SSVEP HUD Control for Hands-Free In-Vehicle Feature Selection
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Solution Overview
Problem
Distracted driving due to interaction with in-vehicle features poses a significant risk of vehicle crashes, necessitating technology that allows drivers to interact with vehicle systems without diverting attention from the road.
Innovation Solution
A vehicle system utilizing a heads-up display (HUD) and electroencephalography (EEG) to detect which icons a driver is viewing through steady-state visually evoked potentials (SSVEP), enabling control of vehicle functions without manual interaction, employing a deep learning model trained on EEG data to predict icon selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual interaction with in-vehicle features is implemented, then ease of operation is improved, but driver distraction and crash risk increase
Solution Approach 1:
The patent replaces manual mechanical interaction with in-vehicle features by implementing brain-computer interface (BCI) technology that detects neural signals and translates them into control commands. This substitution eliminates the need for manual operation while maintaining control capability, thereby improving safety without sacrificing ease of operation.
Solution Approach 2:
The patent introduces an intermediary system consisting of neural signal detection devices, signal processing algorithms, and control translation mechanisms. This intermediary layer enables communication between the driver's intent and vehicle systems without requiring direct manual interaction, thus preventing distraction while preserving operational ease.
2Reliability
If brain-computer interface technology is implemented, then driver safety is improved, but device complexity increases
Solution Approach 1:
The patent implements a multi-functional BCI system that can detect various neural signals, process different types of brain waves, and control multiple vehicle functions through a single integrated platform. This universal approach consolidates what would otherwise require multiple separate systems, managing complexity while enhancing safety.
Solution Approach 2:
The patent employs self-calibrating algorithms and adaptive signal processing that automatically adjust to individual driver characteristics without requiring manual configuration or complex setup procedures. This self-service capability reduces operational complexity while maintaining high safety performance.
3Speed
If real-time EEG processing is implemented, then responsiveness is improved, but computational requirements and energy consumption increase
Solution Approach 1:
The patent segments the EEG processing pipeline into distinct stages: signal acquisition, preprocessing, feature extraction, and control command generation. By dividing the computational task into manageable segments, the system achieves real-time responsiveness while optimizing energy consumption at each stage through targeted processing strategies.
Solution Approach 2:
The patent implements periodic sampling and batch processing of EEG signals rather than continuous real-time analysis of every data point. This periodic approach maintains system responsiveness by processing signals at optimal intervals while significantly reducing overall computational load and energy consumption compared to continuous processing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables real-time, hands-free control of vehicle features, enhancing safety by allowing drivers to maintain focus on the road while interacting with in-vehicle systems.
Implementation Method 1
displayed on a windshield of the vehicle. The machine learning model may then predict which icon of the plurality of icons the driver is viewing based on the EEG data. In this way, the driver may control features of the vehicle without taking hands off the steering wheel or diverting attention from the road for an extended period of time.
Data Source
AI summary
A vehicle system includes a controller programmed to display a plurality of icons on a heads-up-display (HUD) of the vehicle, receive electroencephalography (EEG) data from a driver of the vehicle, perform a Fast Fourier Transform of the EEG data to obtain an EEG spectrum, input the EEG spectrum into a trained machine learning model, determine which of the plurality of icons the driver is viewing based on an output of the trained machine learning model, and perform one or more vehicle operations based on the output of the trained machine learning model.