Defining and Substantiating the Terms Scene, Situation, and Scenario for Automated Driving

Ulbrich, Simon; Menzel, Till; Reschka, Andreas; Schuldt, Fabian; Maurer, Markus

doi:10.1109/itsc.2015.164

Cited by 392 publications

(299 citation statements)

References 12 publications

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“…Schuldt et al [7] suggest a scenario-based test approach as well as a 4-layer-model for structuring scenarios. Definitions of the terms scene, situation and scenario are provided by Ulbrich et al [8]. Menzel et al [3] use theses definitions as a basis and motivate different notations for representing scenarios during the development process using the example of the standard ISO 26262 [9].…”

Section: Related Workmentioning

confidence: 99%

From Functional to Logical Scenarios: Detailing a Keyword-Based Scenario Description for Execution in a Simulation Environment

Menzel

Bagschik

Isensee

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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Scenario-based development and test processes are a promising approach for verifying and validating automated driving functions. For this purpose, scenarios have to be generated during the development process in a traceable manner. In early development stages, the operating scenarios of the item to be developed are usually described in an abstract, linguistic way. Within the scope of a simulation-assisted test process, these linguistically described scenarios have to be transformed into a state space representation and converted into data formats which can be used with the respective simulation environment. Currently, this step of detailing scenarios takes a considerable manual effort. Furthermore, a standardized interpretation of the linguistically described scenarios and a consistent transformation into the data formats are not guaranteed due to multiple authors as well as many constraints between the scenario parameters. In this paper, the authors present an approach to automatically detail a keyword-based scenario description for execution in a simulation environment and provide a basis for test case generation. As a first step, the keyword-based description is transformed into a parameter space representation. At the same time, constraints regarding the selection and combination of parameter values are documented for the following process steps (e. g. evolutionary or stochastic test methods). As a second step, the parameter space representation is converted into data formats required by the simulation environment. As an example, the authors use scenarios on German freeways and convert them into the data formats OpenDRIVE (description of the road) and OpenSCENARIO (description of traffic participants and environmental conditions) for execution in the simulation environment Virtual Test Drive.

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Section: Related Workmentioning

confidence: 99%

From Functional to Logical Scenarios: Detailing a Keyword-Based Scenario Description for Execution in a Simulation Environment

Menzel

Bagschik

Isensee

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

Self Cite

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“…We adopt the definition of a scene proposed in[3]; i.e., "A scene describes a snapshot of the environment including the scenery and dynamic elements, as well as all actors and observers self-representations, and the relationships among those entities. "…”

mentioning

confidence: 99%

Is it Safe to Drive? An Overview of Factors, Metrics, and Datasets for Driveability Assessment in Autonomous Driving

Guo¹,

Kurup²,

Shah³

2020

IEEE Trans. Intell. Transport. Syst.

143

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With recent advances in learning algorithms and hardware development, autonomous cars have shown promise when operating in structured environments under good driving conditions. However, for complex, cluttered and unseen environments with high uncertainty, autonomous driving systems still frequently demonstrate erroneous or unexpected behaviors, that could lead to catastrophic outcomes. Autonomous vehicles should ideally adapt to driving conditions; while this can be achieved through multiple routes, it would be beneficial as a first step to be able to characterize Driveability in some quantified form. To this end, this paper aims to create a framework for investigating different factors that can impact driveability. Also, one of the main mechanisms to adapt autonomous driving systems to any driving condition is to be able to learn and generalize from representative scenarios. The machine learning algorithms that currently do so learn predominantly in a supervised manner and consequently need sufficient data for robust and efficient learning. Therefore, we also perform a comparative overview of 45 public driving datasets that enable learning and publish this dataset index at https://sites.google.com/view/driveability-surveydatasets. Specifically, we categorize the datasets according to use cases, and highlight the datasets that capture complicated and hazardous driving conditions which can be better used for training robust driving models. Furthermore, by discussions of what driving scenarios are not covered by existing public datasets and what driveability factors need more investigation and data acquisition, this paper aims to encourage both targeted dataset collection and the proposal of novel driveability metrics that enhance the robustness of autonomous cars in adverse environments.

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“…Input: SM and offline generated library Output: Customized library 1 Step 1: Observe f with initial testing scenarios. (Sec 4.A) 2 Step 2: while the stop criteria (e.g., budget or estimation precision) is not satisfied do 3 Step 2.1: Obtain the estimationf (Sec 4.B); 4 Step 2.2: Update SM and library (Sec 4.C); 5 Step 2.3: Decide next iteration of testing scenarios (Sec 4.D); 6 Step 2.4: Observe f by testing the CAV with new scenarios; 7 end…”

Section: B Bayesian Optimization Schemementioning

confidence: 99%

Testing Scenario Library Generation for Connected and Automated Vehicles, Part I: Methodology

Feng

et al. 2021

IEEE Trans. Intell. Transport. Syst.

150

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How to generate testing scenario library for connected and automated vehicles (CAVs) is a major challenge faced by the industry. In previous studies, to evaluate maneuver challenge of a scenario, surrogate models (SMs) are often used without explicit knowledge of the CAV under test. However, performance dissimilarities between the SM and the CAV under test usually exist, and it can lead to the generation of suboptimal scenario library. In this paper, an adaptive testing scenario library generation (ATSLG) method is proposed to solve this problem. A customized testing scenario library for a specific CAV model will be generated as the result of the adaptive process. To estimate the performance dissimilarities and leverage each test of the CAV, Bayesian optimization techniques are applied with classification-based Gaussian Process Regression and a newdesigned acquisition function. Comparing with a pre-determined library, a CAV can be tested and evaluated in a more efficient manner with the customized library. To validate the proposed method, a cut-in and a highway exit case are studied for safety and functionality evaluation respectively. For both two cases, the proposed method can further accelerate the evaluation process by a few orders of magnitudes.

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Defining and Substantiating the Terms Scene, Situation, and Scenario for Automated Driving

Cited by 392 publications

References 12 publications

From Functional to Logical Scenarios: Detailing a Keyword-Based Scenario Description for Execution in a Simulation Environment

From Functional to Logical Scenarios: Detailing a Keyword-Based Scenario Description for Execution in a Simulation Environment

Is it Safe to Drive? An Overview of Factors, Metrics, and Datasets for Driveability Assessment in Autonomous Driving

Testing Scenario Library Generation for Connected and Automated Vehicles, Part I: Methodology

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