The 1.17-day orbit of the double-degenerate (DA+DQ) NLTT 16249

New spectroscopic observations show that the double degenerate system NLTT 16249 is in a close orbit (a = 5.6+/-0.3 R_sun) with a period of 1.17 d. The total mass of the system is estimated between 1.47 and 2.04 M_sun but it is not expected to merge within a Hubble time-scale (t_merge ~ 10^11 yr). Vennes & Kawka (2012, ApJ, 745, L12) originally identified the system because of the peculiar composite hydrogen (DA class) and molecular (C_2--DQ class--and CN) spectra and the new observations establish this system as the first DA plus DQ close double degenerate. Also, the DQ component was the first of its class to show nitrogen dredged-up from the core in its atmosphere. The star may be viewed as the first known DQ descendant of the born-again PG1159 stars. Alternatively, the presence of nitrogen may be the result of past interactions and truncated evolution in a close binary system.Comment: published in ApJ Letter

Benchmarking Image Sensors Under Adverse Weather Conditions for Autonomous Driving

Adverse weather conditions are very challenging for autonomous driving because most of the state-of-the-art sensors stop working reliably under these conditions. In order to develop robust sensors and algorithms, tests with current sensors in defined weather conditions are crucial for determining the impact of bad weather for each sensor. This work describes a testing and evaluation methodology that helps to benchmark novel sensor technologies and compare them to state-of-the-art sensors. As an example, gated imaging is compared to standard imaging under foggy conditions. It is shown that gated imaging outperforms state-of-the-art standard passive imaging due to time-synchronized active illumination

A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?

Autonomous driving at level five does not only means self-driving in the sunshine. Adverse weather is especially critical because fog, rain, and snow degrade the perception of the environment. In this work, current state of the art light detection and ranging (lidar) sensors are tested in controlled conditions in a fog chamber. We present current problems and disturbance patterns for four different state of the art lidar systems. Moreover, we investigate how tuning internal parameters can improve their performance in bad weather situations. This is of great importance because most state of the art detection algorithms are based on undisturbed lidar data

Fast Normal Approximation of Point Clouds in Screen Space

Displaying large point clouds of mainly planar point distributions yet comes with large restrictions regarding the surface normal and surface reconstruction. Point data needs to be clustered or traversed to extract a local neighborhood which is necessary to retrieve surface information. We propose using the rendering pipeline to circumvent a pre-computation of the neighborhood in world space to perform a fast approximation of the surface in screen space. We present and compare three different methods for surface reconstruction within a post-process. These methods range from simple approximations to the definition of a tensor surface. All these methods are designed to run at interactive frame-rates. We also present a correction method to increase reconstruction quality, while preserving interactive frame-rates. Our results indicate, that the on-the-fly computation of surface normals is not a limiting factor on modern GPUs. As the surface information is generated during the post-process, only the target display size is the limiting factor. The performance is independent of the point cloud’s size

Using Machine Learning to Detect Ghost Images in Automotive Radar

Radar sensors are an important part of driver assistance systems and intelligent vehicles due to their robustness against all kinds of adverse conditions, e.g., fog, snow, rain, or even direct sunlight. This robustness is achieved by a substantially larger wavelength compared to light-based sensors such as cameras or lidars. As a side effect, many surfaces act like mirrors at this wavelength, resulting in unwanted ghost detections. In this article, we present a novel approach to detect these ghost objects by applying data-driven machine learning algorithms. For this purpose, we use a large-scale automotive data set with annotated ghost objects. We show that we can use a state-of-the-art automotive radar classifier in order to detect ghost objects alongside real objects. Furthermore, we are able to reduce the amount of false positive detections caused by ghost images in some settings