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LanePilot

LanePilot 🚗

GitHub issuesGitHub pull requests

Stargazers repo roster for @AppSolves/LanePilotForkers repo roster for @AppSolves/LanePilot

LanePilot is an AI-powered system that dynamically optimizes traffic flow by analyzing lane utilization and congestion patterns in real time.
It ensures efficient lane allocation to reduce bottlenecks and improve overall road efficiency.

Introduction • Features • Installation • Usage • Customization • Credits • License


LanePilot 🤖

Introduction 📖

Welcome to LanePilot!

LanePilot is an advanced AI-based traffic management system designed to analyze real-time lane usage and congestion, enabling dynamic lane allocation and smarter traffic flow. By leveraging computer vision and deep learning, LanePilot helps reduce bottlenecks, minimize CO₂ emissions, improve road safety, and optimize urban mobility.

Note

For more information, please view the very detailed project documentation (written in German).

Features 🚀

  • Real-Time Lane Detection: Uses AI and computer vision to detect lanes, vehicles, and congestion in real-time.

  • Dynamic Lane Allocation: Automatically suggests or controls lane assignments to optimize traffic flow.

  • Modular Integration: Easily integrates with existing traffic infrastructure and IoT devices.

  • Data Logging & Visualization: Stores and visualizes traffic data for analysis and reporting.

  • Not implemented yet:

    • Vehicle Classification: Identifies vehicle types (e.g., cars, trucks, buses) for tailored traffic management.
    • Customizable Alerts: Notifies operators or drivers about incidents, congestion, or recommended actions.
    • Congestion Analysis: Analyzes traffic patterns and congestion levels to provide insights for urban planners.

LanePilot

Installation 🛠️

Binaries & Packages 📦

If you prefer not to build from source, pre-built binaries and packages are available for various platforms. Check the releases page for the latest versions or run the following commands to download the latest docker images:

  • Raspberry Pi:
curl -sSL https://raw.githubusercontent.com/AppSolves/LanePilot/refs/heads/v2/scripts/compose.sh | bash -s raspberrypi
  • NVIDIA Jetson:
curl -sSL https://raw.githubusercontent.com/AppSolves/LanePilot/refs/heads/v2/scripts/compose.sh | bash -s jetson

Build from Source 🔨

  1. Clone the Repository:
    Clone the repository to your local machine:

    git clone https://github.com/AppSolves/LanePilot.git

  2. Install Dependencies:
    Navigate to the root directory and install the required libraries:

    python -m venv venv
# On Windows:
venv\Scripts\activate
# On Unix/Mac:source venv/bin/activate
pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu130

  3. Install Additional Tools (if needed):

    • Docker:Download here and follow the installation instructions.
    • CUDA, including cuDNN and TensorRT: For GPU acceleration, install the appropriate CUDA version for your GPU. Follow the NVIDIA installation guide for your OS.

  4. Build Docker Images:
    If you wish to build manually, run the scripts/build_opencv.sh and scripts/compose.sh scripts:

    chmod +x scripts/*.sh # Make all helper scripts executable
./scripts/build_opencv.sh # Build the OpenCV image (arm64 only)
./scripts/compose.sh [<platform>]

Important

The Jetson image (more precisely, the opencv_base image) is built without the NVIDIA Video Codec SDK (cudacodec support). This is due to licensing issues with NVIDIA. If you need cudacodec support, please follow the instructions in the relevant Dockerfile and build the image locally using the provided Dockerfile.

Customization 🎨

LanePilot is modular and configurable:

  • Detection Models: Swap or retrain detection models in the models/ directory.
  • Alerts & Actions: Customize alert logic in the common/ or utils/ modules.
  • Configuration Files: Edit the config.yaml files in submodules to adjust settings like model parameters, thresholds, and camera feeds.

Usage 📝

Running LanePilot is as simple as running docker compose on your platform/device:

./scripts/compose.sh [<platform>] # Done ✨

Credits 🙏

This project was developed and is maintained by AppSolves.

Links

License 📜

This project is licensed under a custom license with All Rights Reserved.
No use, distribution, or modification is allowed without explicit permission from the author.

For more information, please see the LICENSE.md file.

LanePilot © 2025 by Kaan Gönüldinc

Conclusion 🎉

Thank you for checking out LanePilot! We hope you find this tool useful for smart traffic management and urban mobility. For questions, feedback, or suggestions, please reach out to us via the provided contact methods. Happy coding!

About

The worlds first real-time AI-powered traffic management system, featuring automated vehicle detection, lane allocation optimization, and dynamic control for (autonomous) cars!

www.appsolves.dev

Topics

aiai-traffic-managementautonomous-drivingcomputer-visioncudaedge-computingembedded-systemsjetson-orin-nano-superlane-detectionpytorch

Resources

Readme
License

Contributing

Contributing
Activity

Stars

4 stars

Watchers

3 watching

Forks

0 forks
Report repository

Releases

Sponsor this project

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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LanePilot
LanePilot 🚗
GitHub issuesGitHub pull requests
Stargazers repo roster for @AppSolves/LanePilotForkers repo roster for @AppSolves/LanePilot
LanePilot is an AI-powered system that dynamically optimizes traffic flow by analyzing lane utilization and congestion patterns in real time.


