[[TOC(Other/Summer/2022/Active/*, depth=1, heading=Active Driving AI Assistant)]]

= Active Driving AI Assistant =
**WINLAB Summer Internship 2022**

**Group Members:** 

== Project Objective ==

This project study will develop a naturalistic driving monitoring and intervention system. Our interactive system will use multiple in-cabin vehicle sensors to assess driving conditions and performance in real-time and draw a driver’s attention using a voice-based interface to improve their performance. We will use AI-driven techniques to continuously assess the effectiveness of our interventions and make adjustments to maximize driving safety and quality.

'''Reading material:'''

 * [https://dl.acm.org/doi/10.1145/3478125 Tong Wu, Nikolas Martelaro, Simon Stent, Jorge Ortiz, and Wendy Ju. 2021. Learning When Agents Can Talk to Drivers Using the INAGT Dataset and Multisensor Fusion. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 5, 3, Article 133 (Sept 2021), 28 pages.] 
 * [https://iv2022.com/program/conference-program Tong Wu, Enna Sachdeva, Kumar Akash,  Xingwei Wu, Teruhisa Misu, and Jorge Ortiz Toward an Adaptive Situational Awareness Support System for Urban Driving.]

== Week 1 Activities ==
Downloaded videos of car approaching stop sign (Youtube)
Learning Python
Video Processing Using Python (cutting, concentration, noise cancellation etc.)
Work with ROS and Python to implement a program to estimate the total stopping distance of a vehicle based on its speed


== Week 2 Activities ==
Look into achieving a program to calculate distance of car to stop sign at the parking lot outside. Our project will be the first step in a major multi-step project that will allow a car to operate autonomously. 
Familiarize ourselves with NJ MVC Driving Laws
Find related studies and Youtube videos relating to AI detection

== Week 3 Activities ==
Applying Python programming for Object Detection
Designing the AI Model
Implement the videos to YOLO (Real time Object Detection)  
Collect more VIDEOS and compare the theoretical results to experimental results
identify the suitable camera 


== Week 4 Activities ==
Research related Datasets and codes, and create our own dataset
Choose images and check if its correc
Install the required packages & run the code in Python Integrated Environment
Learn image Processing