Cognitive Route Search Technique for Self-Driving Vehicles

Author:

Hiraishi Hironori1,Mizoguchi Fumio2

Affiliation:

1. Ashikaga Institute of Technology, Ashikaga, Japan

2. WisdomTex Inc., Tokyo, Japan

Abstract

This article introduces Time-Constrained Heuristic Search (TCS) as a route search technique that incorporates a cognitive aspect in self-driving vehicles. TCS is a search method using which we can set the time limit to generate a route in advance. The most important feature of TCS is that the node expansion in the latter portion of the journey near the destination tends to be minimized to ensure that the search finishes within the prescribed time limit. TCS conducts a search during the initial portion of the journey that is related to the route the vehicle will move on in the immediate future, but does not take into account the latter portion of the route to a great extent. So, TCS can be regarded as the search technique that selects an option to generate immediate benefit. This characteristic is well-known as the present-oriented bias or the prospect theory in cognitive psychology. Hence, the authors can infer that TCS provides an ability of cognitive judgment to self-driving vehicles.

Publisher

IGI Global

Subject

Artificial Intelligence,Human-Computer Interaction,Software

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Reproduction of mental states in driving using a visual filter;2022 IEEE 21st International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC);2022-12-08

2. IEEE ICCI*CC Series in Year 20: Latest Advances in Cognitive Computing (Plenary Panel Report-II);2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC);2021-10-29

3. Mankind at a Crossroads;International Journal of Software Science and Computational Intelligence;2020-07

4. Self-driving Route Design using Improved Circle Algorithm;2020 5th International Conference on Smart Grid and Electrical Automation (ICSGEA);2020-06

5. In-Vehicle Cognitive Route Decision Using Fuzzy Modeling and Artificial Neural Network;IEEE Access;2019

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