Open Access

ARTICLE

AI Safety Approach for Minimizing Collisions in Autonomous Navigation

Abdulghani M. Abdulghani, Mokhles M. Abdulghani, Wilbur L. Walters, Khalid H. Abed^*

Department of Electrical & Computer Engineering and Computer Science, Jackson State University, Jackson, 39217, USA

* Corresponding Author: Khalid H. Abed. Email:

Journal on Artificial Intelligence 2023, 5, 1-14. https://doi.org/10.32604/jai.2023.039786

Received 16 February 2023; Accepted 11 April 2023; Issue published 08 August 2023

Abstract

Autonomous agents can explore the environment around them when equipped with advanced hardware and software systems that help intelligent agents minimize collisions. These systems are developed under the term Artificial Intelligence (AI) safety. AI safety is essential to provide reliable service to consumers in various fields such as military, education, healthcare, and automotive. This paper presents the design of an AI safety algorithm for safe autonomous navigation using Reinforcement Learning (RL). Machine Learning Agents Toolkit (ML-Agents) was used to train the agent with a proximal policy optimizer algorithm with an intrinsic curiosity module (PPO + ICM). This training aims to improve AI safety and minimize or prevent any mistakes that can cause dangerous collisions by the intelligent agent. Four experiments have been executed to validate the results of our research. The designed algorithm was tested in a virtual environment with four different models. A comparison was presented in four cases to identify the best-performing model for improving AI safety. The designed algorithm enabled the intelligent agent to perform the required task safely using RL. A goal collision ratio of 64% was achieved, and the collision incidents were minimized from 134 to 52 in the virtual environment within 30 min.

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Keywords

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