GPU implementation of the Frenet Path Planner for embedded autonomous systems: A case study in the F1tenth scenario

Autonomous vehicles are increasingly utilized in safety-critical and time-sensitive settings like urban environments and competitive racing. Planning maneuvers ahead is pivotal in these scenarios, where the onboard compute platform determines the vehicle's future actions. This paper introduces an optimized implementation of the Frenet Path Planner, a renowned path planning algorithm, accelerated through GPU processing. Unlike existing methods, our approach expedites the entire algorithm, encompassing path generation and collision avoidance. We gauge the execution time of our implementation, showcasing significant enhancements over the CPU baseline (up to 22x of speedup). Furthermore, we assess the influence of different precision types (double, float, half) on trajectory accuracy, probing the balance between completion speed and computational precision. Moreover, we analyzed the impact on the execution time caused by the use of Nvidia Unified Memory and by the interference caused by other processes running on the same system. We also evaluate our implementation using the F1tenth simulator and in a real race scenario. The results position our implementation as a strong candidate for the new state-of-the-art implementation for the Frenet Path Planner algorithm

Optimized Local Path Planner Implementation for GPU-Accelerated Embedded Systems

Autonomous vehicles are latency-sensitive systems. The planning phase is a critical component of such systems, during which the in-vehicle compute platform is responsible for determining the future maneuvers that the vehicle will follow. In this paper, we present a GPU-accelerated optimized implementation of the Frenet Path Planner, a widely known path planning algorithm. Unlike the current state-of-the-art, our implementation accelerates the entire algorithm, including the path generation and collision avoidance phases. We measure the execution time of our implementation and demonstrate dramatic speedups compared to the CPU baseline implementation. Additionally, we evaluate the impact of different precision types (double, float, half) on trajectory errors to investigate the tradeoff between completion latencies and computation precision

Executive functions in children with specific learning disorders: Shedding light on a complex profile through teleassessment

Executive Functions (EFs) are high-order cognitive processes relevant to learning and adaptation and frequently impaired in children with specific learning disorders (SLDs). This study aimed to investigate EFs in children with SLD and explore the role of specific EF-related subprocesses, such as stimuli processing and processing speed. Fifty-seven SLD and 114 typically developing (TD) children, matched for gender and age, completed four tasks measuring response inhibition, interference control, shifting, and updating on a web-based teleassessment platform. The results show that SLD children performed lower in all EF tasks than TD children, regardless of stimulus type and condition. Mediation analyses suggested that differences between the SLD and TD groups are mediated by EF-related subprocesses, offering an interpretative model of EF deficits in children with SLD

A Hypomorphic Lsd1 Allele Results in Heart Development Defects in Mice

Article Authors Metrics Comments Related Content Abstract Introduction Results Discussion Materials and Methods Supporting Information Acknowledgments Author Contributions References Reader Comments (0) Media Coverage (0) Figures Abstract Lysine-specific demethylase 1 (Lsd1/Aof2/Kdm1a), the first enzyme with specific lysine demethylase activity to be described, demethylates histone and non-histone proteins and is essential for mouse embryogenesis. Lsd1 interacts with numerous proteins through several different domains, most notably the tower domain, an extended helical structure that protrudes from the core of the protein. While there is evidence that Lsd1-interacting proteins regulate the activity and specificity of Lsd1, the significance and roles of such interactions in developmental processes remain largely unknown. Here we describe a hypomorphic Lsd1 allele that contains two point mutations in the tower domain, resulting in a protein with reduced interaction with known binding partners and decreased enzymatic activity. Mice homozygous for this allele die perinatally due to heart defects, with the majority of animals suffering from ventricular septal defects. Molecular analyses revealed hyperphosphorylation of E-cadherin in the hearts of mutant animals. These results identify a previously unknown role for Lsd1 in heart development, perhaps partly through the control of E-cadherin phosphorylation

The RSPO–LGR4/5–ZNRF3/RNF43 module controls liver zonation and size

LGR4/5 receptors and their cognate RSPO ligands potentiate Wnt/β-catenin signalling and promote proliferation and tissue homeostasis in epithelial stem cell compartments. In the liver, metabolic zonation requires a Wnt/β-catenin signalling gradient, but the instructive mechanism controlling its spatiotemporal regulation is not known. We have now identified the RSPO-LGR4/5-ZNRF3/RNF43 module as a master regulator of Wnt/β-catenin-mediated metabolic liver zonation. Liver-specific LGR4/5 loss of function (LOF) or RSPO blockade disrupted hepatic Wnt/β-catenin signalling and zonation. Conversely, pathway activation in ZNRF3/RNF43 LOF mice or with recombinant RSPO1 protein expanded the hepatic Wnt/β-catenin signalling gradient in a reversible and LGR4/5-dependent manner. Recombinant RSPO1 protein increased liver size and improved liver regeneration, whereas LGR4/5 LOF caused the opposite effects, resulting in hypoplastic livers. Furthermore, we show that LGR4(+) hepatocytes throughout the lobule contribute to liver homeostasis without zonal dominance. Taken together, our results indicate that the RSPO-LGR4/5-ZNRF3/RNF43 module controls metabolic liver zonation and is a hepatic growth/size rheostat during development, homeostasis and regeneration

A SVM-based behavior monitoring algorithm towards detection of un-desired events in critical infrastructures

In this paper, we report our recent research activities under MICIE, a European project funded under Framework-7 Programme, in which a SVM-based behavior modeling and learning algorithm is described. The proposed algorithm further exploits the adapted learning capability in SVM by using statistics analysis and K-S test verification to introduce an automated parameter control mechanism, and hence the SVM learning and detection can be made adaptive to the statistics of the input data. Experiments on telecommunication network data sets support that the proposed algorithm is able to detect undesired events effectively, presenting a good potential for development of computer-aided monitoring software tools for protection of critical infrastructures. © Springer-Verlag Berlin Heidelberg 2009

A SVM-based behavior monitoring algorithm towards detection of un-desired events in critical infrastructures

In this paper, we report our recent research activities under MICIE, a European project funded under Framework-7 Programme, in which a SVM-based behavior modeling and learning algorithm is described. The proposed algorithm further exploits the adapted learning capability in SVM by using statistics analysis and K-S test verification to introduce an automated parameter control mechanism, and hence the SVM learning and detection can be made adaptive to the statistics of the input data. Experiments on telecommunication network data sets support that the proposed algorithm is able to detect undesired events effectively, presenting a good potential for development of computer-aided monitoring software tools for protection of critical infrastructures. © Springer-Verlag Berlin Heidelberg 2009

Ambient Assisted Living for Elderly People using Smart Personal Assistants

The demand of innovative solutions to afford healthy and safe lifestyle, for elderly people, are increasing lead by the growth of the ageing population and age-related diseases. Moreover, the availability of smart devices based on IoT technologies for personal environments has evolved the demand for tools to monitor and remotely control the home equipment. A new generation of remote control is available: Electronic Personal Assistants. These devices could be controlled using natural user interfaces, help user to simplify the interaction with home automation and other smart devices. The personal assistants use machine learning strategies to interact with the user. The solutions available in the market are often expensive and, moreover, are limited and legacy since based on proprietary hardware and software. This paper aims to present Alfred, a flexible and modular smart personal assistant based on low-cost hardware and open source machine learning software, in the context of the City4Age, an H2020 Project, to provide Elderly-friendly ambient assisted living