Context-Aware Vehicle Control Rules for Local Driving Conditions
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Solution Overview
Problem
Automated systems, such as self-driving vehicles and other controllable systems, often fail to adapt to local rules, conditions, and preferences, leading to uncomfortable and hazardous operations due to varying geographical and situational differences.
Innovation Solution
Collecting and processing data from individual operators in specific operational situations to create context-action pairs that mimic human driving styles, allowing systems to adapt to local norms and preferences through context-based reasoning and machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated systems operate with standardized control rules, then system reliability is improved, but adaptability to local conditions deteriorates
Solution Approach 1:
The patent implements dynamic adaptation by allowing the automated system to adjust its control parameters and behavior in real-time based on detected operational situations and local conditions. The system transitions from static standardized rules to dynamic context-aware control, enabling it to maintain reliability while adapting to varying local environments through continuous learning and adjustment.
Solution Approach 2:
The patent applies local quality by enabling the automated system to customize its operation parameters specifically for different local conditions rather than using uniform control rules everywhere. The system learns and implements location-specific driving styles, preferences, and operational patterns, allowing each region to have optimized control characteristics while maintaining overall system reliability.
2Reliability
If automated systems follow strict operational rules, then safety is improved, but ease of operation deteriorates
Solution Approach 1:
The patent implements self-service by enabling the automated system to autonomously learn and adapt to local operational preferences and conditions without requiring extensive manual programming or intervention. The system automatically adjusts its behavior to match local driving styles and preferences while maintaining safety constraints, making the system easier to deploy across different regions without manual reconfiguration.
Solution Approach 2:
The patent applies feedback mechanisms by continuously monitoring operational outcomes and using this information to refine and adjust control decisions. The system learns from past operations and local conditions, adjusting its behavior to improve both safety and ease of operation through iterative optimization based on detected patterns and performance data.
3Device complexity
If automated systems use generic control parameters, then device complexity is reduced, but adaptability to different operational situations deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-learning and storing multiple operational patterns, driving styles, and local preferences in advance through machine learning processes. The system prepares a library of adapted control parameters for different operational situations and locations before actual deployment, allowing it to quickly select appropriate parameters without complex real-time calculations, thus maintaining low device complexity while achieving high adaptability.
Data Source
AI summary
Techniques are provided for operational situation vehicle control, and include determining action and context data for one or more vehicle operations in one or more operational situations, training vehicle control rules for those operational situations, and using those vehicle control rules to control vehicles in compatible operational situations.