Abstract: In this work, as part of the SAE and GM AutoDrive Challenge II and currently in our third year, our team focuses on the critical integration of hardware and software for autonomous vehicles. We emphasize the synergy between sensors, actuators, and software algorithms to enable precise, real-time decision-making. Key to our approach is sensor fusion, utilizing LiDAR, radar, cameras, and ultrasonic sensors to ensure safety and efficiency. We apply machine learning and AI for predictive decision-making, navigating complex scenarios with accuracy. Our iterative process, from design to testing, aims for Level 4 autonomy as per SAE Standard J3016™. This effort not only addresses technical challenges but also explores the broader impact on safety, efficiency, and sustainability in autonomous transportation, contributing to the advancement of automated driving systems.

Kelvin K. Kwakye is currently a Ph.D. candidate and a project lead Aggies for Autonomous Auto at North Carolina Agricultural and Technical State University (NCAT). His research interest includes: human factors, machine learning applications and transportation safety. Kelvin's academic journey began with his undergraduate studies in Chemical Engineering at KNUST in Ghana, where he established a solid foundation. He decided to pursue further studies in Industrial and Systems Engineering after completing his undergraduate studies, recognizing the impact this field can have on a wide range of industries. Kelvin's remarkable achievements include being honored with the Cadence Black in Technology scholarship, highlighting his significant impact in the field. Kelvin's desire to use his skills and knowledge to benefit society extends beyond his academic pursuits. He is also active in the community, having volunteered with several organizations in Ghana and the United States.