A Flexible Modeling Approach for Robust Multi-Lane Road Estimation

A robust estimation of road course and traffic lanes is an essential part of environment perception for next generations of Advanced Driver Assistance Systems and development of self-driving vehicles. In this paper, a flexible method for modeling multiple lanes in a vehicle in real time is presented. Information about traffic lanes, derived by cameras and other environmental sensors, that is represented as features, serves as input for an iterative expectation-maximization method to estimate a lane model. The generic and modular concept of the approach allows to freely choose the mathematical functions for the geometrical description of lanes. In addition to the current measurement data, the previously estimated result as well as additional constraints to reflect parallelism and continuity of traffic lanes, are considered in the optimization process. As evaluation of the lane estimation method, its performance is showcased using cubic splines for the geometric representation of lanes in simulated scenarios and measurements recorded using a development vehicle. In a comparison to ground truth data, robustness and precision of the lanes estimated up to a distance of 120 m are demonstrated. As a part of the environmental modeling, the presented method can be utilized for longitudinal and lateral control of autonomous vehicles

Multi-Object Tracking with Interacting Vehicles and Road Map Information

In many applications, tracking of multiple objects is crucial for a perception of the current environment. Most of the present multi-object tracking algorithms assume that objects move independently regarding other dynamic objects as well as the static environment. Since in many traffic situations objects interact with each other and in addition there are restrictions due to drivable areas, the assumption of an independent object motion is not fulfilled. This paper proposes an approach adapting a multi-object tracking system to model interaction between vehicles, and the current road geometry. Therefore, the prediction step of a Labeled Multi-Bernoulli filter is extended to facilitate modeling interaction between objects using the Intelligent Driver Model. Furthermore, to consider road map information, an approximation of a highly precise road map is used. The results show that in scenarios where the assumption of a standard motion model is violated, the tracking system adapted with the proposed method achieves higher accuracy and robustness in its track estimations

Mapping the spatial variation of soil moisture at the large scale using GPR for pavement applications

The characterization of shallow soil moisture spatial variability at the large scale is a crucial issue in many research studies and fields of application ranging from agriculture and geology to civil and environmental engineering. In this framework, this work contributes to the research in the area of pavement engineering for preventing damages and planning effective management. High spatial variations of subsurface water content can lead to unexpected damage of the load-bearing layers; accordingly, both safety and operability of roads become lower, thereby affecting an increase in expected accidents. A pulsed ground-penetrating radar system with ground-coupled antennas, i.e., 600-MHz and 1600-MHz center frequencies of investigation, was used to collect data in a 16 m × 16 m study site in the Po Valley area in northern Italy. Two ground-penetrating radar techniques were employed to non-destructively retrieve the subsurface moisture spatial profile. The first technique is based on the evalu¬ation of the dielectric permittivity from the attenuation of signal amplitudes. Therefore, dielectrics were converted into moisture values using soil-specific coefficients from Topp’s relationship. Ground-penetrating-radar-derived values of soil moisture were then compared with measurements from eight capacitance probes. The second technique is based on the Rayleigh scattering of the signal from the Fresnel theory, wherein the shifts of the peaks of frequency spectra are assumed comprehensive indi¬cators for characterizing the spatial variability of moisture. Both ground-penetrating radar methods have shown great promise for mapping the spatial variability of soil moisture at the large scale

Determining the physical conditions of extremely young Class 0 circumbinary disk around VLA1623A

We present detailed analysis of high-resolution C18O (2-1), SO (88-77), CO (3-2) and DCO+ (3-2) data obtained by the Atacama Large Millimeter/sub-millimeter Array (ALMA) towards a Class 0 Keplerian circumbinary disk around VLA1623A, which represents one of the most complete analysis towards a Class 0 source. From the dendrogram analysis, we identified several accretion flows feeding the circumbinary disk in a highly anisotropic manner. Stream-like SO emission around the circumbinary disk reveals the complicated shocks caused by the interactions between the disk, accretion flows and outflows. A wall-like structure is discovered south of VLA1623B. The discovery of two outflow cavity walls at the same position traveling at different velocities suggests the two outflows from both VLA1623A and VLA1623B overlays on top of each other in the plane of sky. Our detailed flat and flared disk modeling shows that Cycle 2 C18O J = 2-1 data is inconsistent with the combined binary mass of 0.2 Msun as suggested by early Cycle 0 studies. The combined binary mass for VLA1623A should be modified to 0.3 ~ 0.5 Msun.Comment: 26 pages, 20 figures, accepted by ApJ 2020.2.2