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Surrogate Safety Measures (SSMs) are used to express road safety in terms of the safety risk in traffic conflicts. Typically, SSMs rely on assumptions regarding the future evolution of traffic participant trajectories to generate a measure of risk, restricting their applicability to scenarios where these assumptions are valid. In response to this limitation, we present the novel Probabilistic RISk Measure derivAtion (PRISMA) method. The objective of the PRISMA method is to derive SSMs that can be used to calculate in real time the probability of a specific event (e.g., a crash). The PRISMA method adopts a data-driven approach to predict the possible future traffic participant trajectories, thereby reducing the reliance on specific assumptions regarding these trajectories. Since the PRISMA is not bound to specific assumptions, the PRISMA method offers the ability to derive multiple SSMs for various scenarios. The occurrence probability of the specified event is based on simulations and combined with a regression model, this enables our derived SSMs to make real-time risk estimations. To illustrate the PRISMA method, an SSM is derived for risk evaluation during longitudinal traffic interactions. Since there is no known method to objectively estimate risk from first principles, i.e., there is no known risk ground truth, it is very difficult, if not impossible, to objectively compare the relative merits of two SSMs. Instead, we provide a method for benchmarking our derived SSM with respect to expected risk trends. The application of the benchmarking illustrates that the SSM matches the expected risk trends. Whereas the derived SSM shows the potential of the PRISMA method, future work involves applying the approach for other types of traffic conflicts, such as lateral traffic conflicts or interactions with vulnerable road users.

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 without relying 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 Representativeness (SR) metric based on the Wasserstein distance, which quantifies to what extent the scenarios with the generated parameter values are representative of real-world scenarios while covering the actual variety found in the real-world scenarios. A comparison of our proposed method with methods relying on assumptions of the scenario parametrization and pdf estimation shows that the proposed method can automatically determine the optimal scenario parametrization and pdf estimation. Furthermore, we demonstrate that our SR metric can be used to choose the (number of) parameters that best describe a scenario. The presented method is promising, because the parameterization and pdf estimation can directly be applied to already available importance sampling strategies for accelerating the evaluation of AVs.

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, therefore, it can be applied to a large variety of scenarios. We provide two examples to illustrate the method. This paper is concluded with some promising future possibilities for our approach, such as automatic generation of scenarios for the assessment of automated vehicles.

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 the need of evaluating the actual pdf, which makes it suitable for drawing random samples for, e.g., Monte Carlo methods. Sampling from a KDE while the samples satisfy a linear equality constraint, however, has not been described in the literature, as far as the authors know. In this paper, we propose a method to sample from a pdf estimated using KDE, such that the samples satisfy a linear equality constraint. We also present an algorithm of our method in pseudo-code. The method can be used to generating scenarios that have, e.g., a predetermined starting speed or to generate different types of scenarios. This paper also shows that the method for sampling scenarios can be used in case a Singular Value Decomposition (SVD) is used to reduce the dimension of the parameter vectors.

In this paper we introduce a new radiative transfer code SPRAI (Simplex Photon Radiation in the Arepo Implementation) based on the SimpleX radiation transfer method. This method, originally used only for post-processing, is now directly integrated into the Arepo code and takes advantage of its adaptive unstructured mesh. Radiated photons are transferred from the sources through the series of Voronoi gas cells within a specific solid angle. From the photon attenuation we derive corresponding photon fluxes and ionization rates and feed them to a primordial chemistry module. This gives us a self-consistent method for studying dynamical and chemical processes caused by ionizing sources in primordial gas. Since the computational cost of the SimpleX method does not scale directly with the number of sources, it is convenient for studying systems such as primordial star-forming halos that may form multiple ionizing sources.

The diffuse soft X-ray emissivity from galactic winds is computed during the Epoch of Reionization (EoR). We consider two analytic models, a pressure-driven wind and a superbubble model, and a 3D cosmological simulation including gas dynamics from the First Billion Years (FiBY) project. The analytic models are normalized to match the diffuse X-ray emissivity of star-forming galaxies in the nearby Universe. The cosmological simulation uses physically motivated star formation and wind prescriptions, and includes radiative transfer corrections. The models and the simulation all are found to produce sufficient heating of the Intergalactic Medium to be detectable by current and planned radio facilities through 21 cm measurements during the EoR. While the analytic models predict a 21 cm emission signal relative to the Cosmic Microwave Background sets in by \\(z_{\\rm trans} \\simeq 8 - 10\\), the predicted signal in the FiBY simulation remains in absorption until reionization completes. The 21 cm absorption differential brightness temperature reaches a minimum of \\(\\Delta T \\simeq -130\\) to \\(-200\\) mK, depending on model. Allowing for additional heat from high mass X-ray binaries pushes the transition to emission to \\(z_{\\rm trans} \\simeq 10 - 12\\), with shallower absorption signatures having a minimum of \\(\\Delta T \\simeq -110\\) to \\(-140\\) mK. The 21 cm signal may be a means of distinguishing between the wind models, with the superbubble model favouring earlier reheating. While an early transition to emission may indicate X-ray binaries dominate the reheating, a transition to emission as early as \\(z_{\\rm trans} > 12\\) would suggest the presence of additional heat sources.

