Modules and techniques for motion planning: An industrial perspective

Research on autonomous cars has become one of the main research paths in the automotive industry, with many critical issues that remain to be explored while considering the overall methodology and its practical applicability. In this paper, we present an industrial experience in which we build a complete autonomous driving system, from the sensor units to the car control equipment, and we describe its adoption and testing phase on the field. We report how we organize data fusion and map manipulation to represent the required reality. We focus on the communication and synchronization issues between the data-fusion device and the path-planner, between the CPU and the GPU units, and among different CUDA kernels implementing the core local planner module. In these frameworks, we propose simple representation strategies and approximation techniques which guarantee almost no penalty in terms of accuracy and large savings in terms of memory occupation and memory transfer times. We show how we adopt a recent implementation on parallel many-core devices, such as CUDA-based GPGPU, to reduce the computational burden of rapidly exploring random trees to explore the state space along with a given reference path. We report on our use of the controller and the vehicle simulator. We run experiments on several real scenarios, and we report the paths generated with the different settings, with their relative errors and computation times. We prove that our approach can generate reasonable paths on a multitude of standard maneuvers in real time

Smart techniques for flying-probe testing

In the production of printed circuit boards, in-circuit tests verify whether the electric and electronic components of the board have been correctly soldered. When the test is performed using flying-probes, several probes are simultaneously moved on the board to reach and touch multiple test points. Taking into consideration the layout of the board, the characteristics of the tester, and several other physical constraints, not all movements of the probes are mutually compatible nor they can always be performed through simple straight lines. As the cost of the test is mainly related to its length, and patching the path of one probe may create new incompatibilities with the trajectory of the other probes, one should carefully trade off the time required to find the trajectories with the time required by the probes to follow them. In this paper, we model the movements of our flying probes as a multiple and collaborative planning problem. We describe an approach for detecting invalid movements and we design a strategy to correct them with the addition of new intermediate points in the trajectory. We report the entire high-level procedure and we explore the optimizations performed in the more expensive and complex steps. We also present parallel implementations of our algorithms, either relying on multi-core CPU devices or many-cores GPU platforms, when these units may be useful to achieve greater speedups. Experimental results show the effectiveness of the proposed solution in terms of elapsed computation times

Partitioning Interpolant-Based Verificationfor effective Unbounded Model Checking

Interpolant-based model checking has been shown to be effective on large verification instances, as it efficiently combines automated abstraction and reachability fixed-point checks. On the other hand, methods based on variable quantification have proved their ability to remove free inputs, thus projecting the search space over state variables. In this paper we propose an integrated approach which combines the abstraction power of interpolation with techniques that rely on AIG and/or BDD representations of states, directly supporting variable quantification and fixed-point checks. The underlying idea of this combination is to adopt AIG- or BDD-based quantifications to limit and restrict the search space and the complexity of the interpolant-based approach. The exploited strategies, most of which are individually well-known, are integrated with a new flavor, specifically designed to improve their effectiveness on difficult verification instances. Experimental results, specifically oriented to hard-to-solve verification problems, show the robustness of our approach

Cost-aware design and simulation of electrical energy systems

One fundamental dimension in the design of an electrical energy system (EES) is the economic analysis of the possible design alternatives, in order to ensure not just the maximization of the energy output but also the return on the investment and the possible profits. Since the energy output and the economic figures of merit are intertwined, for an accurate analysis it is necessary to analyze these two aspects of the problem concurrently, in order to define effective energy management policies. This paper achieves that objective by tracking and measuring the energy efficiency and the cost effectiveness in a single modular framework. The two aspects are modeled separately, through the definition of dedicated simulation layers governed by dedicated virtual buses that elaborate and manage the information and energy flows. Both layers are simulated concurrently within the same simulation infrastructure based on SystemC-AMS, so as to recreate at runtime the mutual influence of the two aspects, while allowing the use of different discrete time scales for the two layers. Thanks to the tight coupling provided by the single simulation engine, our method enables a quick estimation of various cost metrics (net costs, annualized costs, and profits) of any configuration of EES under design, via an informed exploration of the alternatives. To prove the effectiveness of this approach, we apply the proposed strategy to two EES case studies, we explored various management strategies and the presence of different types and numbers of power sources and energy storage devices in the EES. The analysis proved to allow the identification of the optimal profitable solutions, thereby improving the standard design and simulation flow of EES

Effect of Hepatitis E Virus RNA Universal Blood Donor Screening, Catalonia, Spain, 2017-2020

Altres ajuts: Banc de Sang i Teixits.Hepatitis E virus (HEV) is the major cause of acute viral hepatitis in several countries in Europe. HEV is acquired mainly by consumption of contaminated pork but can also be transmitted through blood transfusion. HEV infection is usually self-limited but can become persistent in immunocompromised persons. During the fi rst 30 months of HEV RNA universal screening of blood donations in Catalonia, Spain, we identifi ed 151 HEV RNA-positive donations (1/4,341 blood donations). Most infected donors reported consumption of pates and sausages, and 58% were negative for HEV IgM and IgG. All HEV isolates belonged to genotype 3. All infected donors spontaneously resolved the infection, and no neurologic symptoms and reinfections were observed after 1 year of follow-up. Since the implementation of HEV RNA universal screening, no new cases of transfusion-transmitted HEV infection were reported. Our data indicate HEV screening of blood donations provides safer blood for all recipients, especially for immunosuppressed persons

Does the Red Queen reign in the kingdom of digital organisms?

In competition experiments between two RNA viruses of equal or almost equal fitness, often both strains gain in fitness before one eventually excludes the other. This observation has been linked to the Red Queen effect, which describes a situation in which organisms have to constantly adapt just to keep their status quo. I carried out experiments with digital organisms (self-replicating computer programs) in order to clarify how the competing strains' location in fitness space influences the Red-Queen effect. I found that gains in fitness during competition were prevalent for organisms that were taken from the base of a fitness peak, but absent or rare for organisms that were taken from the top of a peak or from a considerable distance away from the nearest peak. In the latter two cases, either neutral drift and loss of the fittest mutants or the waiting time to the first beneficial mutation were more important factors. Moreover, I found that the Red-Queen dynamic in general led to faster exclusion than the other two mechanisms.Comment: 10 pages, 5 eps figure

Weak convergence of finite element approximations of linear stochastic evolution equations with additive noise II. Fully discrete schemes

We present an abstract framework for analyzing the weak error of fully discrete approximation schemes for linear evolution equations driven by additive Gaussian noise. First, an abstract representation formula is derived for sufficiently smooth test functions. The formula is then applied to the wave equation, where the spatial approximation is done via the standard continuous finite element method and the time discretization via an I-stable rational approximation to the exponential function. It is found that the rate of weak convergence is twice that of strong convergence. Furthermore, in contrast to the parabolic case, higher order schemes in time, such as the Crank-Nicolson scheme, are worthwhile to use if the solution is not very regular. Finally we apply the theory to parabolic equations and detail a weak error estimate for the linearized Cahn-Hilliard-Cook equation as well as comment on the stochastic heat equation