Adaptive Stop-Start Inhibitor for Driver Anxiety
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Solution Overview
Problem
Current engine auto-stop systems in hybrid vehicles fail to accurately predict driver anxiety, leading to potential fuel efficiency reduction and increased emissions, as they do not consider driver history, road attributes, or collective information from connected vehicle fleets, and often cause anxiety in drivers during high-anxiety scenarios.
Innovation Solution
A method that determines a driver's anxiety level by classifying drivers based on their history and road attributes using a neural network trained with data from dashboard cameras, microphones, and other sensors, and selectively inhibits engine idle-stop events based on predicted anxiety levels, leveraging connected vehicle data to anticipate and mitigate anxious situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If engine auto-stop system is activated to conserve fuel, then fuel efficiency is improved, but driver anxiety increases in high-anxiety scenarios
Solution Approach 1:
The system performs preliminary classification of drivers into anxiety-prone and non-anxiety-prone categories using machine learning models that analyze driving behavior patterns, facial expressions, and physiological data. This preliminary classification enables the system to proactively adjust stop-start functionality before anxiety-inducing scenarios occur, preventing driver anxiety while maintaining fuel efficiency for appropriate drivers.
Solution Approach 2:
The system applies different stop-start control strategies to different driver segments based on their classified anxiety levels. For anxiety-prone drivers, the system selectively inhibits stop-start events in high-anxiety scenarios (such as left-turn scenarios, four-way stops, and heavy traffic conditions), while maintaining normal operation for non-anxiety-prone drivers. This localized approach ensures fuel efficiency is optimized for each driver's specific needs.
2Object-affected harmful factors
If manual override is provided to disable stop-start system, then driver anxiety is reduced, but fuel efficiency decreases
Solution Approach 1:
The system automatically classifies drivers and adjusts stop-start functionality without requiring manual intervention. The machine learning models continuously monitor driving behavior, facial expressions, and physiological data to autonomously determine when to inhibit or enable stop-start events. This self-service approach eliminates the need for manual override while maintaining optimal fuel efficiency through intelligent, context-aware control decisions.
Solution Approach 2:
The system dynamically changes the operational parameters of the stop-start system based on real-time driver classification and scenario detection. For anxiety-prone drivers in high-anxiety scenarios, the system modifies the stop-start activation threshold to prevent engine shutdown. This parameter adjustment is automatic and context-dependent, ensuring fuel efficiency is maintained when appropriate while preventing anxiety when needed.
3Ease of operation
If stop-start system is automatically calibrated based on vehicle conditions, then operational simplicity is improved, but accuracy in predicting driver anxiety deteriorates
Solution Approach 1:
The system transitions from single-dimensional vehicle condition monitoring to multi-dimensional driver state assessment by incorporating facial expression analysis, physiological data (heart rate, galvanic skin response), and driving behavior patterns. This dimensional expansion enables accurate prediction of driver anxiety states while maintaining automatic calibration, as the system processes multiple data streams simultaneously to make informed control decisions.
Solution Approach 2:
The system implements continuous feedback loops where machine learning models analyze real-time driver responses to stop-start events and refine anxiety predictions. Facial cameras, physiological sensors, and driving behavior monitors provide ongoing feedback that updates driver classification and adjusts stop-start functionality dynamically. This feedback mechanism improves prediction accuracy over time while maintaining automatic operation without manual calibration.
Data Source
AI summary
Methods and systems are provided for selectively inhibiting a stop-start controller of a vehicle based on a predicted anxiety of a driver of the vehicle on a road segment that includes one or more road attributes associated with increased levels of anxiety. In one example, selectively inhibiting a stop-start controller of a vehicle based on a predicted anxiety of a driver of the vehicle includes determining a driver classification for a driver operating the vehicle; predicting an anxiety level of the driver at an upcoming traffic condition, based on the driver classification; and selectively inhibiting an upcoming engine idle-stop event based on the predicted anxiety level of the driver.