We explore scenarios for reionizing the intergalactic medium with low galaxy ionizing photon escape fractions. We combine simulation-based halo-mass dependent escape fractions with an extrapolation of the observed galaxy rest-ultraviolet luminosity functions to solve for the reionization history from z=20 to z=4. We explore the posterior distributions for key unknown quantities, including the limiting halo mass for star-formation, the ionizing photon production efficiency, and a potential contribution from active galactic nuclei (AGN). We marginalize over the allowable parameter space using a Markov Chain Monte Carlo method, finding a solution which satisfies the most model-independent constraints on reionization. Our fiducial model can match observational constraints with an average escape fraction of <5% throughout the bulk of the epoch of reionization if: i) galaxies form stars down to the atomic cooling limit before reionization and a photosuppression mass of log(M_h/Msol)~9 during/after reionization (-13-15) dominate the ionizing emissivity, leading to an earlier start to reionization and a smoother evolution of the ionized volume filling fraction than models which assume a single escape fraction at all redshifts and luminosities. The ionizing emissivity from this model is consistent with observations at z=4-5 (and below, when extrapolated), in contrast to some models which assume a single escape fraction. Our predicted ionized volume filling fraction at z=7 of Q_HII=78% (+\\- 8%) is in ~1-2 sigma tension with observations of Lya emitters at z~7 and the damping wing analyses of the two known z>7 quasars, which prefer Q_HII,z=7~40-50%.

We investigate the environment in which direct-collapse black holes may form by analysing a cosmological, hydrodynamical simulation that is part of the First Billion Years project. This simulation includes the most relevant physical processes leading to direct collapse of haloes, most importantly, molecular hydrogen depletion by dissociation of \\(H_2\\) and \\(H^-\\) from the evolving Lyman-Werner radiation field. We selected a sample of pristine atomic cooling haloes that have never formed stars in their past, have not been polluted with heavy elements and are cooling predominantly via atomic hydrogen lines. Amongst them we identified six haloes that could potentially harbour massive seed black holes formed via direct collapse (with masses in the range of \\(10^{4-6} M_{sun}\\)). These potential hosts of direct-collapse black holes form as satellites and are found within 15 physical kpc of proto-galaxies, with stellar masses in the range \\(10^{5-7} M_{sun}\\) and maximal star formation rates of 0.1 Msun/yr over the past 5 Myr, and are exposed to the highest flux of Lyman-Werner radiation emitted from the neighbouring galaxies. It is the proximity to these proto-galaxies that differentiates these haloes from rest of the sample.

Direct collapse black holes (DCBHs) formed from the collapse of atomically-cooled primordial gas in the early Universe are strong candidates for the seeds of supermassive BHs. DCBHs are thought to form in atomic cooling haloes in the presence of a strong molecule-dissociating, Lyman-Werner (LW) radiation field. Given that star forming galaxies are likely to be the source of the LW radiation in this scenario, ionizing radiation from these galaxies may accompany the LW radiation. We present cosmological simulations resolving the collapse of primordial gas into an atomic cooling halo, including the effects of both LW and ionizing radiation. We find that in cases where the gas is not self-shielded from the ionizing radiation, the collapse can be delayed by ~ 25 Myr. When the ionized gas does collapse, the free electrons that are present catalyze H2 formation. In turn, H2 cooling becomes efficient in the center of the halo, and DCBH formation is prevented. We emphasize, however, that in many cases the gas collapsing into atomic cooling haloes at high redshift is self-shielding to ionizing radiation. Therefore, it is only in a fraction of such haloes in which DCBH formation is prevented due to reionization.

Proto-galaxies forming in low-mass dark matter haloes are thought to provide the majority of ionizing photons needed to reionize the Universe, due to their high escape fractions of ionizing photons. We study how the escape fraction in high-redshift galaxies relates to the physical properties of the halo in which the galaxies form, by computing escape fractions in more than 75000 haloes between redshifts 27 and 6 that were extracted from the First Billion Years project, high-resolution cosmological hydrodynamics simulations of galaxy formation. We find that the main constraint on the escape fraction is the gas column density in a radius of 10 pc around the stellar populations, causing a strong mass dependence of the escape fraction. The lower potential well in haloes with virial mass below 1e8 solar mass results in low column densities that can be penetrated by radiation from young stars (age < 5 Myr). In haloes with virial mass above 1e8 solar mass supernova feedback is important, but only 30% of the haloes in this mass range have an escape fraction higher than 1%. We find a large range of escape fractions in haloes with similar properties, caused by different distributions of the dense gas in the halo. This makes it very hard to predict the escape fraction on the basis of halo properties and results in a highly anisotropic escape fraction. The strong mass dependence, the large spread and the large anisotropy of the escape fraction may strongly affect the topology of reionization and is something current models of cosmic reionization should strive to take into account.