Jan-Pieter Paardekooper

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The development of new assessment methods for the performance of automated vehicles is essential to enable the deployment of automated driving technologies, due to the complex operational domain of automated vehicles. One contributing method is scenario-based assessment in which test cases are derived from real-world road traffic scenarios obtained from driving data. Given the complexity of the reality that is being modeled in these scenarios, it is a challenge to define a structure for capturing these scenarios. An intensional definition that provides a set of characteristics that are deemed to be both necessary and sufficient to qualify as a scenario assures that the scenarios constructed are both complete and intercomparable. In this article, we develop a comprehensive and operable definition of the notion of scenario while considering existing definitions in the literature. This is achieved by proposing an …

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Abstract taken from Google Scholar:

The development of assessment methods for the performance of Automated Vehicles (AVs) is essential to enable the deployment of automated driving technologies, due to the complex operational domain of AVs. One candidate is scenario-based assessment, in which test cases are derived from real-world road traffic scenarios obtained from driving data. Because of the high variety of the possible scenarios, using only observed scenarios for the assessment is not sufficient. Therefore, methods for generating additional scenarios are necessary. Our contribution is twofold. First, we propose a method to determine the parameters that describe the scenarios to a sufficient degree while relying less on strong assumptions on the parameters that characterize the scenarios. By estimating the probability density function (pdf) of these parameters, realistic parameter values can be generated. Second, we present the Scenario …

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The development of safety validation methods is essential for the safe deployment and operation of Automated Driving Systems (ADSs). One of the goals of safety validation is to prospectively evaluate the risk of an ADS dealing with real-world traffic. ISO 26262 and ISO/DIS 21448, the leading standards in automotive safety, provide an approach to estimate the risk where the former focuses on risks due to potential malfunctioning of components and the latter focuses on risks due to possible functional insufficiencies. The main shortcomings of the approach provided in ISO 26262 are that it depends on subjective judgments of safety experts and that only a qualitative risk estimation is performed. ISO/DIS 21448 addresses these shortcomings partially by providing statistical methods to guide the safety validation, but no complete method is provided to quantify the risk. The first objective of this article is to propose a …

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The safety assessment of Automated Vehicles (AVs) is an important aspect of the development cycle of AVs. A scenario-based assessment approach is accepted by many players in the field as part of the complete safety assessment. A scenario is a representation of a situation on the road to which the AV needs to respond appropriately. One way to generate the required scenario-based test descriptions is to parameterize the scenarios and to draw these parameters from a probability density function (pdf). Because the shape of the pdf is unknown beforehand, assuming a functional form of the pdf and fitting the parameters to the data may lead to inaccurate fits. As an alternative, Kernel Density Estimation (KDE) is a promising candidate for estimating the underlying pdf, because it is flexible with the underlying distribution of the parameters. Drawing random samples from a pdf estimated with KDE is possible without …

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An increasing number of tasks is being taken over from the human driver as automated driving technology is developed. Accidents have been reported in situations where the automated driving technology was not able to function according to specifications. As data-driven Artificial Intelligence (AI) systems are becoming more ubiquitous in automated vehicles, it is increasingly important to make AI systems situational aware. One aspect of this is determining whether these systems are competent in the current and immediate traffic situation, or that they should hand over control to the driver or safety system.We aim to increase the safety of automated driving functions by combining data-driven AI systems with knowledge-based AI into a hybrid-AI system that can reason about competence in the traffic state now and in the next few seconds. We showcase our method using an intention prediction algorithm that is based on a deep neural network and trained with real-world data of traffic participants performing a cut-in maneuver in front of the vehicle. This is combined with a unified, quantitative representation of the situation on the road represented by an ontology-based knowledge graph and firstorder logic inference rules, that takes as input both the observations of the sensors of the automated vehicle as well as the output of the intention prediction algorithm. The knowledge graph utilises the two features of importance, based on domain knowledge, and doubt, based on the observations and information about the dataset, to reason about the competence of the intention prediction algorithm. We have applied the competence assessment of the intention …

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Due to a mistake, the BPASS v2. 0 binary-star model (Stanway et al. 2016) used in Figure 6 (b) of the published article featured a different initial mass function (IMF) than the Geneva single-star model used for comparison. While the Geneva model used the Kroupa universal IMF (Kroupa 2001) with upper slope α= 2.3 ( µ a-dN M M d) at 0.5 M (Me) 100, the BPASS model used a slope of α= 2.0 throughout the same mass interval. Hence, in its original form, that figure actually illustrated the combined effect of assuming binary stars and adopting a more extreme IMF. This resulted in a greater number of massive stars and a boosted ionizing flux for the BPASS model.In the revised Figure 6 (b), we show the corrected comparison with identical IMF slopes (α= 2.3). In this case, the shift between the two sets of simulated galaxies in the EW (Hβ)–β diagram is more modest than in the published article. The galaxies simulated …

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Abstract taken from Google Scholar:

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Abstract taken from Google Scholar:

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Abstract taken from Google Scholar:

Scenario-based methods for the assessment of Automated Vehicles (AVs) are widely supported by many players in the automotive field. Scenarios captured from real-world data can be used to define the scenarios for the assessment and to estimate their relevance. Therefore, different techniques are proposed for capturing scenarios from real-world data. In this paper, we propose a new method to capture scenarios from real-world data using a two-step approach. The first step consists in automatically labeling the data with tags. Second, we mine the scenarios, represented by a combination of tags, based on the labeled tags. One of the benefits of our approach is that the tags can be used to identify characteristics of a scenario that are shared among different type of scenarios. In this way, these characteristics need to be identified only once. Furthermore, the method is not specific for one type of scenario and …

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Automated driving is expected to play a central role in future mobility systems by enabling, among other benefits, mobility-as-a-service schemes and better road utilization. To this end, automated vehicles must not only be functionally safe. They should also be perceived as driving safely by other traffic participants and have a positive impact on traffic safety. However, to the best of our knowledge, there is no consensus yet on what “driving safely” means. This article proposes a new characterization of safe driving behavior for automated vehicles based on models of “typical” human driving behavior. Such behavior (specially from attentive, experienced drivers) is known to lead to interactions of mid to low severity (i.e., low collision risk). Automated vehicles displaying similar behavior would interact with other traffic participants in a recognizable, predictable, and safe way. As a first step towards this characterization …

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