Version 1
: Received: 8 December 2023 / Approved: 12 December 2023 / Online: 12 December 2023 (04:16:31 CET)
How to cite:
Raiyn, J.; Weidl, G. Predicting Autonomous Driving Behavior through Human Factor Considerations in Safety-Critical Events. Preprints2023, 2023120771. https://doi.org/10.20944/preprints202312.0771.v1
Raiyn, J.; Weidl, G. Predicting Autonomous Driving Behavior through Human Factor Considerations in Safety-Critical Events. Preprints 2023, 2023120771. https://doi.org/10.20944/preprints202312.0771.v1
Raiyn, J.; Weidl, G. Predicting Autonomous Driving Behavior through Human Factor Considerations in Safety-Critical Events. Preprints2023, 2023120771. https://doi.org/10.20944/preprints202312.0771.v1
APA Style
Raiyn, J., & Weidl, G. (2023). Predicting Autonomous Driving Behavior through Human Factor Considerations in Safety-Critical Events. Preprints. https://doi.org/10.20944/preprints202312.0771.v1
Chicago/Turabian Style
Raiyn, J. and Galia Weidl. 2023 "Predicting Autonomous Driving Behavior through Human Factor Considerations in Safety-Critical Events" Preprints. https://doi.org/10.20944/preprints202312.0771.v1
Abstract
This research explores the predictive capabilities of autonomous driving systems by integrating human factor considerations within the context of safety-critical events. Recognizing the significance of human behaviors in influencing driving dynamics, the study employs advanced modeling techniques to enhance the accuracy of predictions in scenarios that demand heightened safety measures. Traditional rule-based systems and monotonic logic often fall short in addressing the complexities of safety-critical events. To overcome these limitations, the research proposes the application of non-monotonic logic, allowing for flexible and adaptive reasoning that accommodates exceptions and context-specific information.The study emphasizes the importance of incorporating individual differences among drivers, such as risk-taking tendencies, reaction times, decision-making processes, and driving styles. By considering these human factors, the research aims to develop realistic and accurate autonomous driving models that capture the nuances of real-world driving scenarios, especially in safety-critical situations. The predictive model takes into account both internal and external factors, enabling the autonomous system to anticipate and respond effectively to unforeseen events.The primary goal is to provide autonomous vehicles with the capability to make plausible inferences, handle conflicting data, and adapt their behavior in real-time during safety-critical events. The proposed model integrates personalized cognitive agents for each driver, incorporating their unique preferences, characteristics, and needs. This personalized approach aims to optimize the safety and efficiency of autonomous driving, contributing to the ongoing evolution of intelligent transportation systems.In conclusion, this research contributes to advancing the field of autonomous driving by introducing a predictive model that leverages human factor considerations to enhance safety in safety-critical events. The incorporation of non-monotonic logic and individualized cognitive agents signifies a comprehensive approach to address the challenges associated with predicting autonomous driving behavior, paving the way for safer and more reliable autonomous vehicles in dynamic and unpredictable environments.
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.
