Vehicle Driver Advisory System Using Spoken Dialog Interface
Solution Overview
Problem
Different drivers have varying performance preferences from a vehicle, such as fuel economy or response and handling, which existing systems fail to customize effectively.
Innovation Solution
An intelligent driver-vehicle interface system that uses a Spoken Dialog System (SDS) and information filter to communicate with vehicle systems, learning driver preferences through voice commands and sensor data to adjust powertrain modes such as sport, normal, comfort, and fuel economy modes, and proactively recommend performance settings based on driving conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the vehicle provides fixed performance characteristics, then the system complexity is low, but the adaptability to different driver preferences deteriorates
Solution Approach 1:
The system automatically detects driver preferences by monitoring operating parameters and autonomously determines recommended performance modes without requiring manual driver input. The processor analyzes sensor data, identifies preference patterns, and generates mode recommendations automatically, allowing the system to serve itself in adapting to driver needs.
Solution Approach 2:
The system continuously monitors vehicle operating parameters and driver behavior patterns, uses this feedback to learn driver preferences over time, and adjusts performance mode recommendations accordingly. The feedback loop enables the system to adapt dynamically changing driver preferences while maintaining manageable complexity through iterative learning.
2Ease of operation
If the system proactively recommends performance modes, then the ease of operation improves, but the loss of information about driver intent increases
Solution Approach 1:
The system proactively analyzes current operating conditions and driver behavior patterns in advance to generate performance mode recommendations before the driver needs to make a selection. By performing preliminary analysis and presenting ready-made recommendations, the system reduces the cognitive load and operational steps required for mode selection.
Solution Approach 2:
The system autonomously performs the information processing and mode selection recommendation tasks that would otherwise require active driver engagement. By self-service analysis of operating parameters and automatic generation of contextualized recommendations, the system minimizes information loss about driver intent while improving ease of operation.
Data Source
AI summary
An automotive vehicle may include at least one controller configured to monitor a plurality of parameters representing a driver's dynamic control of the vehicle and to establish a reference range based on values of the monitored parameters during a first time period. The at least one controller may be further configured to detect a change in driver behavior condition when, during a second time period, a parameter derived from the monitored parameters falls outside of the reference range, and in response to detecting the change in driver behavior condition, to alter a response of the vehicle to driver inputs or to generate output representing a recommendation to alter a response of the vehicle to driver inputs.