A comprehensive review of embedded systems in autonomous vehicles: Trends, challenges, and future directions

World Journal of Advanced Research and Reviews, Год журнала: 2024, Номер 21(1), С. 2009 - 2020

Опубликована: Янв. 25, 2024

The integration of embedded systems in autonomous vehicles represents a transformative paradigm shift the automotive industry, offering unprecedented opportunities for enhanced safety, efficiency, and user experience. This comprehensive review explores current landscape vehicles, delving into emerging trends, persistent challenges, future directions that shape trajectory this rapidly evolving field. begins by examining foundational concepts context elucidating intricate interplay between hardware software components. It surveys state-of-the-art technologies empower these systems, including advanced sensors, actuators, communication protocols, highlighting their pivotal roles perception, decision-making, control aspects driving. One prominent trends discussed is increasing reliance on artificial intelligence (AI) machine learning algorithms within systems. incorporation intelligent enables to adapt learn from real-world scenarios, enhancing ability navigate diverse dynamic environments. Additionally, sheds light growing emphasis connectivity edge computing, illustrating how leverage facilitate seamless surrounding infrastructure. Despite promising advancements, critically examines challenges impede widespread adoption vehicles. Issues such as safety concerns, cybersecurity threats, regulatory frameworks are analyzed, providing insights complex ecosystem which operate. In addressing envisions marked continuous innovation collaboration across industries. anticipates evolution towards more robust, adaptive, fault-tolerant architectures, paving way increased autonomy deployment provides holistic understanding encapsulating directions. As undergoes shift, serves valuable resource researchers, practitioners, policymakers seeking terrain vehicle technology.

Язык: Английский

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Delocalized, asynchronous, closed-loop discovery of organic laser emitters

Science, Год журнала: 2024, Номер 384(6697)

Опубликована: Май 16, 2024

Contemporary materials discovery requires intricate sequences of synthesis, formulation, and characterization that often span multiple locations with specialized expertise or instrumentation. To accelerate these workflows, we present a cloud-based strategy enabled delocalized asynchronous design-make-test-analyze cycles. We showcased this approach through the exploration molecular gain for organic solid-state lasers as frontier application in optoelectronics. Distributed robotic synthesis in-line property characterization, orchestrated by artificial intelligence experiment planner, resulted 21 new state-of-the-art materials. Gram-scale ultimately allowed verification best-in-class stimulated emission thin-film device. Demonstrating integration five laboratories across globe, workflow provides blueprint delocalizing-and democratizing-scientific discovery.

Язык: Английский

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A dynamic knowledge graph approach to distributed self-driving laboratories

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Янв. 23, 2024

Abstract The ability to integrate resources and share knowledge across organisations empowers scientists expedite the scientific discovery process. This is especially crucial in addressing emerging global challenges that require solutions. In this work, we develop an architecture for distributed self-driving laboratories within World Avatar project, which seeks create all-encompassing digital twin based on a dynamic graph. We employ ontologies capture data material flows design-make-test-analyse cycles, utilising autonomous agents as executable components carry out experimentation workflow. Data provenance recorded ensure its findability, accessibility, interoperability, reusability. demonstrate practical application of our framework by linking two robots Cambridge Singapore collaborative closed-loop optimisation pharmaceutically-relevant aldol condensation reaction real-time. graph autonomously evolves toward scientist’s research goals, with effectively generating Pareto front cost-yield three days.

Язык: Английский

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Superlative mechanical energy absorbing efficiency discovered through self-driving lab-human partnership

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Май 21, 2024

Abstract Energy absorbing efficiency is a key determinant of structure’s ability to provide mechanical protection and defined by the amount energy that can be absorbed prior stresses increasing level damages system protected. Here, we explore additively manufactured polymer structures using self-driving lab (SDL) perform >25,000 physical experiments on generalized cylindrical shells. We use human-SDL collaborative approach where are selected from over trillions candidates in an 11-dimensional parameter space Bayesian optimization then automatically performed while human team monitors progress periodically modify aspects system. The result this campaign discovery structure with 75.2% library experimental data reveals transferable principles for designing tough structures.

Язык: Английский

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Navigating phase diagram complexity to guide robotic inorganic materials synthesis

Nature Synthesis, Год журнала: 2024, Номер 3(5), С. 606 - 614

Опубликована: Апрель 9, 2024

Abstract Efficient synthesis recipes are needed to streamline the manufacturing of complex materials and accelerate realization theoretically predicted materials. Often, solid-state multicomponent oxides is impeded by undesired by-product phases, which can kinetically trap reactions in an incomplete non-equilibrium state. Here we report a thermodynamic strategy navigate high-dimensional phase diagrams search precursors that circumvent low-energy, competing by-products, while maximizing reaction energy drive fast transformation kinetics. Using robotic inorganic laboratory, perform large-scale experimental validation our precursor selection principles. For set 35 target quaternary oxides, with chemistries representative intercalation battery cathodes electrolytes, robot performs 224 spanning 27 elements 28 unique precursors, operated 1 human experimentalist. Our frequently yield higher purity than traditional precursors. Robotic laboratories offer exciting platform for data-driven science, from develop fundamental insights guide both chemists.

Язык: Английский

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