Cutting-Edge Computer Vision
Harnesses advanced computer vision and sensor fusion for pinpoint lane tracking.
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01
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Lane Drift Alerts
Issues timely warnings when the vehicle veers unintentionally out of its lane.
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Lane Precision
Offers gentle steering adjustments to ensure the vehicle stays centered within the lane.
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04
Boosted Driver Confidence
Elevates driver confidence and significantly lowers the likelihood of lane departure incidents.
APPLICATIONS
Enhanced Lane Safety
Mitigates lane departure accidents by aiding drivers in lane adherence.
Driver Support
Assists drivers in maintaining lane discipline, especially on extended trips or in demanding scenarios.
Autonomous Driving Foundation
Lays the groundwork for autonomous driving capabilities.
Versatile Performance
Effective in diverse driving contexts, encompassing urban streets and highways a like.
DRIVEN BY DATA
BUILDING AN ALGORITHM
For lane shift prediction, equations and models are used for tasks such as distance and velocity estimation, object tracking, control, and path planning within ADAS systems.
The specific values for parameters are determined through calibration and tuning to suit the system's requirements.
Estimation Methodology
Results
Results based on the Convolutional Neural Network (CNN) model
Table Headings Explained
Test Image : image tested for lane detection.
Predicted Lane 1 : the top predicted lane by the CNN model.
Predicted Lane 2 : the second predicted lane by the CNN model.
True Lane : the actual lane in the image, which serves as the ground truth.
Accuracy : the calculated percentage of the model’s correct predictions for the true lane.
