Driving Assistance System Operation Misapplication Mitigation
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Solution Overview
Problem
Existing driving assistance systems are not robust enough to effectively mitigate operation misapplications, such as pedal, gear, and steering misapplications, in vehicles, leading to potential traffic accidents.
Innovation Solution
The system determines if an operation misapplication feature is applicable based on an operational design domain context and compares driver characteristics to predetermined patterns to assess readiness. It then actuates the vehicle to mitigate the misapplication, applying measures like acceleration suppression, brake application, or drive control depending on the severity of the misapplication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If driving assistance systems are implemented to mitigate operation misapplications, then safety and reliability are improved, but the system complexity and computational requirements increase
Solution Approach 1:
The system segments the mitigation approach by dividing operation misapplications into different types (first type requiring actuation, second type requiring warnings only) based on operational context and driver state. This segmentation allows the system to apply complex interventions only when necessary, reducing overall system complexity while maintaining safety.
Solution Approach 2:
The system performs preliminary assessment of driver readiness and operational context before implementing mitigation measures. By evaluating driver state patterns and operational design domain context in advance, the system determines the appropriate level of intervention, avoiding unnecessary complex actuations and reducing computational burden.
2Measurement precision
If the system continuously monitors driver state and operational context to detect misapplications, then detection precision is improved, but energy consumption and processing time increase
Solution Approach 1:
The system applies partial monitoring by focusing computational resources on critical assessments: matching operational context against predetermined patterns and comparing driver state against readiness patterns. This selective monitoring achieves sufficient detection precision without requiring continuous full-system analysis, thereby reducing energy consumption.
Solution Approach 2:
The system changes monitoring parameters dynamically by adjusting the stringency of driver readiness assessment based on operational context. In high-risk contexts, monitoring becomes more stringent; in low-risk contexts, monitoring intensity is reduced, optimizing the balance between detection precision and energy consumption.
3Reliability
If the system applies actuation measures to mitigate first type operation misapplications, then reliability is improved, but the ease of operation and driver control are reduced
Solution Approach 1:
The system applies preliminary anti-action by implementing actuation measures (acceleration suppression, brake application) to counteract dangerous driver inputs before they can cause harm. This preliminary counter-action prevents misapplications from resulting in accidents, prioritizing safety while minimizing the duration and intensity of control interference.
Solution Approach 2:
The system dynamically adjusts the level of driver control intervention based on the severity and type of misapplication detected. For first type misapplications, actuation measures are applied temporarily; for second type misapplications, only warnings are issued. This dynamic response maintains ease of operation during normal driving while ensuring safety during critical situations.
Data Source
AI summary
Disclosed are systems and techniques for driving assistance systems. For example, a computing device can determining an operation misapplication feature is applicable to a vehicle based on an operational design domain (ODD) context associated with the vehicle matching at least one of a plurality of predetermined ODDs. The computing device can determine, based on a comparison of characteristics of a driver of the vehicle to a plurality of predetermined driver state patterns, the driver is not ready to engage in a driving task associated with the vehicle. The computing device can determine the operation misapplication of the vehicle is a first type of operation misapplication. The computing device can actuate the vehicle to mitigate the operation misapplication based on determining the operation misapplication is the first type of operation misapplication.