It ensures efficient lane allocation to reduce bottlenecks and improve overall road efficiency.


Introduction • Features • Installation • Usage • Customization • Credits • License

LanePilot 🤖
Introduction 📖
Welcome to LanePilot!
LanePilot is an advanced AI-based traffic management system designed to analyze real-time lane usage and congestion, enabling dynamic lane allocation and smarter traffic flow. By leveraging computer vision and deep learning, LanePilot helps reduce bottlenecks, minimize CO₂ emissions, improve road safety, and optimize urban mobility.
Note
For more information, please view the very detailed project documentation (written in German).
Features 🚀
  • Real-Time Lane Detection: Uses AI and computer vision to detect lanes, vehicles, and congestion in real-time.
  • Dynamic Lane Allocation: Automatically suggests or controls lane assignments to optimize traffic flow.
  • Modular Integration: Easily integrates with existing traffic infrastructure and IoT devices.
  • Data Logging & Visualization: Stores and visualizes traffic data for analysis and reporting.
  • Not implemented yet:
    • Vehicle Classification: Identifies vehicle types (e.g., cars, trucks, buses) for tailored traffic management.
    • Customizable Alerts: Notifies operators or drivers about incidents, congestion, or recommended actions.
    • Congestion Analysis: Analyzes traffic patterns and congestion levels to provide insights for urban planners.

LanePilot
Installation 🛠️
Binaries & Packages 📦
If you prefer not to build from source, pre-built binaries and packages are available for various platforms. Check the releases page for the latest versions or run the following commands to download the latest docker images:
  • Raspberry Pi:
curl -sSL https://raw.githubusercontent.com/AppSolves/LanePilot/refs/heads/v2/scripts/compose.sh | bash -s raspberrypi
  • NVIDIA Jetson:
curl -sSL https://raw.githubusercontent.com/AppSolves/LanePilot/refs/heads/v2/scripts/compose.sh | bash -s jetson
Build from Source 🔨
  1. Clone the Repository:
    
Clone the repository to your local machine:
    git clone https://github.com/AppSolves/LanePilot.git
  2. Install Dependencies:
    
Navigate to the root directory and install the required libraries:
    python -m venv venv
# On Windows:
venv\Scripts\activate
# On Unix/Mac:source venv/bin/activate
pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu130
  3. Install Additional Tools (if needed):
    • Docker:Download here and follow the installation instructions.
    • CUDA, including cuDNN and TensorRT: For GPU acceleration, install the appropriate CUDA version for your GPU. Follow the NVIDIA installation guide for your OS.
  4. Build Docker Images:
    
If you wish to build manually, run the scripts/build_opencv.sh and scripts/compose.sh scripts:
    chmod +x scripts/*.sh # Make all helper scripts executable
./scripts/build_opencv.sh # Build the OpenCV image (arm64 only)
./scripts/compose.sh [<platform>]
Important
The Jetson image (more precisely, the opencv_base image) is built without the NVIDIA Video Codec SDK (cudacodec support).
This is due to licensing issues with NVIDIA.
If you need cudacodec support, please follow the instructions in the relevant Dockerfile and build the image locally using the provided Dockerfile.
Customization 🎨
LanePilot is modular and configurable:
  • Detection Models: Swap or retrain detection models in the models/ directory.
  • Alerts & Actions: Customize alert logic in the common/ or utils/ modules.
  • Configuration Files: Edit the config.yaml files in submodules to adjust settings like model parameters, thresholds, and camera feeds.
Usage 📝
Running LanePilot is as simple as running docker compose on your platform/device:
./scripts/compose.sh [<platform>] # Done ✨
Credits 🙏
This project was developed and is maintained by AppSolves.
Links
License 📜
This project is licensed under a custom license with All Rights Reserved.

No use, distribution, or modification is allowed without explicit permission from the author.
For more information, please see the LICENSE.md file.
LanePilot © 2025 by Kaan Gönüldinc
Conclusion 🎉
Thank you for checking out LanePilot! We hope you find this tool useful for smart traffic management and urban mobility. For questions, feedback, or suggestions, please reach out to us via the provided contact methods. Happy coding!
About
The worlds first real-time AI-powered traffic management system, featuring automated vehicle detection, lane allocation optimization, and dynamic control for (autonomous) cars!
www.appsolves.dev
Topics
aiai-traffic-managementautonomous-drivingcomputer-visioncudaedge-computingembedded-systemsjetson-orin-nano-superlane-detectionpytorch
Resources
Readme
License
Contributing
Contributing
Activity
Stars
4 stars
Watchers
3 watching
Forks
0 forks
Report repository
Releases
Sponsor this project
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
LanePilot
LanePilot 🚗
GitHub issuesGitHub pull requests
Stargazers repo roster for @AppSolves/LanePilotForkers repo roster for @AppSolves/LanePilot
LanePilot is an AI-powered system that dynamically optimizes traffic flow by analyzing lane utilization and congestion patterns in real time.


