Lane Change Intention Detection Using Eye Gaze and Traffic Data
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Solution Overview
Problem
Current methods for predicting a driver's intention to perform a lane change maneuver are inadequate as they do not effectively utilize eye gaze data and traffic conditions to ensure safe and timely lane changes.
Innovation Solution
A system that monitors a driver's eye gaze using camera images and combines this data with traffic conditions to predict lane change intentions, learning from the driver's history to control the vehicle accordingly, and confirms the prediction through a human-machine interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system monitors and analyzes driver eye gaze data combined with traffic conditions to predict lane change intentions, then the accuracy of intention prediction is improved, but the system complexity and computational requirements increase
Solution Approach 1:
The system segments the intention prediction task into distinct analytical components: eye gaze direction detection, gaze duration measurement, traffic condition monitoring, and intention classification. This segmentation allows each component to be processed independently, improving prediction accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The system adds temporal dimension by analyzing the duration and sequence of eye gaze patterns, transforming static gaze position data into dynamic behavioral indicators. This dimensional expansion enables more accurate intention prediction by capturing the evolution of driver attention over time, while the modular processing keeps complexity manageable.
2Loss of time
If the system processes real-time eye gaze data and traffic conditions to predict lane change intentions, then the timeliness of lane change execution is improved, but the computational load and processing time increase
Solution Approach 1:
The system performs preliminary analysis of eye gaze patterns continuously during normal driving, building a baseline understanding of driver attention without triggering full prediction processing. When specific gaze patterns indicating lane change intention are detected, the system activates intensive analysis only at that moment, reducing overall computational energy consumption while maintaining timely response.
Solution Approach 2:
The system applies partial processing by focusing computational resources only on the most probable intentions based on initial gaze analysis. Rather than evaluating all possible maneuvers equally, the system concentrates processing power on the specific lane change direction indicated by the driver's gaze, reducing computational load while maintaining prediction timeliness.
3Reliability
If the system uses multiple parameters including eye gaze history and traffic conditions for prediction, then the reliability of safety assessment is improved, but the difficulty of data integration and processing increases
Solution Approach 1:
The system introduces an intermediary processing layer that standardizes and normalizes data from multiple sources including eye gaze trackers, traffic sensors, and vehicle state monitors. This intermediary layer transforms heterogeneous data into a unified format, enabling reliable safety assessment through comprehensive parameter analysis while reducing the difficulty of data integration through systematic preprocessing.
Data Source
AI summary
In various embodiments, methods, systems, and vehicles are provided controlling a vehicle. In an exemplary embodiment, a method includes: monitoring an eye gaze of a driver of the vehicle; monitoring current traffic conditions surrounding the vehicle; predicting an intention of the driver to perform a lane change maneuver based on the eye gaze, a history of the eye gaze of the driver, and the current traffic conditions; and controlling, by the processor, the vehicle based on the predicted intention of the driver to perform a lane change maneuver.