Integrating Human and AI Preferences into Autonomous Vehicle Moral Decision-Making

Description:

This invention combines reinforcement learning and large language models to embed nuanced human moral values into autonomous vehicle decision processes. By leveraging human data from diverse ethical theories and demographic variations, it creates an adaptive framework that guides vehicles to make ethical choices aligned with societal values, improving safety and public trust.

Key Advantages:

• Adaptive ethical framework that evolves with diverse human moral preferences.
• Combines reinforcement learning with large language models for improved decision-making.
• Incorporates demographic-specific ethical variations to reflect societal diversity.
• Moves beyond rigid rule-based ethical systems traditionally used in AVs.
• Validated in simulated environments for effective alignment with human moral behavior.

Problems Solved:

• Bridges the gap in ethical decision-making for autonomous vehicles.
• Enhances public acceptance of AVs through alignment with societal morals.
• Addresses ambiguity in ethical dilemmas faced by AVs on the road.
• Overcomes limitations of fixed, rule-based ethical systems in AI.
• Provides a scalable approach adaptable to different demographic and cultural contexts.

Market Applications:

• Autonomous vehicle manufacturers such as Tesla, Waymo, and Cruise.
• Insurance companies assessing risk and liability for AV operations.
• Regulatory bodies formulating ethical standards and guidelines for AV deployment.
• Public sector autonomous transportation and infrastructure systems.
• AI developers focused on ethical decision-making frameworks.