Personalized Self-Driving Vehicle Protocol for Emergency Decisions
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Solution Overview
Problem
Self-driving motor vehicles lack the ability to customize and adapt to individual passenger preferences and behaviors, leading to difficulties in handling emergency scenarios and conflicting interests, which poses challenges in liability and operation efficiency.
Innovation Solution
A customized driving protocol is introduced, where a self-driving motor vehicle is trained and personalized based on user-specific data sets, including scenario-user-choice pairs and user profiles, to tailor its operation behaviors to match individual user preferences and responsibilities, using a system that verifies and refines these settings through simulation and road tests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a self-driving motor vehicle uses generic factory settings, then the device complexity is reduced and ease of manufacture is improved, but the adaptability to individual user preferences and behaviors deteriorates
Solution Approach 1:
The system performs preliminary customization by acquiring user profile data sets and scenario-user-choice pair data sets before the self-driving motor vehicle is practically used on public roadways. This preliminary action allows the vehicle to be pre-adapted to individual user preferences through simulation and road tests, resolving the contradiction by establishing adaptability before deployment without adding complexity during operation.
Solution Approach 2:
The self-driving motor vehicle automatically identifies current users, matches them with corresponding data sets, and applies personalized operation behaviors without external intervention. This self-service mechanism enables individualized adaptation while maintaining simple operational complexity, as the system autonomously manages the customization process.
2Adaptability or versatility
If a self-driving motor vehicle is customized for each user, then the adaptability to user preferences is improved, but the ease of operation and time required for setup deteriorates
Solution Approach 1:
All customization activities including user profile acquisition, scenario-user-choice pair collection, and data set generation are performed before the vehicle is practically used on public roadways. This preliminary customization eliminates setup time during operation, as the system is already adapted to each user's preferences when deployed.
Solution Approach 2:
The system uses simulation environments to create virtual copies of driving scenarios and user behaviors, allowing extensive customization testing and refinement without consuming actual road time. This copying approach accelerates the customization process by parallelizing development activities.
3Reliability
If a self-driving motor vehicle uses generic operation behaviors, then the device complexity is reduced, but the reliability in handling emergency scenarios and conflicting interests deteriorates
Solution Approach 1:
The system applies different operation behaviors tailored to specific users in specific scenarios rather than using uniform generic behaviors. By customizing the control characteristics locally for each user-scenario combination, the system achieves higher reliability in emergency situations while managing complexity through targeted personalization rather than system-wide complexity.
Solution Approach 2:
The system performs preliminary training through simulation and road tests to establish reliable operation behaviors for each user before practical deployment. This preliminary action ensures that the personalized control system is thoroughly validated and optimized, achieving high reliability while the complexity is resolved during the pre-deployment phase rather than during critical operation.
4Adaptability or versatility
If a self-driving motor vehicle is trained with user-specific data sets, then the adaptability to individual behaviors is improved, but the loss of time for data collection and processing increases
Solution Approach 1:
User profile data sets and scenario-user-choice pair data sets are acquired and processed before the self-driving motor vehicle is practically used on public roadways. This preliminary data collection and processing eliminates time delays during operation, as all behavioral adaptation is completed in advance through simulation and road tests.
Solution Approach 2:
The system uses simulation environments to create virtual representations of user behaviors and driving scenarios, allowing extensive data collection and processing to occur in parallel without consuming actual road time. This copying approach enables comprehensive behavioral adaptation while minimizing the time impact on practical operations.
Data Source
AI summary
A method is introduced to personalize a self-driving motor vehicle, which promises to provide an experience as if the self-driving motor vehicle is driven by the mind of a passenger the first time it operates on a freeway.