Vehicle Situational Recommendations for Predictive Action Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current vehicle systems require occupants to navigate through multiple menus to perform actions and lack efficient methods for predicting and initiating desired actions based on vehicle location and conditions.
Innovation Solution
A system utilizing onboard sensors and a driver-specific machine learning model to predict and initiate actions, providing recommendations through a human-machine interface, and updating the model based on occupant input and data from similar vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a system automatically predicts and initiates desired actions based on sensor data and machine learning, then ease of operation and productivity are improved, but device complexity increases due to the need for sophisticated algorithms and data processing capabilities
Solution Approach 1:
The system performs actions automatically without requiring explicit user commands. The machine learning model predicts desired actions based on sensor data and vehicle conditions, then initiates these actions autonomously, allowing the system to serve itself rather than requiring continuous user input.
Solution Approach 2:
The system predicts and prepares actions in advance based on analyzed patterns from sensor data and historical information. By forecasting desired actions before they are explicitly requested, the system can pre-position resources and initiate sequences ahead of time, improving operational efficiency.
2Ease of operation
If the system provides personalized recommendations through a user interface, then ease of operation improves, but device complexity increases due to additional interface components and processing requirements
Solution Approach 1:
The system presents predicted actions to the user through the interface and receives feedback on whether these predictions were correct. This feedback loop allows the machine learning model to continuously improve its accuracy by learning from user responses, making the system progressively better at predicting desired actions.
Solution Approach 2:
The user interface dynamically adapts based on the predicted actions and user interactions. The system adjusts its recommendations and interface presentations in real-time based on current vehicle conditions, sensor data, and learned user preferences, making the interaction flexible rather than static.
Data Source
AI summary
A system for providing situational recommendations within a vehicle includes a system controller in communication with a plurality of onboard sensors, the plurality of onboard sensors adapted to collect real-time data related to a location of the vehicle and operating conditions of the vehicle, a database in communication with the system controller adapted to store data related to past actions and data related to a location of the vehicle and operating conditions of the vehicle when such past actions occurred, the system controller including a driver specific machine learning model adapted to predict a desired action based on the real-time data related to the location and operating conditions of the vehicle and data from the database, the system controller further adapted to initiate the predicted desired action, receive input from an occupant within the vehicle, and update the driver specific machine learning model.