It ensures efficient lane allocation to reduce bottlenecks and improve overall road efficiency.


Introduction • Features • Installation • Usage • Customization • Credits • License

LanePilot 🤖
Introduction 📖
Welcome to LanePilot!
LanePilot is an advanced AI-based traffic management system designed to analyze real-time lane usage and congestion, enabling dynamic lane allocation and smarter traffic flow. By leveraging computer vision and deep learning, LanePilot helps reduce bottlenecks, minimize CO₂ emissions, improve road safety, and optimize urban mobility.
Note
For more information, please view the very detailed project documentation (written in German).
Features 🚀
  • Real-Time Lane Detection: Uses AI and computer vision to detect lanes, vehicles, and congestion in real-time.
  • Dynamic Lane Allocation: Automatically suggests or controls lane assignments to optimize traffic flow.
  • Modular Integration: Easily integrates with existing traffic infrastructure and IoT devices.
  • Data Logging & Visualization: Stores and visualizes traffic data for analysis and reporting.
  • Not implemented yet:
    • Vehicle Classification: Identifies vehicle types (e.g., cars, trucks, buses) for tailored traffic management.
    • Customizable Alerts: Notifies operators or drivers about incidents, congestion, or recommended actions.
    • Congestion Analysis: Analyzes traffic patterns and congestion levels to provide insights for urban planners.

LanePilot
Installation 🛠️
Binaries & Packages 📦
If you prefer not to build from source, pre-built binaries and packages are available for various platforms. Check the releases page for the latest versions or run the following commands to download the latest docker images:
  • Raspberry Pi:
curl -sSL https://raw.githubusercontent.com/AppSolves/LanePilot/refs/heads/v2/scripts/compose.sh | bash -s raspberrypi
  • NVIDIA Jetson:
curl -sSL https://raw.githubusercontent.com/AppSolves/LanePilot/refs/heads/v2/scripts/compose.sh | bash -s jetson
Build from Source 🔨
  1. Clone the Repository:
    
Clone the repository to your local machine:
    git clone https://github.com/AppSolves/LanePilot.git
  2. Install Dependencies:
    
Navigate to the root directory and install the required libraries:
    python -m venv venv
# On Windows:
venv\Scripts\activate
# On Unix/Mac:source venv/bin/activate
pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu130
  3. Install Additional Tools (if needed):
    • Docker:Download here and follow the installation instructions.
    • CUDA, including cuDNN and TensorRT: For GPU acceleration, install the appropriate CUDA version for your GPU. Follow the NVIDIA installation guide for your OS.
  4. Build Docker Images:
    
If you wish to build manually, run the scripts/build_opencv.sh and scripts/compose.sh scripts:
    chmod +x scripts/*.sh # Make all helper scripts executable
./scripts/build_opencv.sh # Build the OpenCV image (arm64 only)
./scripts/compose.sh [<platform>]
Important
The Jetson image (more precisely, the opencv_base image) is built without the NVIDIA Video Codec SDK (cudacodec support).
This is due to licensing issues with NVIDIA.
If you need cudacodec support, please follow the instructions in the relevant Dockerfile and build the image locally using the provided Dockerfile.
Customization 🎨
LanePilot is modular and configurable:
  • Detection Models: Swap or retrain detection models in the models/ directory.
  • Alerts & Actions: Customize alert logic in the common/ or utils/ modules.
  • Configuration Files: Edit the config.yaml files in submodules to adjust settings like model parameters, thresholds, and camera feeds.
Usage 📝
Running LanePilot is as simple as running docker compose on your platform/device:
./scripts/compose.sh [<platform>] # Done ✨
Credits 🙏
This project was developed and is maintained by AppSolves.
Links
License 📜
This project is licensed under a custom license with All Rights Reserved.

No use, distribution, or modification is allowed without explicit permission from the author.
For more information, please see the LICENSE.md file.
LanePilot © 2025 by Kaan Gönüldinc
Conclusion 🎉
Thank you for checking out LanePilot! We hope you find this tool useful for smart traffic management and urban mobility. For questions, feedback, or suggestions, please reach out to us via the provided contact methods. Happy coding!
About
The worlds first real-time AI-powered traffic management system, featuring automated vehicle detection, lane allocation optimization, and dynamic control for (autonomous) cars!
www.appsolves.dev
Topics
aiai-traffic-managementautonomous-drivingcomputer-visioncudaedge-computingembedded-systemsjetson-orin-nano-superlane-detectionpytorch
Resources
Readme
License
Contributing
Contributing
Activity
Stars
4 stars
Watchers
3 watching
Forks
0 forks
Report repository
Releases
Sponsor this project
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
LanePilot
LanePilot 🚗
GitHub issuesGitHub pull requests
Stargazers repo roster for @AppSolves/LanePilotForkers repo roster for @AppSolves/LanePilot
LanePilot is an AI-powered system that dynamically optimizes traffic flow by analyzing lane utilization and congestion patterns in real time.


