Version 1
: Received: 18 September 2023 / Approved: 19 September 2023 / Online: 20 September 2023 (08:46:34 CEST)
Version 2
: Received: 20 September 2023 / Approved: 20 September 2023 / Online: 21 September 2023 (08:26:57 CEST)
Version 3
: Received: 21 September 2023 / Approved: 21 September 2023 / Online: 22 September 2023 (05:30:08 CEST)
How to cite:
Raiyn, J.; Weidl, G. Improving Autonomous Vehicle Reasoning with Non-Monotonic Logic: Advancing Safety and Performance in Complex Environments. Preprints2023, 2023091341. https://doi.org/10.20944/preprints202309.1341.v3
Raiyn, J.; Weidl, G. Improving Autonomous Vehicle Reasoning with Non-Monotonic Logic: Advancing Safety and Performance in Complex Environments. Preprints 2023, 2023091341. https://doi.org/10.20944/preprints202309.1341.v3
Raiyn, J.; Weidl, G. Improving Autonomous Vehicle Reasoning with Non-Monotonic Logic: Advancing Safety and Performance in Complex Environments. Preprints2023, 2023091341. https://doi.org/10.20944/preprints202309.1341.v3
APA Style
Raiyn, J., & Weidl, G. (2023). Improving Autonomous Vehicle Reasoning with Non-Monotonic Logic: Advancing Safety and Performance in Complex Environments. Preprints. https://doi.org/10.20944/preprints202309.1341.v3
Chicago/Turabian Style
Raiyn, J. and Galia Weidl. 2023 "Improving Autonomous Vehicle Reasoning with Non-Monotonic Logic: Advancing Safety and Performance in Complex Environments" Preprints. https://doi.org/10.20944/preprints202309.1341.v3
Abstract
The software design of autonomous vehicles (AVs) incorporates artificial intelligence (AI) characteristics to enhance their safety and overall driving performance. Central to vehicle’s operation is the ability to reason effectively in complex and uncertain environments. However, traditional logical systems, such as monotonic logic, often struggle to handle the inherent uncertainties and exceptions encountered in real-world scenarios. This paper proposes the utilization of non-monotonic logic in order to enhance the reasoning capabilities of autonomous vehicles. By incorporating non-monotonic reasoning, vehicles can navigate intricate traffic scenarios, make plausible inferences, and adapt their decisions when faced with conflicting information. This research aims to provide a comprehensive review of non-monotonic logic's application in autonomous vehicles, highlighting its advantages over traditional logical systems and its potential impact on safety and performance. Additionally, through this research, we seek to contribute to the advancement of autonomous driving technology by enhancing the reasoning capabilities of vehicles in various scenarios, such as car- following related to critical safety events. The personalized cognitive agent is proposed in driving behavior to consider particularly in their assumptions of homogeneous drivers. The personalized cognitive agent is incorporating heterogeneous driving behaviors, based on individual user preferences, characteristics, and needs. Driving behavior is a complex interplay of various factors, encompassing both human and external elements. Human factors, including age, experience, and gender, contribute significantly to how individuals navigate the roads. These factors influence decisions, reactions, and risk-taking tendencies on the part of drivers. Additionally, external factors such as weather conditions further compound this intricate dynamic, requiring drivers to adapt their behavior to the prevailing environment. The goal of a personalized cognitive agent is to provide tailored and customized experiences to cognitive vehicles, taking into account the unique requirements and individual preferences of occupants inside autonomous vehicles.
Engineering, Transportation Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Jamal Raiyn
Commenter's Conflict of Interests: Author