Eye-Tracking Data Correlation for Vehicle Maneuver Prediction
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Solution Overview
Problem
Autonomous and semi-autonomous vehicles lack the ability to predict the maneuvers of surrounding vehicles, which hinders safe and efficient operation, especially in complex environments with multiple entities.
Innovation Solution
A system that collects eye-movement data using sensors and an eye-movement tracking system, correlates it with vehicle characteristic data, and transmits predicted vehicle maneuvers via V2X communication to nearby vehicles and infrastructure, enabling improved prediction and control of vehicle intentions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If V2X communication is implemented to share maneuver predictions, then safety and efficiency of vehicle operation is improved, but device complexity and infrastructure requirements increase
Solution Approach 1:
The system segments the maneuver prediction functionality into modular components: eye-tracking data collection module, data correlation module, maneuver prediction module, and V2X communication module. This segmentation allows each component to be developed and optimized independently, reducing overall system complexity while maintaining safety benefits.
Solution Approach 2:
The patent introduces an intermediary processing layer that correlates eye-movement data with vehicle characteristic data before generating maneuver predictions. This intermediary step simplifies the communication protocol by pre-processing data locally, reducing the complexity of direct V2X communication requirements while improving safety through more accurate predictions.
2Measurement precision
If eye-movement tracking system is added to capture driver intent, then prediction accuracy of vehicle maneuvers is improved, but device complexity and cost increase
Solution Approach 1:
The eye-movement tracking system is designed to serve multiple functions: capturing driver gaze direction, determining attention focus, detecting intent to maneuver, and monitoring driver state. This multi-functionality justifies the added device complexity by providing comprehensive maneuver prediction capability from a single system integration.
Solution Approach 2:
The patent merges the eye-movement tracking system with the existing vehicle sensor network and controller, integrating multiple data streams (eye movement data, vehicle characteristic data, environment data) into a unified maneuver prediction framework. This merging approach consolidates hardware and software resources, managing complexity while enhancing prediction accuracy.
3Speed
If real-time data collection and processing is performed, then responsiveness to maneuver predictions is improved, but use of energy and computational resources increases
Solution Approach 1:
The system implements periodic sampling of eye-movement data and vehicle characteristic data at optimized intervals, rather than continuous real-time processing. This periodic action maintains responsiveness to maneuver predictions by capturing critical changes while significantly reducing energy consumption and computational resource requirements compared to continuous processing.
Solution Approach 2:
The controller performs preliminary correlation of eye-movement data with vehicle characteristic data in advance, preparing processed information for rapid maneuver prediction. This preliminary action reduces the computational burden during critical decision-making moments, balancing responsiveness with energy efficiency by pre-processing data when computational resources are more abundantly available.
Data Source
AI summary
A method for collecting and mapping eye movement data to vehicle data includes providing a vehicle having a plurality of sensors configured to capture vehicle characteristic data, an eye-movement tracking system configured to capture eye movement data, a wireless communication system, and a controller in communication with the plurality of sensors, the eye movement tracking system, and the wireless communication system, receiving the eye movement data and the vehicle characteristic data, analyzing the eye movement data and the vehicle characteristic data to temporally correlate the eye movement data and the vehicle characteristic data and generate a matched dataset, determining a predicted vehicle maneuver from the matched dataset, and transmitting the predicted vehicle maneuver to a nearby vehicle using V2X communication.