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Summary & Details

Monte Carlo Tree Search and Knowledge Graphs for Decision Making in Autonomous Vehicles

Delivery: Available on this site

Format: PDF

Price: Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880

Publication code: 20225254

Paper/Info type: Proceedings (Spring)

No.57-22

Pages: 1-7(Total 7 p)

Date of publication: May 2022

Publisher: JSAE

Language: English

Event: 2022 JSAE Annual Congress (Spring)

Detailed Information

Author(E)	1) Irene Cara, 2) Mauro Comi, 3) Batrice Masini, 4) Beatrice Masini, 5) Ihsan Yalcinkaya, 6) Rutger Beekelaar
Affiliation(E)	1) TNO, 2) TNO, 3) TNO, 4) TNO, 5) TNO, 6) TNO
Abstract(E)	A fully autonomous vehicle must be able to drive in any condition and scenario. AI-based methods can increase the scalability of rule-based methods but they are not always as explainable and reliable as expert knowledge-based methods. The proposed approach investigates a novel method in the context of AI-based tactical decision making that combines Monte Carlo Tree Search, rules and an ontology-based Knowledge graph. The hybrid approach can increase scalability, trustworthiness and explainability of the framework. This decision making method is tested in two highway scenarios and one urban scenario in a simulation environment, proving the potential of the framework.

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