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@@ -1,17 +1,65 @@
-# intel-oneAPI
+ 🚗LoKi
-#### Team Name -
-#### Problem Statement -
-#### Team Leader Email -
+ Github Repository Code Link -: https://github.com/aaditrychoudhury/IntelOneAPI
+Problem Statement - Object Detection For Autonomous Vehicles
-## A Brief of the Prototype:
- This section must include UML Daigrms and prototype description
+This project serves as a showcase of an advanced and refined application of contemporary headlight technology in modern vehicles. Beyond the conventional low and high beams, its purpose is to exemplify the enhanced capabilities of LEDs in promoting safer driving conditions during low light or dark environments. By harnessing the flexibility inherent in LED technology, this project demonstrates the capacity to illuminate a significant portion of the road while ensuring the absence of blinding effects for both oncoming and preceding traffic, thereby prioritizing the safety of all drivers involved.
+
+
+
+## intel-oneAPI:
+#### Team Name - Benaam
+#### Team Leader Email - [Yuvraj Singh Deora -: 06yuvraj2001singh@gmail.com](06yuvraj2001singh@gmail.com)
+
+## How it works:📃
+The goal of this prototype is to offer appropriate road illumination while also decreasing glare for approaching and retracing vehicles. The goal is achieved by integrating two distinct but connected systems into a single functional whole. The first system maintains the LED matrix hardware, while the second identifies moving targets using a video stream and an object detection model. This combination enables the system to detect vehicles, locate them, and adjust the lights for visibility. Using these coordinated parts, the project shows how LEDs may be used to improve road safety.
+
+## Demo Video
+Youtube Video-: https://youtu.be/hCPSCrTQUeg
+
+
+
+ ## Strengths of LoKi 📈:
+- Robust object detection leveraging image processing and ML-DL algorithms
+- Integrated LED matrix within the car's body for effective visual cues
+- Low-cost implementation utilizing affordable hardware components
+- Enhanced driver awareness leading to reduced accident risks
+- Customizable and compatible with other systems for comprehensive driver assistance integration
-## Tech Stack:
- List Down all technologies used to Build the prototype **Clearly mentioning Intel® AI Analytics Toolkits, it's libraries and the SYCL/DCP++ Libraries used**
-
-## Step-by-Step Code Execution Instructions:
- This Section must contain set of instructions required to clone and run the prototype, so that it can be tested and deeply analysed
+## Framework 🔦:
+All necessary technologies used to Build the prototype are:
+* [Arduino IDE](https://www.arduino.cc/en/main/software)
+* [Visual Studio Code](https://visualstudio.microsoft.com/free-developer-offers/)
+* [Python 3.6 or higher](https://www.python.org/downloads/)
+* [Intel® Optimization for TensorFlow](https://www.intel.com/content/www/us/en/developer/tools/oneapi/optimization-for-tensorflow.html#gs.qcv1ln)
+
+## Activity Diagram
+
+
+
+## Architecture Diagram
+
+
+## Steps to Run
+- Fork this repo
+- Clone on your local machine
+```terminal
+https://github.com/aaditrychoudhury/IntelOneAPI.git
+cd IntelOneAPI
+```
+- Open VehicleDetection.py file in any Python Editor
+- HIT Run
-## What I Learned:
+## Learnings 💻 :
Write about the biggest learning you had while developing the prototype
+
+
+## Contributors
+ - [Aaditry Choudhary](https://github.com/aaditrychoudhury)
+ - [Hrithik Purwar](https://github.com/hrithikpurwar)
+ - [Yuvraj Singh Deora](https://github.com/YUVRAJ06singh08deora)
+ - [Khushboo Nijhawan](https://github.com/KhushbooNijhawan)
+- ---
+
+ With :heart: by Team Benaam
+