Vehicle Message Stylization Using Occupant State and Context
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Solution Overview
Problem
Existing vehicle messaging systems provide predefined messages that may not be optimally perceived by occupants due to individual differences in perception and contextual factors, leading to reduced awareness and response.
Innovation Solution
A message system that uses sensor data to determine a style for presenting messages based on the current context, including the occupant's mental state, vehicle conditions, and environmental factors, and generates messages using a style model to improve perception and response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If predefined messages are used in vehicle systems, then the messaging function is simple and reliable, but the messages do not adapt to individual occupant preferences or contextual conditions, reducing effectiveness
Solution Approach 1:
The message system dynamically adapts its output based on real-time sensor data about occupant state (stress level, attention, mood) and environmental context (traffic conditions, weather, time of day). The system transitions from static predefined messages to dynamic contextualized messages that adjust content, tone, and delivery method based on current conditions.
Solution Approach 2:
The system changes multiple parameters of message delivery including content selection, tone (stern/soothing), modality (visual/audio/haptic), timing, and priority based on sensed occupant state and environmental factors. This allows the same underlying information to be delivered in vastly different ways appropriate to the current context.
2Reliability
If sensor data collection and style modeling are implemented, then message effectiveness improves, but the system complexity and computational requirements increase
Solution Approach 1:
The vehicle's existing sensor network (cameras, microphones, accelerometers, GPS) is repurposed to collect data for message styling in addition to their primary functions. The style model serves as a universal translator that converts diverse sensor inputs into standardized message parameters, allowing one system to handle multiple sensing functions.
Solution Approach 2:
The style model acts as an intermediary layer between the complex sensor data collection system and the message delivery system. It processes and interprets sensor data to determine appropriate message characteristics, shielding the message generation logic from the complexity of raw sensor processing while maintaining reliability.
3Productivity
If messages are customized to individual occupant states, then occupant awareness and response improve, but the time required to process and deliver messages increases
Solution Approach 1:
The system continuously pre-processes sensor data in the background to maintain an up-to-date profile of occupant state and environmental conditions. When a message needs to be delivered, the styling information is already prepared or can be quickly determined from the pre-processed data, minimizing additional processing time.
Solution Approach 2:
The system incorporates feedback loops where occupant responses to messages are monitored and used to refine future message styling. This creates a learning system that becomes progressively more efficient at delivering effective messages with minimal processing time as it adapts to individual occupant preferences and patterns.
Data Source
AI summary
Systems, methods, and other embodiments described herein relate to stylizing messages within a vehicle according to an occupant and a current context. In one embodiment, a method includes determining a style for presenting messages associated with an occupant of a vehicle according to a context defined in relation to an occupant and an environment of the vehicle. The method includes generating a message according to the style for the occupant. The method includes providing the message to the occupant.