ADAS Macro-State Hazard Alerts for Driver Decision Risk
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Solution Overview
Problem
Current advanced driver-assistance systems (ADAS) primarily address in-the-moment decisions and do not effectively account for macro-level decisions that influence accident risks, such as vehicle choice and driving time, which are subconscious and not captured in driving statistics.
Innovation Solution
A vehicle system that determines a macro state level based on vehicle, driver, and external parameters to generate alerts and control signals, integrating both macro and micro-level decision analysis to mitigate traffic hazards, using a perception system with a processor to receive parameter values and generate control signals for adjusting vehicle autonomy levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If ADAS systems focus only on in-the-moment micro-level decisions, then response time to immediate hazards is improved, but the system fails to account for macro-level subconscious decisions that influence overall accident risk
Solution Approach 1:
The system segments decision-making into two distinct levels: macro-level subconscious decisions (vehicle choice, driving time, route selection) and micro-level conscious decisions (in-the-moment hazard responses). This segmentation allows the system to process each level appropriately without compromising response time to immediate hazards while simultaneously capturing macro-level risk factors that were previously overlooked.
Solution Approach 2:
The system performs preliminary action by capturing and analyzing macro-level decision data before accidents occur. By proactively collecting information about subconscious decisions such as vehicle selection, driving time preferences, and route choices, the system establishes a baseline risk profile that informs subsequent micro-level hazard responses, enabling more comprehensive safety interventions.
2Reliability
If the system captures and analyzes macro-level subconscious decisions, then traffic hazard risk assessment is improved, but system complexity increases due to integrating multiple decision levels
Solution Approach 1:
The system adds another dimension to traditional ADAS by incorporating macro-level subconscious decision data alongside micro-level hazard responses. This dimensional expansion transforms the system from analyzing only immediate driving actions to evaluating both long-term behavioral patterns and short-term hazard reactions, thereby improving risk assessment reliability without fundamentally redesigning the core safety architecture.
Solution Approach 2:
The system introduces an intermediary layer that bridges macro-level and micro-level decision analysis. This intermediary component processes and integrates data from both decision levels, translating complex macro-level behavioral patterns into actionable insights that enhance micro-level hazard responses, thereby improving overall reliability without proportionally increasing system complexity.
3Reliability
If the system integrates both macro and micro-level decision analysis, then comprehensive safety monitoring is improved, but data processing requirements increase
Solution Approach 1:
The system extracts and separates macro-level decision data from micro-level hazard data, processing each type independently according to its specific requirements. By taking out macro-level patterns for separate analysis, the system avoids the computational overhead of processing all data at micro-level resolution, thereby maintaining comprehensive safety monitoring while reducing overall data processing energy consumption.
Solution Approach 2:
The system applies partial action by focusing computational resources on processing micro-level hazard data in real-time, while using less intensive processing for macro-level behavioral patterns. Since macro-level decisions change more slowly and have longer prediction horizons, they require less frequent and less computationally intensive analysis, thereby reducing total energy consumption while maintaining comprehensive safety coverage.
Data Source
AI summary
Methods, vehicles, and systems described herein generate alerts based on a generated macro state level indicative of a traffic hazard risk in view of a set of parameters which may lead driver to make various sub-conscious decisions. The set of parameters can include at least one of a vehicle parameter, driver state parameter, or external parameter.