Is Scenario Generation Ready for SOTIF? A Systematic Literature Review
- URL: http://arxiv.org/abs/2308.02273v2
- Date: Tue, 8 Aug 2023 09:35:18 GMT
- Title: Is Scenario Generation Ready for SOTIF? A Systematic Literature Review
- Authors: Lukas Birkemeyer, Christian King, Ina Schaefer
- Abstract summary: We perform a Systematic Literature Review to identify techniques that generate scenarios complying with requirements of the SOTIF-standard.
We investigate which details of the real-world are covered by generated scenarios, whether scenarios are specific for a system under test or generic, and whether scenarios are designed to minimize the set of unknown and hazardous scenarios.
- Score: 3.1491385041570146
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Scenario-based testing is considered state-of-the-art to verify and validate
Advanced Driver Assistance Systems or Automated Driving Systems. Due to the
official launch of the SOTIF-standard (ISO 21448), scenario-based testing
becomes more and more relevant for releasing those Highly Automated Driving
Systems. However, an essential missing detail prevent the practical application
of the SOTIF-standard: How to practically generate scenarios for scenario-based
testing? In this paper, we perform a Systematic Literature Review to identify
techniques that generate scenarios complying with requirements of the
SOTIF-standard. We classify existing scenario generation techniques and
evaluate the characteristics of generated scenarios wrt. SOTIF requirements. We
investigate which details of the real-world are covered by generated scenarios,
whether scenarios are specific for a system under test or generic, and whether
scenarios are designed to minimize the set of unknown and hazardous scenarios.
We conclude that scenarios generated with existing techniques do not comply
with requirements implied by the SOTIF-standard; hence, we propose directions
for future research.
Related papers
- RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework [69.4501863547618]
This paper introduces RAGEval, a framework designed to assess RAG systems across diverse scenarios.
With a focus on factual accuracy, we propose three novel metrics Completeness, Hallucination, and Irrelevance.
Experimental results show that RAGEval outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples.
arXiv Detail & Related papers (2024-08-02T13:35:11Z) - GOOSE: Goal-Conditioned Reinforcement Learning for Safety-Critical Scenario Generation [0.14999444543328289]
Goal-conditioned Scenario Generation (GOOSE) is a goal-conditioned reinforcement learning (RL) approach that automatically generates safety-critical scenarios.
We demonstrate the effectiveness of GOOSE in generating scenarios that lead to safety-critical events.
arXiv Detail & Related papers (2024-06-06T08:59:08Z) - SOTIF-Compliant Scenario Generation Using Semi-Concrete Scenarios and
Parameter Sampling [6.195203785530687]
SOTIF standard requires scenario-based testing to verify and validate Advanced Driver Assistance Systems and Automated Driving Systems.
Existing scenario generation approaches either focus on exploring or exploiting the scenario space.
This paper proposes semi-concrete scenarios and parameter sampling to generate SOTIF-compliant test suites.
arXiv Detail & Related papers (2023-08-14T09:25:24Z) - Tree-Based Scenario Classification: A Formal Framework for Coverage
Analysis on Test Drives of Autonomous Vehicles [0.0]
In scenario-based testing, relevant (driving) scenarios are the basis of tests.
We address the open challenges of classifying sets of scenarios and measuring coverage of theses scenarios in recorded test drives.
arXiv Detail & Related papers (2023-07-11T08:30:57Z) - A Requirements-Driven Platform for Validating Field Operations of Small
Uncrewed Aerial Vehicles [48.67061953896227]
DroneReqValidator (DRV) allows sUAS developers to define the operating context, configure multi-sUAS mission requirements, specify safety properties, and deploy their own custom sUAS applications in a high-fidelity 3D environment.
The DRV Monitoring system collects runtime data from sUAS and the environment, analyzes compliance with safety properties, and captures violations.
arXiv Detail & Related papers (2023-07-01T02:03:49Z) - Clustering-based Criticality Analysis for Testing of Automated Driving
Systems [0.18416014644193066]
This paper focuses on the the goal to reduce the scenario set by clustering concrete scenarios from a single logical scenario.
By employing clustering techniques, redundant and uninteresting scenarios can be identified and eliminated, resulting in a representative scenario set.
arXiv Detail & Related papers (2023-06-22T08:36:20Z) - UMSE: Unified Multi-scenario Summarization Evaluation [52.60867881867428]
Summarization quality evaluation is a non-trivial task in text summarization.
We propose Unified Multi-scenario Summarization Evaluation Model (UMSE)
Our UMSE is the first unified summarization evaluation framework engaged with the ability to be used in three evaluation scenarios.
arXiv Detail & Related papers (2023-05-26T12:54:44Z) - Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic
Prior [135.78858513845233]
STRIVE is a method to automatically generate challenging scenarios that cause a given planner to produce undesirable behavior, like collisions.
To maintain scenario plausibility, the key idea is to leverage a learned model of traffic motion in the form of a graph-based conditional VAE.
A subsequent optimization is used to find a "solution" to the scenario, ensuring it is useful to improve the given planner.
arXiv Detail & Related papers (2021-12-09T18:03:27Z) - Addressing the IEEE AV Test Challenge with Scenic and VerifAI [10.221093591444731]
This paper summarizes our formal approach to testing autonomous vehicles (AVs) in simulation for the IEEE AV Test Challenge.
We demonstrate a systematic testing framework leveraging our previous work on formally-driven simulation for intelligent cyber-physical systems.
arXiv Detail & Related papers (2021-08-20T04:51:27Z) - Multi Agent System for Machine Learning Under Uncertainty in Cyber
Physical Manufacturing System [78.60415450507706]
Recent advancements in predictive machine learning has led to its application in various use cases in manufacturing.
Most research focused on maximising predictive accuracy without addressing the uncertainty associated with it.
In this paper, we determine the sources of uncertainty in machine learning and establish the success criteria of a machine learning system to function well under uncertainty.
arXiv Detail & Related papers (2021-07-28T10:28:05Z) - Generating and Characterizing Scenarios for Safety Testing of Autonomous
Vehicles [86.9067793493874]
We propose efficient mechanisms to characterize and generate testing scenarios using a state-of-the-art driving simulator.
We use our method to characterize real driving data from the Next Generation Simulation (NGSIM) project.
We rank the scenarios by defining metrics based on the complexity of avoiding accidents and provide insights into how the AV could have minimized the probability of incurring an accident.
arXiv Detail & Related papers (2021-03-12T17:00:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.