{
  "$type": "site.standard.document",
  "description": "A computer-implemented method, system, and computer program product for autonomous vehicle ethical decision-making. A dataset of human moral judgements regarding autonomous vehicle ethical dilemmas is obtained, such as via a moral machine framework. Furthermore, a reinforcement learning (RL) agent…",
  "path": "/patents/1382136",
  "publishedAt": "2026-05-07T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "B60W60/0011",
    "Texas State University"
  ],
  "textContent": "A computer-implemented method, system, and computer program product for autonomous vehicle ethical decision-making. A dataset of human moral judgements regarding autonomous vehicle ethical dilemmas is obtained, such as via a moral machine framework. Furthermore, a reinforcement learning (RL) agent is trained using the dataset to determine a preferred ethical action in a given dilemma. As a result of such training, the trained RL agent is responsible for synthesizing the human-preferred choices from the dataset into a functional policy. The preferred ethical action in a given action that was determined by the trained RL agent is then executed to control the autonomous vehicle (AV). For example, the RL agent's ethically-informed decisions directly govern the AV's behavior, such as steering or braking. Such an execution of the preferred ethical action translates the theoretical moral policy trained on human preferences into an on-the-road control command that influences the vehicle's operation in real-time.",
  "title": "INTEGRATING HUMAN AND AI PREFERENCES IN AUTONOMOUS VEHICLES"
}