It ensures efficient lane allocation to reduce bottlenecks and improve overall road efficiency.


Introduction • Features • Installation • Usage • Customization • Credits • License

LanePilot 🤖
Introduction 📖
Welcome to LanePilot!
LanePilot is an advanced AI-based traffic management system designed to analyze real-time lane usage and congestion, enabling dynamic lane allocation and smarter traffic flow. By leveraging computer vision and deep learning, LanePilot helps reduce bottlenecks, minimize CO₂ emissions, improve road safety, and optimize urban mobility.
Note
For more information, please view the very detailed project documentation (written in German).
Features 🚀
  • Real-Time Lane Detection: Uses AI and computer vision to detect lanes, vehicles, and congestion in real-time.
  • Dynamic Lane Allocation: Automatically suggests or controls lane assignments to optimize traffic flow.
  • Modular Integration: Easily integrates with existing traffic infrastructure and IoT devices.
  • Data Logging & Visualization: Stores and visualizes traffic data for analysis and reporting.
  • Not implemented yet:
    • Vehicle Classification: Identifies vehicle types (e.g., cars, trucks, buses) for tailored traffic management.
    • Customizable Alerts: Notifies operators or drivers about incidents, congestion, or recommended actions.
    • Congestion Analysis: Analyzes traffic patterns and congestion levels to provide insights for urban planners.

LanePilot
Installation 🛠️
Binaries & Packages 📦
If you prefer not to build from source, pre-built binaries and packages are available for various platforms. Check the releases page for the latest versions or run the following commands to download the latest docker images:
  • Raspberry Pi:
curl -sSL https://raw.githubusercontent.com/AppSolves/LanePilot/refs/heads/v2/scripts/compose.sh | bash -s raspberrypi
  • NVIDIA Jetson:
curl -sSL https://raw.githubusercontent.com/AppSolves/LanePilot/refs/heads/v2/scripts/compose.sh | bash -s jetson
Build from Source 🔨
  1. Clone the Repository:
    
Clone the repository to your local machine:
    git clone https://github.com/AppSolves/LanePilot.git
  2. Install Dependencies:
    
Navigate to the root directory and install the required libraries:
    python -m venv venv
# On Windows:
venv\Scripts\activate
# On Unix/Mac:source venv/bin/activate
pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu130
  3. Install Additional Tools (if needed):
    • Docker:Download here and follow the installation instructions.
    • CUDA, including cuDNN and TensorRT: For GPU acceleration, install the appropriate CUDA version for your GPU. Follow the NVIDIA installation guide for your OS.
  4. Build Docker Images:
    
If you wish to build manually, run the scripts/build_opencv.sh and scripts/compose.sh scripts:
    chmod +x scripts/*.sh # Make all helper scripts executable
./scripts/build_opencv.sh # Build the OpenCV image (arm64 only)
./scripts/compose.sh [<platform>]
Important
The Jetson image (more precisely, the opencv_base image) is built without the NVIDIA Video Codec SDK (cudacodec support).
This is due to licensing issues with NVIDIA.
If you need cudacodec support, please follow the instructions in the relevant Dockerfile and build the image locally using the provided Dockerfile.
Customization 🎨
LanePilot is modular and configurable:
  • Detection Models: Swap or retrain detection models in the models/ directory.
  • Alerts & Actions: Customize alert logic in the common/ or utils/ modules.
  • Configuration Files: Edit the config.yaml files in submodules to adjust settings like model parameters, thresholds, and camera feeds.
Usage 📝
Running LanePilot is as simple as running docker compose on your platform/device:
./scripts/compose.sh [<platform>] # Done ✨
Credits 🙏
This project was developed and is maintained by AppSolves.
Links
License 📜
This project is licensed under a custom license with All Rights Reserved.

No use, distribution, or modification is allowed without explicit permission from the author.
For more information, please see the LICENSE.md file.
LanePilot © 2025 by Kaan Gönüldinc
Conclusion 🎉
Thank you for checking out LanePilot! We hope you find this tool useful for smart traffic management and urban mobility. For questions, feedback, or suggestions, please reach out to us via the provided contact methods. Happy coding!
About
The worlds first real-time AI-powered traffic management system, featuring automated vehicle detection, lane allocation optimization, and dynamic control for (autonomous) cars!
www.appsolves.dev
Topics
aiai-traffic-managementautonomous-drivingcomputer-visioncudaedge-computingembedded-systemsjetson-orin-nano-superlane-detectionpytorch
Resources
Readme
License
Contributing
Contributing
Activity
Stars
4 stars
Watchers
3 watching
Forks
0 forks
Report repository
Releases
Sponsor this project
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
LanePilot
LanePilot 🚗
GitHub issuesGitHub pull requests
Stargazers repo roster for @AppSolves/LanePilotForkers repo roster for @AppSolves/LanePilot
LanePilot is an AI-powered system that dynamically optimizes traffic flow by analyzing lane utilization and congestion patterns in real time.


