ADAS Drive Policy Management Using AI Context and Voice Inputs
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Solution Overview
Problem
Existing vehicle Advanced Driver Assist Systems (ADAS) do not effectively utilize vehicle context data to manage drive policies, leading to suboptimal vehicle operation.
Innovation Solution
Implementing an Artificial Intelligence (AI) and Machine Learning (ML) based ADAS drive policy management system that detects current vehicle context through sensors and user voice inputs, allowing for dynamic selection and adjustment of driving policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional vehicle policies are used without context data, then the system is simple and reliable, but operating efficiency is suboptimal
Solution Approach 1:
The patent implements dynamic policy selection where the ADAS system continuously adapts driving policies based on real-time vehicle context data. The system transitions from static, pre-defined policies to dynamic, context-aware policy selection, allowing the vehicle to optimize operating efficiency for different situations (e.g., highway vs. city driving, weather conditions, traffic patterns) while maintaining system reliability through structured decision-making frameworks.
Solution Approach 2:
The system incorporates feedback loops where vehicle context data from sensors (GPS, accelerometers, weather sensors, traffic cameras) continuously feeds into the policy management system. This feedback mechanism allows the system to learn from past driving scenarios and improve future policy selections, enhancing operating efficiency without requiring complete system redesign.
2Productivity
If vehicle context data is utilized for policy management, then operating efficiency improves, but system complexity increases
Solution Approach 1:
The patent segments the complex policy management task into distinct modular components: context data acquisition module, context analysis module, policy selection module, and policy execution module. Each module handles specific aspects of context-aware driving, making the overall system more manageable and maintainable while achieving improved operating efficiency through coordinated operation of these specialized components.
Solution Approach 2:
The system employs a universal policy management framework that can handle multiple types of vehicle contexts (geographic location, weather conditions, traffic patterns, vehicle state) through a single integrated architecture. This multi-functional approach allows the system to process diverse context data types using common processing pipelines, reducing complexity compared to having separate systems for each context type.
3Adaptability or versatility
If AI/ML systems are implemented for real-time policy adjustment, then adaptability improves, but processing requirements and complexity increase
Solution Approach 1:
The patent implements preliminary action by pre-training AI/ML models offline with extensive driving scenario data before deployment. Context-aware policies and decision-making rules are pre-computed and stored as knowledge bases. During real-time operation, the system performs lighter-weight inference rather than full training, significantly reducing processing requirements while maintaining high adaptability to new situations through the pre-learned knowledge.
Solution Approach 2:
The system applies partial action by focusing AI/ML processing only on critical decision points rather than continuously analyzing all sensor data. The system identifies key moments requiring adaptive policy changes (e.g., upcoming intersections, detected hazards, route changes) and applies sophisticated processing only when needed, reducing overall computational burden while maintaining high adaptability where it matters most.
Data Source
AI summary
Various embodiments include methods and vehicles that may include managing driving policies in a vehicle advanced driver assist system (ADAS) that include obtaining vehicle sensor data, determining a current vehicle context based on the vehicle sensor data, selecting a modified vehicle driving policy from a plurality of saved modified vehicle driving policies based on the determined current vehicle context, and controlling vehicle behavior by the ADAS based upon the selected modified vehicle driving policy. Methods may also include receiving user voice inputs from a vehicle microphone, using a generative AI to infer relevance of the user voice inputs to vehicle driving policies or actions of the ADAS, adjusting a vehicle driving policy of the ADAS based on the inferred relevance of the user voice inputs, and commanding vehicle behavior based upon the adjusted vehicle driving policy.