Autonomous Vehicle Ethical Routing for Passenger Activity Disruption
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Solution Overview
Problem
Current navigation systems for self-driving cars do not adequately consider the activities and preferences of passengers, leading to potential disruptions of their activities during travel, such as sleep, entertainment, or meetings, and lack a mechanism to make ethical decisions in complex scenarios like accidents.
Innovation Solution
A system comprising a processor, memory, ethical investigator, synthesizer, and determinator that analyzes passenger data to determine optimal routes based on their activities and preferences, and makes ethical decisions by presenting questions to passengers to synthesize their preferences for autonomous vehicle operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional navigation systems are used to select optimal routes, then the shortest route to destination is achieved, but passenger activities may be disrupted and ethical decisions in complex scenarios cannot be made
Solution Approach 1:
The navigation system dynamically adjusts routes based on real-time passenger activity detection. Sensors monitor passenger states (sleeping, working, entertaining) and the system modifies route selection to minimize disruptions, such as avoiding sharp turns or stops during sleeping hours, while still maintaining reasonable travel time efficiency.
Solution Approach 2:
The system implements continuous feedback loops where passenger responses to ethical questions and activity monitoring data are fed back into the decision-making algorithm. This allows the system to learn from passenger preferences and adjust both route selection and ethical decision-making in real-time, balancing efficiency with passenger comfort and preferences.
2Ease of operation
If navigation systems consider passenger activities and preferences, then passenger comfort is improved, but system complexity increases
Solution Approach 1:
The system segments the complex decision-making process into distinct functional modules: activity detection module, preference elicitation module, ethical decision module, and route optimization module. Each module handles a specific aspect of passenger consideration, making the overall system more manageable and maintainable while still achieving comprehensive passenger comfort optimization.
Solution Approach 2:
The system introduces an intermediary ethical decision-making layer that mediates between traditional navigation objectives and passenger preferences. This intermediary component processes ethical questions, synthesizes passenger preferences, and translates them into modified navigation parameters, thereby managing complexity through a dedicated intermediary subsystem rather than integrating all functions directly into the core navigation algorithm.
3Reliability
If ethical questions are presented to passengers for decision making, then ethical preferences are captured, but additional time and computational resources are required
Solution Approach 1:
The system performs preliminary actions by pre-presenting ethical questions to passengers before critical decision points are reached. It also pre-synthesizes ethical preferences and stores them for future reference, allowing the system to retrieve pre-established preferences rather than conducting time-consuming surveys during urgent situations, thereby reducing real-time decision-making delays.
Solution Approach 2:
The system changes parameters by adjusting the frequency and timing of ethical questions based on situation urgency. In non-critical situations, comprehensive ethical questions are posed to capture detailed preferences. In urgent situations, the system relies on pre-synthesized preferences or uses simplified decision parameters, thereby adapting the time investment to the criticality of the situation.
Data Source
AI summary
A method, system and product for implementing user-based ethical decision making in autonomous vehicles. The system includes an ethical investigator that presents an ethical questioner to an entity and collects the entity's responses. An ethical synthesizer synthesizes an ethical preference of the entity based on the collected responses. An ethical determinator selects an action from a plurality of potential actions based on an ethical preference of a controlling entity of a self-driving vehicle at a time the selection is made. The system is configured to cause the self-driving vehicle to perform the action selected by said ethical determinator automatically and without relying on user input.