It ensures efficient lane allocation to reduce bottlenecks and improve overall road efficiency.


Introduction • Features • Installation • Usage • Customization • Credits • License

LanePilot 🤖
Introduction 📖
Welcome to LanePilot!
LanePilot is an advanced AI-based traffic management system designed to analyze real-time lane usage and congestion, enabling dynamic lane allocation and smarter traffic flow. By leveraging computer vision and deep learning, LanePilot helps reduce bottlenecks, minimize CO₂ emissions, improve road safety, and optimize urban mobility.
Note
For more information, please view the very detailed project documentation (written in German).
Features 🚀
  • Real-Time Lane Detection: Uses AI and computer vision to detect lanes, vehicles, and congestion in real-time.
  • Dynamic Lane Allocation: Automatically suggests or controls lane assignments to optimize traffic flow.
  • Modular Integration: Easily integrates with existing traffic infrastructure and IoT devices.
  • Data Logging & Visualization: Stores and visualizes traffic data for analysis and reporting.
  • Not implemented yet:
    • Vehicle Classification: Identifies vehicle types (e.g., cars, trucks, buses) for tailored traffic management.
    • Customizable Alerts: Notifies operators or drivers about incidents, congestion, or recommended actions.
    • Congestion Analysis: Analyzes traffic patterns and congestion levels to provide insights for urban planners.

LanePilot
Installation 🛠️
Binaries & Packages 📦
If you prefer not to build from source, pre-built binaries and packages are available for various platforms. Check the releases page for the latest versions or run the following commands to download the latest docker images:
  • Raspberry Pi:
curl -sSL https://raw.githubusercontent.com/AppSolves/LanePilot/refs/heads/v2/scripts/compose.sh | bash -s raspberrypi
  • NVIDIA Jetson:
curl -sSL https://raw.githubusercontent.com/AppSolves/LanePilot/refs/heads/v2/scripts/compose.sh | bash -s jetson
Build from Source 🔨
  1. Clone the Repository:
    
Clone the repository to your local machine:
    git clone https://github.com/AppSolves/LanePilot.git
  2. Install Dependencies:
    
Navigate to the root directory and install the required libraries:
    python -m venv venv
# On Windows:
venv\Scripts\activate
# On Unix/Mac:source venv/bin/activate
pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu130
  3. Install Additional Tools (if needed):
    • Docker:Download here and follow the installation instructions.
    • CUDA, including cuDNN and TensorRT: For GPU acceleration, install the appropriate CUDA version for your GPU. Follow the NVIDIA installation guide for your OS.
  4. Build Docker Images:
    
If you wish to build manually, run the scripts/build_opencv.sh and scripts/compose.sh scripts:
    chmod +x scripts/*.sh # Make all helper scripts executable
./scripts/build_opencv.sh # Build the OpenCV image (arm64 only)
./scripts/compose.sh [<platform>]
Important
The Jetson image (more precisely, the opencv_base image) is built without the NVIDIA Video Codec SDK (cudacodec support).
This is due to licensing issues with NVIDIA.
If you need cudacodec support, please follow the instructions in the relevant Dockerfile and build the image locally using the provided Dockerfile.
Customization 🎨
LanePilot is modular and configurable:
  • Detection Models: Swap or retrain detection models in the models/ directory.
  • Alerts & Actions: Customize alert logic in the common/ or utils/ modules.
  • Configuration Files: Edit the config.yaml files in submodules to adjust settings like model parameters, thresholds, and camera feeds.
Usage 📝
Running LanePilot is as simple as running docker compose on your platform/device:
./scripts/compose.sh [<platform>] # Done ✨
Credits 🙏
This project was developed and is maintained by AppSolves.
Links
License 📜
This project is licensed under a custom license with All Rights Reserved.

No use, distribution, or modification is allowed without explicit permission from the author.
For more information, please see the LICENSE.md file.
LanePilot © 2025 by Kaan Gönüldinc
Conclusion 🎉
Thank you for checking out LanePilot! We hope you find this tool useful for smart traffic management and urban mobility. For questions, feedback, or suggestions, please reach out to us via the provided contact methods. Happy coding!
About
The worlds first real-time AI-powered traffic management system, featuring automated vehicle detection, lane allocation optimization, and dynamic control for (autonomous) cars!
www.appsolves.dev
Topics
aiai-traffic-managementautonomous-drivingcomputer-visioncudaedge-computingembedded-systemsjetson-orin-nano-superlane-detectionpytorch
Resources
Readme
License
Contributing
Contributing
Activity
Stars
4 stars
Watchers
3 watching
Forks
0 forks
Report repository
Releases
Sponsor this project
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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LanePilot
LanePilot 🚗
GitHub issuesGitHub pull requests
Stargazers repo roster for @AppSolves/LanePilotForkers repo roster for @AppSolves/LanePilot
LanePilot is an AI-powered system that dynamically optimizes traffic flow by analyzing lane utilization and congestion patterns in real time.


