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1001 Ways of Scenario Generation for Testing of Self-driving Cars: A Survey

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arxiv 2304.10850 v1 pith:PRY57NZD submitted 2023-04-21 cs.RO cs.SE

1001 Ways of Scenario Generation for Testing of Self-driving Cars: A Survey

classification cs.RO cs.SE
keywords scenariodifferentgenerationmethodsdrivingliteraturescenariossurvey
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Scenario generation is one of the essential steps in scenario-based testing and, therefore, a significant part of the verification and validation of driver assistance functions and autonomous driving systems. However, the term scenario generation is used for many different methods, e.g., extraction of scenarios from naturalistic driving data or variation of scenario parameters. This survey aims to give a systematic overview of different approaches, establish different categories of scenario acquisition and generation, and show that each group of methods has typical input and output types. It shows that although the term is often used throughout literature, the evaluated methods use different inputs and the resulting scenarios differ in abstraction level and from a systematical point of view. Additionally, recent research and literature examples are given to underline this categorization.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. A Survey on the Applications of Generative Artificial Intelligence in Automated Driving Systems Test Scenario Generation Methods

    cs.SE 2025-12 reject novelty 4.0

    A literature survey of scenario-generation methods for ADS testing that adds an unvalidated AII/RAS/OCS metric suite and ODD-difficulty schema, undermined by inconsistent calculations in the worked examples.