This project focuses on the development of a human-aware advanced driver assistance system (ADAS) that helps promote safe driving based on a driver's biometrics and driving behavior. The system uses biometric signals from the driver using BioHarness Belt and Galvanic Skin Response (GSR) sensors to monitor the driver's heart rate, breathing rate, ECG and GSR signals. These sensory information are then analyzed by machine learning models to determine whether the driver is stressed or drowsy. The project extended the current state-of-the-art driving simulator CARLA to not only include the driver's brake intensity, speed, throttle and steering but also the driver's biometric state. The vehicle controls and biometrics are then plotted and analyzed for correlations that could imply a driver is driving aggressively, assertively, or defensively. Based on these correlations, the driver will be displayed a warning on the simulation which advises them to drive carefully. The output from this project is an extension to the CARLA tool that developers can use to design and simulate human-aware solutions for ADAS. Faculty Advisor: Professor Salma Elmalaki