UWB Intention Detection for Vehicle Actuation
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Solution Overview
Problem
Existing vehicle systems lack the ability to predict user intentions accurately and activate features autonomously based on real-time user location and movements, requiring manual commands for activation.
Innovation Solution
Implementing a UWB-based system with fixed UWB anchors and mobile UWB tags to track user motion and location, using Bayesian estimation and machine learning to predict intentions, and control vehicle actuators accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual commands are used to activate vehicle features, then system complexity is reduced, but user convenience and efficiency deteriorate due to required manual input
Solution Approach 1:
The system enables self-service by automatically detecting user presence, tracking motion patterns, and predicting intentions without requiring manual commands. The UWB sensors continuously monitor the user's location and movement, and the machine learning model autonomously determines when to activate vehicle features, allowing the system to serve itself rather than requiring user initiation.
Solution Approach 2:
The system performs preliminary action by continuously tracking user motion and predicting intentions before the user actually attempts to activate features. The machine learning model analyzes motion patterns in real-time and proactively prepares to activate features based on predicted user intentions, acting before the user would manually command activation.
2Measurement precision
If UWB sensors and machine learning models are implemented to predict user intentions, then user intention prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The UWB sensor system serves multiple functions: it tracks user location, monitors motion patterns, and provides data to the machine learning model for intention prediction. This multi-functional approach consolidates what could be separate systems into a unified sensor network, reducing overall system complexity while maintaining high prediction accuracy.
Solution Approach 2:
The machine learning model acts as an intermediary that processes raw UWB sensor data and translates it into predicted user intentions. This intermediary layer simplifies the system architecture by centralizing the complex analysis function in a dedicated model, rather than distributing complex logic across multiple components.
3Speed
If real-time UWB sensor data is processed continuously, then responsiveness to user intentions is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic action by processing UWB sensor data at optimized intervals rather than continuously. The machine learning model analyzes motion patterns periodically, triggering feature activation only when prediction confidence thresholds are met, rather than maintaining constant processing that would consume more energy.
Solution Approach 2:
The system applies partial action by selectively processing sensor data based on detected motion patterns. Rather than analyzing all sensor data at full resolution continuously, the system adjusts processing intensity based on user activity levels, consuming less energy during periods of low activity while maintaining responsiveness when motion is detected.
Data Source
AI summary
A method for vehicle user prediction includes receiving, in real time, Ultra-wideband (UWB) sensor data from UWB sensors. The UWB sensors include UWB and a UWB tag. The method further includes tracking, in real time, a motion of the UWB tag using the UWB sensor data to determine whether the vehicle user is approaching the vehicle and determining a real-time location of the UWB tag relative to the vehicle using a Bayesian estimation, UWB sensor data, and the motion of the UWB tag. The method also includes predicting, using a machine learning model, an intention of the vehicle user using the motion of the UWB tag and the real-time location of the UWB tag relative to the vehicle and commanding an actuator of the vehicle, using a controller of the vehicle, to actuate in response to predicting the intention of the vehicle user.