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:

• ​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.
• ​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

Week 5 Activities

Build our own Model to Train the Data Preparing our Data using data generator Visualizing data Early stop and model checkpoint