Generating Critical Driving Scenarios from Accident Sketches

Gambi, Alessio; Nguyen, Vuong; Ahmed, Jasim

doi:10.1109/aitest55621.2022.00022

Cited by 10 publications

(6 citation statements)

References 22 publications

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“…Synthetic data are also used in other domains, such as covering nuclear power plant accidents [117], ensuring safe drone landings [118], and generating critical autonomous driving situations to improve AI-based systems. A number of techniques have already been used to generate safety-critical driving scenarios, such as those based on accident sketches [119], based on a search algorithm that iteratively optimizes behavior action sequences of the surrounding traffic participants [120], based on influential behavior patterns [121] or based on reinforcement learning [122]. The approaches mentioned here for representing critical situations are domain-specific.…”

Section: Technical Robustness and Generalizationmentioning

confidence: 99%

A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities

Meiser,

Zinnikus

2024

Energies

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To achieve the energy transition, energy and energy efficiency are becoming more and more important in society. New methods, such as Artificial Intelligence (AI) and Machine Learning (ML) models, are needed to coordinate supply and demand and address the challenges of the energy transition. AI and ML are already being applied to a growing number of energy infrastructure applications, ranging from energy generation to energy forecasting and human activity recognition services. Given the rapid development of AI and ML, the importance of Trustworthy AI is growing as it takes on increasingly responsible tasks. Particularly in the energy domain, Trustworthy AI plays a decisive role in designing and implementing efficient and reliable solutions. Trustworthy AI can be considered from two perspectives, the Model-Centric AI (MCAI) and the Data-Centric AI (DCAI) approach. We focus on the DCAI approach, which relies on large amounts of data of sufficient quality. These data are becoming more and more synthetically generated. To address this trend, we introduce the concept of Synthetic Data-Centric AI (SDCAI). In this survey, we examine Trustworthy AI within a Synthetic Data-Centric AI context, focusing specifically on the role of simulation and synthetic data in enhancing the level of Trustworthy AI in the energy domain.

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Section: Technical Robustness and Generalizationmentioning

confidence: 99%

A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities

Meiser,

Zinnikus

2024

Energies

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“…The fourth step is to identify and extract the scenarios in the collected data using pre-defined scenarios (7x) or scenarios extracted by rule-based (28x), unsupervised (7x) or supervised approaches (5x) -or any combination of them (12x). Pre-defined scenarios [33], [37], [43], [64], [70], [71] are often extracted from accident data because every recorded accident can be considered a scenario. Rule-based approaches (see Table A1) rely on clearly defined parameter ranges or thresholds to identify scenarios and often require extracted road user specific trajectories from, e.g., video material using Convolutional Neural Networks (CNNs) [127].…”

Section: A Process Of Scenario Generationmentioning

confidence: 99%

“…A rarely used data source is a dataset of pre-crafted scenarios or trajectories [20], [23], [68], [73]. Accident databases, as data sources, share approximately 26% of the identified methods, which, in most cases, originate from a set of police accident reports [19], [24], [28]- [30], [33], [35], [43], [46], [47], [55], [61], [67], [69], [70], [75], [76]. In some cases, only accidents involving vehicles with an ADS were considered and filtered from existing accident databases [34], [37], [38], [64].…”

Section: B Categorizationmentioning

confidence: 99%

Categorizing Data-Driven Methods for Test Scenario Generation to Assess Automated Driving Systems

Bäumler,

Linke,

Prokop

2024

IEEE Access

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This survey aims to provide an overview of various methods for generating data-driven test scenarios for assessing automated driving systems (ADS). The survey updates the overall process of scenario generation and categorizes the current methods using a systematic literature review of 64 studies identified between 2017 and 01/2023. Overall, we demonstrate that the data-driven scenario generation process should be updated by another process step, scenario fusion, leading to seven process steps: (1) scope definition, (2) primary data source selection, (3) primary data collection, (4) scenario identification, ( 5) scenario fusion, ( 6) scenario generation, and (7) scenario evaluation. "Scenario fusion" aims to fuse scenarios identified in different data sources for a better coverage of the ADSs' operational design domains (ODDs) and a more comprehensive scenario description. Moreover, we introduce an improved definition for the representativity of test scenario catalogs, which helps improve the collection of traffic data using sampling plans. Also, we show that real driving and police accident data are the most commonly used data input sources. Besides, we illustrate that the ODD is often not defined. Finally, we discuss that the standardization of test scenario generation is difficult because most methods do not address specific ADSs and test environments, and do not provide standardized interfaces. Overall, we recommend comparing existing approaches using the same input data and researching the mutual supplementation of the existing methods. Finally, pre-defined case studies, further standardized terminology, and standards for test execution and evaluation can help speed up the standardization process.INDEX TERMS Advanced driver assistance systems, autonomous driving, system validation, vehicle safety I. INTRODUCTION

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“…Hence, scenario-based testing focuses more on how to generate critical scenarios efficiently. Researchers have proposed some methods to generate critical scenarios for ADS testing, which can be categorized into data-driven methods [12,17,19,24,32,34,35,38] and searching-based methods [18, 22, 23, 28, 43-45, 55, 57].…”

Section: Related Workmentioning

confidence: 99%

“…Data-driven methods aim to generate critical scenarios from existing scenario description sources, such as accident reports and real-world motion videos. For example, Gambi et al proposed methods to generate critical simulation scenarios from police reports [17] and accident sketches, i.e., official crash reports with visual representations [19]. Searching-based methods aim to search for critical scenarios using different technologies, such as evolutionary algorithms and guided fuzzing technologies.…”

Section: Related Workmentioning

confidence: 99%

BehAVExplor: Behavior Diversity Guided Testing for Autonomous Driving Systems

Cheng

Zhou

Xie

2023

Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis

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Testing Autonomous Driving Systems (ADSs) is a critical task for ensuring the reliability and safety of autonomous vehicles. Existing methods mainly focus on searching for safety violations while the diversity of the generated test cases is ignored, which may generate many redundant test cases and failures. Such redundant failures can reduce testing performance and increase failure analysis costs.In this paper, we present a novel behavior-guided fuzzing technique (BehAVExplor) to explore the different behaviors of the ego vehicle (i.e., the vehicle controlled by the ADS under test) and detect diverse violations. Specifically, we design an efficient unsupervised model, called BehaviorMiner, to characterize the behavior of the ego vehicle. BehaviorMiner extracts the temporal features from the given scenarios and performs a clustering-based abstraction to group behaviors with similar features into abstract states. A new test case will be added to the seed corpus if it triggers new behaviors (e.g., cover new abstract states). Due to the potential conflict between the behavior diversity and the general violation feedback, we further propose an energy mechanism to guide the seed selection and the mutation. The energy of a seed quantifies how good it is. We evaluated BehAVExplor on Apollo, an industrial-level ADS, and LGSVL simulation environment. Empirical evaluation results show that BehAVExplor can effectively find more diverse violations than the state-of-the-art. CCS CONCEPTS• Software and its engineering → Software testing and debugging.

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Generating Critical Driving Scenarios from Accident Sketches

Cited by 10 publications

References 22 publications

A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities

A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities

Categorizing Data-Driven Methods for Test Scenario Generation to Assess Automated Driving Systems

BehAVExplor: Behavior Diversity Guided Testing for Autonomous Driving Systems

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