It ensures efficient lane allocation to reduce bottlenecks and improve overall road efficiency.


Introduction • Features • Installation • Usage • Customization • Credits • License

LanePilot 🤖
Introduction 📖
Welcome to LanePilot!
LanePilot is an advanced AI-based traffic management system designed to analyze real-time lane usage and congestion, enabling dynamic lane allocation and smarter traffic flow. By leveraging computer vision and deep learning, LanePilot helps reduce bottlenecks, minimize CO₂ emissions, improve road safety, and optimize urban mobility.
Note
For more information, please view the very detailed project documentation (written in German).
Features 🚀
  • Real-Time Lane Detection: Uses AI and computer vision to detect lanes, vehicles, and congestion in real-time.
  • Dynamic Lane Allocation: Automatically suggests or controls lane assignments to optimize traffic flow.
  • Modular Integration: Easily integrates with existing traffic infrastructure and IoT devices.
  • Data Logging & Visualization: Stores and visualizes traffic data for analysis and reporting.
  • Not implemented yet:
    • Vehicle Classification: Identifies vehicle types (e.g., cars, trucks, buses) for tailored traffic management.
    • Customizable Alerts: Notifies operators or drivers about incidents, congestion, or recommended actions.
    • Congestion Analysis: Analyzes traffic patterns and congestion levels to provide insights for urban planners.

LanePilot
Installation 🛠️
Binaries & Packages 📦
If you prefer not to build from source, pre-built binaries and packages are available for various platforms. Check the releases page for the latest versions or run the following commands to download the latest docker images:
  • Raspberry Pi:
curl -sSL https://raw.githubusercontent.com/AppSolves/LanePilot/refs/heads/v2/scripts/compose.sh | bash -s raspberrypi
  • NVIDIA Jetson:
curl -sSL https://raw.githubusercontent.com/AppSolves/LanePilot/refs/heads/v2/scripts/compose.sh | bash -s jetson
Build from Source 🔨
  1. Clone the Repository:
    
Clone the repository to your local machine:
    git clone https://github.com/AppSolves/LanePilot.git
  2. Install Dependencies:
    
Navigate to the root directory and install the required libraries:
    python -m venv venv
# On Windows:
venv\Scripts\activate
# On Unix/Mac:source venv/bin/activate
pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu130
  3. Install Additional Tools (if needed):
    • Docker:Download here and follow the installation instructions.
    • CUDA, including cuDNN and TensorRT: For GPU acceleration, install the appropriate CUDA version for your GPU. Follow the NVIDIA installation guide for your OS.
  4. Build Docker Images:
    
If you wish to build manually, run the scripts/build_opencv.sh and scripts/compose.sh scripts:
    chmod +x scripts/*.sh # Make all helper scripts executable
./scripts/build_opencv.sh # Build the OpenCV image (arm64 only)
./scripts/compose.sh [<platform>]
Important
The Jetson image (more precisely, the opencv_base image) is built without the NVIDIA Video Codec SDK (cudacodec support).
This is due to licensing issues with NVIDIA.
If you need cudacodec support, please follow the instructions in the relevant Dockerfile and build the image locally using the provided Dockerfile.
Customization 🎨
LanePilot is modular and configurable:
  • Detection Models: Swap or retrain detection models in the models/ directory.
  • Alerts & Actions: Customize alert logic in the common/ or utils/ modules.
  • Configuration Files: Edit the config.yaml files in submodules to adjust settings like model parameters, thresholds, and camera feeds.
Usage 📝
Running LanePilot is as simple as running docker compose on your platform/device:
./scripts/compose.sh [<platform>] # Done ✨
Credits 🙏
This project was developed and is maintained by AppSolves.
Links
License 📜
This project is licensed under a custom license with All Rights Reserved.

No use, distribution, or modification is allowed without explicit permission from the author.
For more information, please see the LICENSE.md file.
LanePilot © 2025 by Kaan Gönüldinc
Conclusion 🎉
Thank you for checking out LanePilot! We hope you find this tool useful for smart traffic management and urban mobility. For questions, feedback, or suggestions, please reach out to us via the provided contact methods. Happy coding!
About
The worlds first real-time AI-powered traffic management system, featuring automated vehicle detection, lane allocation optimization, and dynamic control for (autonomous) cars!
www.appsolves.dev
Topics
aiai-traffic-managementautonomous-drivingcomputer-visioncudaedge-computingembedded-systemsjetson-orin-nano-superlane-detectionpytorch
Resources
Readme
License
Contributing
Contributing
Activity
Stars
4 stars
Watchers
3 watching
Forks
0 forks
Report repository
Releases
Sponsor this project
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
LanePilot
LanePilot 🚗
GitHub issuesGitHub pull requests
Stargazers repo roster for @AppSolves/LanePilotForkers repo roster for @AppSolves/LanePilot
LanePilot is an AI-powered system that dynamically optimizes traffic flow by analyzing lane utilization and congestion patterns in real time.


