the automated emergency braking function based on vehicle simulations. In particular, we introduce combinatorial testing and search-based testing techniques and compare them. Whereas the first is for identifying interactions of parameters that lead to harmful situations considering predefined value domains, the latter is for finding parameter values that cause such critical situations. We discuss the underlying foundations behind the methods as well as their potential application areas. In addition, we summarize the results obtained when using these methods for testing automated emergency braking.
引用
收藏
页数:6
相关论文
共 50 条
  • [21] Evaluation of Virtual Traffic Situations for Testing Automated Driving Functions based on Multidimensional Criticality Analysis
    Huber, Bernd
    Herzog, Steffen
    Sippl, Christoph
    German, Reinhard
    Djanatliev, Anatoli
    2020 IEEE 23RD INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2020,
  • [22] Ontology-based adaptive testing for automated driving functions using data mining techniques
    Elgharbawy, M.
    Schwarzhaupt, A.
    Frey, M.
    Gauterin, F.
    TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR, 2019, 66 : 234 - 251
  • [23] A fast and accurate hybrid simulation model for the large-scale testing of automated driving functions
    Fraikin, Nicolas
    Funk, Kilian
    Frey, Michael
    Gauterin, Frank
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING, 2020, 234 (04) : 1183 - 1196
  • [24] Chassis Functions for Highly Automated Driving and their Verification
    Perner, Marcus
    Gebhardt, Martin
    Weinhold, Nick
    ATZ worldwide, 2022, 124 (06) : 44 - 47
  • [25] Automated driving and autonomous functions on road vehicles
    Gordon, T. J.
    Lidberg, M.
    VEHICLE SYSTEM DYNAMICS, 2015, 53 (07) : 958 - 994
  • [26] Data Management in the Development of Automated Driving Functions
    Niederbrucker, Gerhard
    Pfaff, Albrecht
    Donn, Christian
    ATZ worldwide, 2020, 122 (11): : 46 - 51
  • [27] Scenario Identification for Validation of Automated Driving Functions
    Elrofai, Hala
    Worm, Daniel
    den Camp, Olaf Op
    ADVANCED MICROSYSTEMS FOR AUTOMOTIVE APPLICATIONS 2016: SMART SYSTEMS FOR THE AUTOMOBILE OF THE FUTURE, 2016, : 153 - 163
  • [28] Challenges in Functional Testing on the Way to Automated Driving
    Wittel, Steffen
    Ulmer, Daniel
    Buehler, Oliver
    TWELFTH INTERNATIONAL CONFERENCE ON SYSTEMS (ICONS 2017), 2017, : 16 - 21
  • [29] Automated Testing of Ultrawideband Positioning for Autonomous Driving
    Vedder, Benjamin
    Svensson, Bo Joel
    Vinter, Jonny
    Jonsson, Magnus
    JOURNAL OF ROBOTICS, 2020, 2020
  • [30] An Analysis of Testing Scenarios for Automated Driving Systems
    Liu, Siyuan
    Capretz, Luiz Fernando
    2021 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION AND REENGINEERING (SANER 2021), 2021, : 622 - 629