Pathvision
“PathVision: Seeing the Road Ahead with AI.”
Created on 25th August 2025
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Pathvision
“PathVision: Seeing the Road Ahead with AI.”
The problem Pathvision solves
🚦 PathVision: Smarter Lane Detection for Safer Roads
🔹 What can people use it for?
Autonomous Vehicles – Provides accurate lane detection to keep cars aligned and reduce accidents.
Driver Assistance Systems (ADAS) – Helps drivers stay in the correct lane, especially in poor visibility.
Traffic Safety Monitoring – Can be integrated into roadside cameras to analyze lane discipline and traffic flow.
Smart Cities – Supports intelligent transportation systems with real-time lane data.
Research & Development – Useful for testing new self-driving or computer vision applications.
🔹 How it makes tasks easier/safer
✅ Reduces human error by assisting drivers with lane departure warnings.
✅ Increases road safety by keeping vehicles in the correct lane under low-light or high-speed conditions.
✅ Improves traffic management by detecting violations like wrong-lane driving.
✅ Scales easily with YOLO-based detection for fast and real-time processing.
Challenges we ran into
🛠️ Challenge We Faced
One of the main hurdles we encountered was detecting lanes accurately under poor lighting and noisy road conditions (e.g., faded lane markings, shadows, and curves). Initially, our model gave inconsistent results because the edges weren’t always clear for the detection algorithm.
💡 How We Overcame It
We preprocessed frames using techniques like Canny edge detection and Gaussian smoothing to enhance lane visibility.
Integrated YOLO-based detection with region-of-interest masking so that the model only focused on the lane area, reducing noise.
Fine-tuned the parameters iteratively, testing on multiple road scenarios (day/night/curved roads).
This combination made PathVision robust and reliable, ensuring consistent lane detection even in challenging real-world environments.
Technologies used