It ensures efficient lane allocation to reduce bottlenecks and improve overall road efficiency.


Introduction • Features • Installation • Usage • Customization • Credits • License

LanePilot 🤖
Introduction 📖
Welcome to LanePilot!
LanePilot is an advanced AI-based traffic management system designed to analyze real-time lane usage and congestion, enabling dynamic lane allocation and smarter traffic flow. By leveraging computer vision and deep learning, LanePilot helps reduce bottlenecks, minimize CO₂ emissions, improve road safety, and optimize urban mobility.
Note
For more information, please view the very detailed project documentation (written in German).
Features 🚀
  • Real-Time Lane Detection: Uses AI and computer vision to detect lanes, vehicles, and congestion in real-time.
  • Dynamic Lane Allocation: Automatically suggests or controls lane assignments to optimize traffic flow.
  • Modular Integration: Easily integrates with existing traffic infrastructure and IoT devices.
  • Data Logging & Visualization: Stores and visualizes traffic data for analysis and reporting.
  • Not implemented yet:
    • Vehicle Classification: Identifies vehicle types (e.g., cars, trucks, buses) for tailored traffic management.
    • Customizable Alerts: Notifies operators or drivers about incidents, congestion, or recommended actions.
    • Congestion Analysis: Analyzes traffic patterns and congestion levels to provide insights for urban planners.

LanePilot
Installation 🛠️
Binaries & Packages 📦
If you prefer not to build from source, pre-built binaries and packages are available for various platforms. Check the releases page for the latest versions or run the following commands to download the latest docker images:
  • Raspberry Pi:
curl -sSL https://raw.githubusercontent.com/AppSolves/LanePilot/refs/heads/v2/scripts/compose.sh | bash -s raspberrypi
  • NVIDIA Jetson:
curl -sSL https://raw.githubusercontent.com/AppSolves/LanePilot/refs/heads/v2/scripts/compose.sh | bash -s jetson
Build from Source 🔨
  1. Clone the Repository:
    
Clone the repository to your local machine:
    git clone https://github.com/AppSolves/LanePilot.git
  2. Install Dependencies:
    
Navigate to the root directory and install the required libraries:
    python -m venv venv
# On Windows:
venv\Scripts\activate
# On Unix/Mac:source venv/bin/activate
pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu130
  3. Install Additional Tools (if needed):
    • Docker:Download here and follow the installation instructions.
    • CUDA, including cuDNN and TensorRT: For GPU acceleration, install the appropriate CUDA version for your GPU. Follow the NVIDIA installation guide for your OS.
  4. Build Docker Images:
    
If you wish to build manually, run the scripts/build_opencv.sh and scripts/compose.sh scripts:
    chmod +x scripts/*.sh # Make all helper scripts executable
./scripts/build_opencv.sh # Build the OpenCV image (arm64 only)
./scripts/compose.sh [<platform>]
Important
The Jetson image (more precisely, the opencv_base image) is built without the NVIDIA Video Codec SDK (cudacodec support).
This is due to licensing issues with NVIDIA.
If you need cudacodec support, please follow the instructions in the relevant Dockerfile and build the image locally using the provided Dockerfile.
Customization 🎨
LanePilot is modular and configurable:
  • Detection Models: Swap or retrain detection models in the models/ directory.
  • Alerts & Actions: Customize alert logic in the common/ or utils/ modules.
  • Configuration Files: Edit the config.yaml files in submodules to adjust settings like model parameters, thresholds, and camera feeds.
Usage 📝
Running LanePilot is as simple as running docker compose on your platform/device:
./scripts/compose.sh [<platform>] # Done ✨
Credits 🙏
This project was developed and is maintained by AppSolves.
Links
License 📜
This project is licensed under a custom license with All Rights Reserved.

No use, distribution, or modification is allowed without explicit permission from the author.
For more information, please see the LICENSE.md file.
LanePilot © 2025 by Kaan Gönüldinc
Conclusion 🎉
Thank you for checking out LanePilot! We hope you find this tool useful for smart traffic management and urban mobility. For questions, feedback, or suggestions, please reach out to us via the provided contact methods. Happy coding!
About
The worlds first real-time AI-powered traffic management system, featuring automated vehicle detection, lane allocation optimization, and dynamic control for (autonomous) cars!
www.appsolves.dev
Topics
aiai-traffic-managementautonomous-drivingcomputer-visioncudaedge-computingembedded-systemsjetson-orin-nano-superlane-detectionpytorch
Resources
Readme
License
Contributing
Contributing
Activity
Stars
4 stars
Watchers
3 watching
Forks
0 forks
Report repository
Releases
Sponsor this project
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
LanePilot
LanePilot 🚗
GitHub issuesGitHub pull requests
Stargazers repo roster for @AppSolves/LanePilotForkers repo roster for @AppSolves/LanePilot
LanePilot is an AI-powered system that dynamically optimizes traffic flow by analyzing lane utilization and congestion patterns in real time.


