A Scenario-Based Development Framework for Autonomous Driving

This article summarizes the research progress of scenario-based testing and development technology for autonomous vehicles. We systematically analyzed previous research works and proposed the definition of scenario, the elements of the scenario ontology, the data source of the scenario, the processing method of the scenario data, and scenario-based V-Model. Moreover, we summarized the automated test scenario construction method by random scenario generation and dangerous scenario generation.

[1]  Soo-Won Kim,et al.  Design of Pedestrian Target Selection With Funnel Map for Pedestrian AEB System , 2017, IEEE Transactions on Vehicular Technology.

[2]  Klaus Bengler,et al.  Area-wide real-world test scenarios of poor visibility for safe development of automated vehicles , 2018 .

[3]  Michael J. Black,et al.  OpenDR: An Approximate Differentiable Renderer , 2014, ECCV.

[4]  Erik Steinmetz,et al.  Processing of Eye/Head-Tracking Data in Large-Scale Naturalistic Driving Data Sets , 2012, IEEE Transactions on Intelligent Transportation Systems.

[5]  Kai Koskimies,et al.  Automated Support for Modeling OO Software , 1998, IEEE Softw..

[6]  Korbinian Groh,et al.  Towards a Scenario-Based Assessment Method for Highly Automated Driving Functions , 2017 .

[7]  Hermann Winner,et al.  Concept and development of a unified ontology for generating test and use-case catalogues for assisted and automated vehicle guidance , 2014 .

[8]  Yangshan Tang,et al.  Development of scenes drawing system for urban road accidents , 2011, 2011 IEEE International Conference on Mechatronics and Automation.

[9]  Jörg Seewig,et al.  Automated scenario generation for regression testing of autonomous vehicles , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[10]  Ruben Schilling,et al.  Validation of Automated Driving Functions , 2016 .

[11]  Allan I. McInnes.  Model-checking the Flooding Time Synchronization Protocol , 2009, 2009 IEEE International Conference on Control and Automation.

[12]  Christian Martel,et al.  OpenScenario: A Flexible Integrated Environment to Develop Educational Activities Based on Pedagogical Scenarios , 2009, 2009 Ninth IEEE International Conference on Advanced Learning Technologies.

[13]  Andreas Fellner,et al.  Model-based, mutation-driven test case generation via heuristic-guided branching search , 2017, MEMOCODE.

[14]  Andreas Geiger,et al.  Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Eric Sax,et al.  Machine learning and deep neural network — Artificial intelligence core for lab and real-world test and validation for ADAS and autonomous vehicles: AI for efficient and quality test and validation , 2017, 2017 Intelligent Systems Conference (IntelliSys).

[16]  Olaf Op den Camp,et al.  Scenario Identification for Validation of Automated Driving Functions , 2016 .

[17]  Erwin de Gelder,et al.  Assessment of Automated Driving Systems using real-life scenarios , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[18]  Ernst Stadlober,et al.  Virtual Stochastic Testing of Advanced Driver Assistance Systems , 2016 .

[19]  Nanning Zheng,et al.  Parallel testing of vehicle intelligence via virtual-real interaction , 2019, Science Robotics.

[20]  Wenshuo Wang,et al.  Extracting Traffic Primitives Directly From Naturalistically Logged Data for Self-Driving Applications , 2017, IEEE Robotics and Automation Letters.

[21]  T.M. Duffy,et al.  Scenario-Based Design: Envisioning Work and Technology in System Development [Book Review] , 1996, IEEE Transactions on Professional Communication.

[22]  David González,et al.  A Review of Motion Planning Techniques for Automated Vehicles , 2016, IEEE Transactions on Intelligent Transportation Systems.

[23]  Jihua Zhu,et al.  Spatiotemporal road scene reconstruction using superpixel-based Markov random field , 2020, Inf. Sci..

[24]  Markus Maurer,et al.  Scenarios for Development, Test and Validation of Automated Vehicles , 2018, 2018 IEEE Intelligent Vehicles Symposium (IV).

[25]  Michael D. Dettinger,et al.  CLIMATE CHANGE SCENARIOS AND SEA LEVEL RISE ESTIMATES FOR THE CALIFORNIA 2008 CLIMATE CHANGE SCENARIOS ASSESSMENT , 2009 .

[26]  John D. Gould,et al.  The 1984 Olympic Message System: a test of behavioral principles of system design , 1987, CACM.

[27]  Henrik Eriksson,et al.  Status of test methods for autonomous emergency braking systems - Results from the active test project , 2013 .

[28]  Rong Yan,et al.  Automatically labeling video data using multi-class active learning , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[29]  Huei Peng,et al.  Development and evaluation of collision warning/collision avoidance algorithms using an errable driver model , 2010 .

[30]  Joshué Pérez,et al.  A complete framework for developing and testing automated driving controllers , 2017 .

[31]  Andrea A. diSessa,et al.  A Principled Design for an Integrated Computational Environment , 1985, Hum. Comput. Interact..

[32]  Wenfei Fan,et al.  Conditional functional dependencies for capturing data inconsistencies , 2008, TODS.

[33]  Geoffrey E. Hinton,et al.  Stochastic Neighbor Embedding , 2002, NIPS.

[34]  Andrea Leitner,et al.  Challenges for reproducing real-life test runs in simulation for validating automated driving functions , 2018, Elektrotech. Informationstechnik.

[35]  Andreas Pütz,et al.  System validation of highly automated vehicles with a database of relevant traffic scenarios , 2017 .

[36]  Jianzhong Li,et al.  Towards certain fixes with editing rules and master data , 2010, The VLDB Journal.

[37]  Kangwon Lee,et al.  Longitudinal driver model and collision warning and avoidance algorithms based on human driving databases. , 2004 .

[38]  Li Huang,et al.  Study on the Test Scenarios of Level 2 Automated Vehicles , 2018, 2018 IEEE Intelligent Vehicles Symposium (IV).

[39]  Subhransu Maji,et al.  Automatic Image Annotation using Deep Learning Representations , 2015, ICMR.

[40]  Ding Zhao,et al.  Evaluation of automated vehicles in the frontal cut-in scenario — An enhanced approach using piecewise mixture models , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[41]  Ulrich Eberle,et al.  Simulation-Based Identification of Critical Scenarios for Cooperative and Automated Vehicles , 2018 .

[42]  Wassim G. Najm,et al.  Pre-Crash Scenario Typology for Crash Avoidance Research , 2007 .

[43]  Markus Maurer,et al.  Map-relative localization in lane-level maps for ADAS and autonomous driving , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[44]  Markus Maurer,et al.  Ontology based Scene Creation for the Development of Automated Vehicles , 2017, 2018 IEEE Intelligent Vehicles Symposium (IV).

[45]  J. Swenson,et al.  Reference scenarios for deforestation and forest degradation in support of REDD: a review of data and methods , 2008 .

[46]  Mikhail Belkin,et al.  Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..

[47]  Feng Gao,et al.  Automatic Generation Method of Test Scenario for ADAS Based on Complexity , 2017, ICVS 2017.

[48]  Fikret Sivrikaya,et al.  Time synchronization in sensor networks: a survey , 2004, IEEE Network.

[49]  Sören Hohmann,et al.  Bridging the Gap between Open Loop Tests and Statistical Validation for Highly Automated Driving , 2017 .

[50]  Fabian Schuldt,et al.  Defining and Substantiating the Terms Scene, Situation, and Scenario for Automated Driving , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[51]  Frank Flemisch,et al.  The theater-system technique: agile designing and testing of system behavior and interaction, applied to highly automated vehicles , 2009, AutomotiveUI.