Experimental Autonomous Road Vehicle with Logical Artificial Intelligence

Shadrin, S. S.; Varlamov, O. O.; Ivanov, A. M.

doi:10.1155/2017/2492765

Cited by 51 publications

(24 citation statements)

References 12 publications

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“…The processing time and detection distance imply restrictions on movement of autonomous vehicle. The vehicle should move at such speed so that to maintain safety of each road user [13,16].…”

Section: A Experimental Results Of Neural Network Architecturesmentioning

confidence: 99%

Applicability of Computer Vision Architectures and Their Influence on Traffic Safety of Autonomous Vehicles

Endachev*,

Vasin,

Shadrin

2019

IJEAT

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This article considers modern rapid architectures of detecting neural networks, structural peculiarities of each selected neural network architectures are analyzed. Experiment is carried out on the basis of potentially dangerous situation upon autonomous vehicle movement; in the selected experimental environment a set of architectures for computer vision system of autonomous vehicle is analyzed, and traffic safety of autonomous vehicle is estimated under various weather conditions; computing time required for application of additional control and analysis algorithms is evaluated. Experimental results are analyzed aiming at development of reasonable selection of neural network architectures for object recognition required for variability of support of autonomous vehicle traffic. Conclusion about applicability of the considered neural network architectures is made for conditions of certain project.

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Section: A Experimental Results Of Neural Network Architecturesmentioning

confidence: 99%

Applicability of Computer Vision Architectures and Their Influence on Traffic Safety of Autonomous Vehicles

Endachev*,

Vasin,

Shadrin

2019

IJEAT

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“…In recent years, with the rapid development of Artificial Intelligence (AI) technology [1,2], many intelligent algorithms, such as genetic algorithms, expert intelligence systems [3,4], neural network algorithms [5][6][7], and fuzzy logic algorithms [8,9] are widely used in the automation research of automobiles and aerial vehicle [10]. On the other side, the revolution of navigation technology also promoted the development of ship automation and ship intelligence technology, and the Maritime Autonomous Surface Ships (MASS) or the Unmanned Surface Vehicles (USV) have become a popular research topic [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

COLREGs-Compliant Unmanned Surface Vehicles Collision Avoidance Based on Multi-Objective Genetic Algorithm

Ning

Chen

et al. 2020

IEEE Access

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Autonomous ships or Unmanned Surface Vehicles (USV) collision avoidance and path planning problems among multi-vessels are investigated in this paper. Firstly, a modified fuzzy dynamic risk of collision model based on time and space collision risk index is proposed, which is much closer to real ship applications. Then, the fitness functions based on the risk of collision, navigational economy, International Regulations for Preventing Collisions at Sea 1972 (COLREGs) and collision avoidance timing are established respectively to ensure the rationality of ship collision avoidance decisions. Moreover, path planning with global search capability is realized by the multi-objective decision theory combined with a genetic algorithm. The practicability and rationality of the recommended trajectory are guaranteed. Meanwhile, the problem of the non-inferior solution can be addressed by adapting the weight method and the constraint method and the optimized solution of the decision-making system can be achieved finally. Simulation results are further presented to validate the effectiveness of the proposed path planning and collision avoidance methods.

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“…The creation of a logical type of AI is devoted to inferences, models of reduction (transformation of a complex solution into a simple solution), abduction (propose a hypothesis), induction (reasoning from particular to general), truth maintenance systems, and decisions support systems. The MIVAR (Multidimensional Informational Variable Adaptive Reality) approach allows creating all of AI types, but the most it is appropriate for the development of logical AI [1]- [3]. The introduction of AI into medicine establishes entirely new surroundings for both doctors and patients.…”

Section: Introductionmentioning

confidence: 99%

Logical, Philosophical and Ethical Aspects of AI in Medicine

Varlamov¹,

Chuvikov²,

Adamova³

et al. 2019

IJMLC

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The logical type of Artificial Intelligence is presented in the article. MIVAR (Multidimensional Informational Variable Adaptive Reality) technology is based on the gnoseological triplet concept "Thing-Property-Relationship." The unlimited number of MIVAR units fill the MIVAR space that makes it communicatory, discrete, and scalable. MIVAR information processing allows creating complicated algorithms for medicine. We have chosen the clinical model of pain in the heart for preparation ontology for doctors. Knowledge expert model MIVAR WiMi "Chest Pain" is shown. The logical Artificial Intelligence (expert model) can help doctors fast, precisely and in the best way in the decision-making process. Except medical ontology Artificial Intelligence must have an empathic, emotional experience, without that medical care cannot be imagined. MIVAR technology is the closest to human thinking among other Artificial Intelligence technologies; it differs from the Artificial network of Mirror neurons, related to emotional perception, but MIVAR space with rules and constraints can imitate emotions and empathy.

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Experimental Autonomous Road Vehicle with Logical Artificial Intelligence

Cited by 51 publications

References 12 publications

Applicability of Computer Vision Architectures and Their Influence on Traffic Safety of Autonomous Vehicles

Applicability of Computer Vision Architectures and Their Influence on Traffic Safety of Autonomous Vehicles

COLREGs-Compliant Unmanned Surface Vehicles Collision Avoidance Based on Multi-Objective Genetic Algorithm

Logical, Philosophical and Ethical Aspects of AI in Medicine

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