Driver Stress-Aware Vehicle Routing Using Physiological Data
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Solution Overview
Problem
Current vehicle routing systems do not effectively account for driver stress levels, leading to inefficient routes that may exacerbate driver fatigue and anxiety, as they primarily rely on traffic and distance without considering physiological responses to road conditions.
Innovation Solution
A route planning system that utilizes historical driver data, including physiological parameters like heart rate and respiration rate, to identify and recommend low-stress road segments, incorporating these data into route calculations to suggest routes that minimize stress, even if they are not the fastest or shortest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional routing systems use only traffic and distance data, then route calculation is simple and fast, but driver stress and fatigue are not addressed
Solution Approach 1:
The patent segments the routing problem into two distinct components: traditional route optimization (distance, time, traffic) and driver stress assessment. By separating these functions, the system can integrate physiological data without completely redesigning the routing engine, thus managing complexity while addressing driver stress through dedicated stress analysis modules
Solution Approach 2:
The patent introduces physiological sensors and stress analysis algorithms as intermediary components between the driver and the routing system. These intermediaries collect, process, and translate biological signals (heart rate, skin conductance, respiration) into stress metrics that can inform routing decisions, bridging the gap between physical driving conditions and driver wellbeing
2Reliability
If routing systems collect and process physiological data, then driver stress can be monitored and addressed, but data processing complexity and computational requirements increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and baseline-calibrating physiological data during normal driving conditions before stressful events occur. The system establishes individual driver baselines and pre-identifies stress patterns, enabling faster real-time analysis during actual stress events without requiring complex computational resources during critical moments
Solution Approach 2:
The patent employs feedback mechanisms where physiological data continuously informs routing decisions, and routing changes subsequently affect physiological responses. This closed-loop system uses feedback from stress metrics to dynamically adjust route recommendations, creating an adaptive routing system that learns from driver responses and improves its stress-management capabilities over time
3Object-affected harmful factors
If the system recommends low-stress routes, then driver wellbeing improves, but travel time may increase compared to fastest routes
Solution Approach 1:
The patent applies dynamics by making the routing objective function adaptive rather than static. The system dynamically adjusts the weighting between travel time and stress reduction based on real-time driver state, traffic conditions, and route characteristics. When driver stress is high, the system dynamically shifts priority toward stress-reducing routes; when stress is low and time is critical, it prioritizes speed, creating a flexible balance between competing objectives
Solution Approach 2:
The patent utilizes parameter changes by modifying routing parameters (such as speed limits, route type preferences, and traffic avoidance settings) based on physiological data. Instead of simply choosing between fast and slow routes, the system adjusts multiple route parameters to optimize the trade-off between travel time and stress reduction, finding optimal compromises that account for driver-specific physiological responses to different driving conditions
Data Source
AI summary
A tangible, non-transitory machine-readable medium includes machine-readable instructions that, when executed by one or more processors, cause the one or more processors to receive historical driver data indicative of one or more physiological parameters of one or more drivers during one or more prior driving trips, receive historical position data indicative of a respective position of a respective vehicle driven by the one or more drivers during the one or more prior driving trips, establish a correlation between the historical driver data and the historical position data, and identify one or more low-stress road segments using the correlation between the historical driver data and the historical position data.