Investigations of Ethical Dimensions in Autonomous Driving using GPT3.5

This research reviews the critical aspects of safety and ethical decision-making in autonomous driving systems. Focusing on rule-based, probabilistic, reinforcement learning, and hybrid models, the study examines how these models approach challenging scenarios, such as unexpected pedestrian appearances and encounters with large animals on mountain roads. By analyzing these scenarios through the lens of GPT-3.5, the research highlights the diverse decision-making approaches and their ethical implications. The study highlights the importance of transparency in decision-making processes, building public trust, and navigating the complexities of human perception versus autonomous vehicle responses. Ultimately, this research aims to contribute to the development of safe and ethical autonomous driving systems that can coexist harmoniously with human drivers and enhance road safety for all.