It ensures efficient lane allocation to reduce bottlenecks and improve overall road efficiency.


Introduction • Features • Installation • Usage • Customization • Credits • License

LanePilot 🤖
Introduction 📖
Welcome to LanePilot!
LanePilot is an advanced AI-based traffic management system designed to analyze real-time lane usage and congestion, enabling dynamic lane allocation and smarter traffic flow. By leveraging computer vision and deep learning, LanePilot helps reduce bottlenecks, minimize CO₂ emissions, improve road safety, and optimize urban mobility.
Note
For more information, please view the very detailed project documentation (written in German).
Features 🚀
  • Real-Time Lane Detection: Uses AI and computer vision to detect lanes, vehicles, and congestion in real-time.
  • Dynamic Lane Allocation: Automatically suggests or controls lane assignments to optimize traffic flow.
  • Modular Integration: Easily integrates with existing traffic infrastructure and IoT devices.
  • Data Logging & Visualization: Stores and visualizes traffic data for analysis and reporting.
  • Not implemented yet:
    • Vehicle Classification: Identifies vehicle types (e.g., cars, trucks, buses) for tailored traffic management.
    • Customizable Alerts: Notifies operators or drivers about incidents, congestion, or recommended actions.
    • Congestion Analysis: Analyzes traffic patterns and congestion levels to provide insights for urban planners.

LanePilot
Installation 🛠️
Binaries & Packages 📦
If you prefer not to build from source, pre-built binaries and packages are available for various platforms. Check the releases page for the latest versions or run the following commands to download the latest docker images:
  • Raspberry Pi:
curl -sSL https://raw.githubusercontent.com/AppSolves/LanePilot/refs/heads/v2/scripts/compose.sh | bash -s raspberrypi
  • NVIDIA Jetson:
curl -sSL https://raw.githubusercontent.com/AppSolves/LanePilot/refs/heads/v2/scripts/compose.sh | bash -s jetson
Build from Source 🔨
  1. Clone the Repository:
    
Clone the repository to your local machine:
    git clone https://github.com/AppSolves/LanePilot.git
  2. Install Dependencies:
    
Navigate to the root directory and install the required libraries:
    python -m venv venv
# On Windows:
venv\Scripts\activate
# On Unix/Mac:source venv/bin/activate
pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu130
  3. Install Additional Tools (if needed):
    • Docker:Download here and follow the installation instructions.
    • CUDA, including cuDNN and TensorRT: For GPU acceleration, install the appropriate CUDA version for your GPU. Follow the NVIDIA installation guide for your OS.
  4. Build Docker Images:
    
If you wish to build manually, run the scripts/build_opencv.sh and scripts/compose.sh scripts:
    chmod +x scripts/*.sh # Make all helper scripts executable
./scripts/build_opencv.sh # Build the OpenCV image (arm64 only)
./scripts/compose.sh [<platform>]
Important
The Jetson image (more precisely, the opencv_base image) is built without the NVIDIA Video Codec SDK (cudacodec support).
This is due to licensing issues with NVIDIA.
If you need cudacodec support, please follow the instructions in the relevant Dockerfile and build the image locally using the provided Dockerfile.
Customization 🎨
LanePilot is modular and configurable:
  • Detection Models: Swap or retrain detection models in the models/ directory.
  • Alerts & Actions: Customize alert logic in the common/ or utils/ modules.
  • Configuration Files: Edit the config.yaml files in submodules to adjust settings like model parameters, thresholds, and camera feeds.
Usage 📝
Running LanePilot is as simple as running docker compose on your platform/device:
./scripts/compose.sh [<platform>] # Done ✨
Credits 🙏
This project was developed and is maintained by AppSolves.
Links
License 📜
This project is licensed under a custom license with All Rights Reserved.

No use, distribution, or modification is allowed without explicit permission from the author.
For more information, please see the LICENSE.md file.
LanePilot © 2025 by Kaan Gönüldinc
Conclusion 🎉
Thank you for checking out LanePilot! We hope you find this tool useful for smart traffic management and urban mobility. For questions, feedback, or suggestions, please reach out to us via the provided contact methods. Happy coding!
About
The worlds first real-time AI-powered traffic management system, featuring automated vehicle detection, lane allocation optimization, and dynamic control for (autonomous) cars!
www.appsolves.dev
Topics
aiai-traffic-managementautonomous-drivingcomputer-visioncudaedge-computingembedded-systemsjetson-orin-nano-superlane-detectionpytorch
Resources
Readme
License
Contributing
Contributing
Activity
Stars
4 stars
Watchers
3 watching
Forks
0 forks
Report repository
Releases
Sponsor this project
Packages
Used by
Contributors
Languages