A RoundD-like Roundabout Scenario in CARLA Simulator

Evaluating the challenges and opportunities of cooperative autonomous vehicles (CAV) require an adapted simulation methodology reproducing realistic driving and sensory contexts. In this paper, we propose a RounD-like CARLA scenario reproducing in CARLA the driving context recorded in the RounD dataset. We focus in particular on roundabout scenarios, as they are considered particularly challenging for CAV. We present the methodology followed to generate the CARLA scenario and describe challenges to reproduce trajectories corresponding to RounD. Origin and destination of vehicles, waypoint and speed are extracted from RounD for CARLA vehicles to closely reproduce the driving patterns observed in RounD. The benefit of such scenario are manyfold, such as evaluating control algorithms of CAVs, deep AI reinforcement learning, or vehicular sensor data sampling under realistic driving contexts. It notably will reduce the gap of AI mechanisms for CAV between simulation scenarios and realistic conditions

Forces on Dust Grains Exposed to Anisotropic Interstellar Radiation Fields

Grains exposed to anisotropic radiation fields are subjected to forces due to the asymmetric photon-stimulated ejection of particles. These forces act in addition to the ``radiation pressure'' due to absorption and scattering. Here we model the forces due to photoelectron emission and the photodesorption of adatoms. The ``photoelectric'' force depends on the ambient conditions relevant to grain charging. We find that it is comparable to the radiation pressure when the grain potential is relatively low and the radiation spectrum is relatively hard. The calculation of the ``photodesorption'' force is highly uncertain, since the surface physics and chemsitry of grain materials are poorly understood at present. For our simple yet plausible model, the photodesorption force dominates the radiation pressure for grains with size >~0.1 micron exposed to starlight from OB stars. We find that the anisotropy of the interstellar radiation field is ~10% in the visible and ultraviolet. We estimate size-dependent drift speeds for grains in the cold and warm neutral media and find that micron-sized grains could potentially be moved across a diffuse cloud during its lifetime.Comment: LaTeX(41 pages, 19 figures), submitted to Ap

Are dust shell models well-suited to explain interferometric data of late-type stars in the near-infrared?

Recently available near-infrared interferometric data on late-type stars show a strong increase of diameter for asymptotic giant branch (AGB) stars between the K (2.0 - 2.4 \mu m) and L (3.4 - 4.1 \mu m) bands. Aiming at an explanation of these findings, we chose the objects \alpha Orionis (Betelgeuse), SW Virginis, and R Leonis, which are of different spectral types and stages of evolution, and which are surrounded by circumstellar envelopes with different optical thicknesses. For these stars, we compared observations with spherically symmetric dust shell models. Photometric and 11 \mu m interferometric data were also taken into account to further constrain the models. -- [...] -- We conclude that AGB models comprising a photosphere and dust shell, although consistent with SED data and also interferometric data in K and at 11 \mu m, cannot explain the visibility data in L; an additional source of model opacity, possibly related to a gas component, is needed in L to be consistent with the visibility data.Comment: 12 pages, 4 figures, to be published in A&A; Latex aa class, uses packages graphicx, hyperref, natbib, and txfonts; keywords: Techniques: interferometric -- Radiative transfer -- Circumstellar matter -- Infrared: stars -- Stars: late-type -- Stars: individual: \alpha Orionis, SW Virginis, R Leoni

Accurate parameter estimation for star formation history in galaxies using SDSS spectra

To further our knowledge of the complex physical process of galaxy formation, it is essential that we characterize the formation and evolution of large databases of galaxies. The spectral synthesis STARLIGHT code of Cid Fernandes et al. (2004) was designed for this purpose. Results of STARLIGHT are highly dependent on the choice of input basis of simple stellar population (SSP) spectra. Speed of the code, which uses random walks through the parameter space, scales as the square of the number of basis spectra, making it computationally necessary to choose a small number of SSPs that are coarsely sampled in age and metallicity. In this paper, we develop methods based on diffusion map (Lafon & Lee, 2006) that, for the first time, choose appropriate bases of prototype SSP spectra from a large set of SSP spectra designed to approximate the continuous grid of age and metallicity of SSPs of which galaxies are truly composed. We show that our techniques achieve better accuracy of physical parameter estimation for simulated galaxies. Specifically, we show that our methods significantly decrease the age-metallicity degeneracy that is common in galaxy population synthesis methods. We analyze a sample of 3046 galaxies in SDSS DR6 and compare the parameter estimates obtained from different basis choices.Comment: Resubmitted to MNRAS; 16 pages, 15 figure

A RoundD-like Roundabout Scenario in CARLA Simulator

Evaluating the challenges and opportunities of cooperative autonomous vehicles (CAV) require an adapted simulation methodology reproducing realistic driving and sensory contexts. In this paper, we propose a RounD-like CARLA scenario reproducing in CARLA the driving context recorded in the RounD dataset. We focus in particular on roundabout scenarios, as they are considered particularly challenging for CAV. We present the methodology followed to generate the CARLA scenario and describe challenges to reproduce trajectories corresponding to RounD. Origin and destination of vehicles, waypoint and speed are extracted from RounD for CARLA vehicles to closely reproduce the driving patterns observed in RounD. The benefit of such scenario are manyfold, such as evaluating control algorithms of CAVs, deep AI reinforcement learning, or vehicular sensor data sampling under realistic driving contexts. It notably will reduce the gap of AI mechanisms for CAV between simulation scenarios and realistic conditions

A RoundD-like Roundabout Scenario in CARLA Simulator

Evaluating the challenges and opportunities of cooperative autonomous vehicles (CAV) require an adapted simulation methodology reproducing realistic driving and sensory contexts. In this paper, we propose a RounD-like CARLA scenario reproducing in CARLA the driving context recorded in the RounD dataset. We focus in particular on roundabout scenarios, as they are considered particularly challenging for CAV. We present the methodology followed to generate the CARLA scenario and describe challenges to reproduce trajectories corresponding to RounD. Origin and destination of vehicles, waypoint and speed are extracted from RounD for CARLA vehicles to closely reproduce the driving patterns observed in RounD. The benefit of such scenario are manyfold, such as evaluating control algorithms of CAVs, deep AI reinforcement learning, or vehicular sensor data sampling under realistic driving contexts. It notably will reduce the gap of AI mechanisms for CAV between simulation scenarios and realistic conditions