Autonomous vehicles rely on advanced driver-assistance systems (ADAS) that are developed to automate/adapt/enhance vehicle systems for safety and better driving. Safety features are designed to avoid collisions and accidents by offering technologies that alert the driver to potential problems, or to avoid collisions by implementing safeguards and taking over control of the vehicle. Adaptive features may automate lighting, provide adaptive cruise control, automate braking, incorporate GPS/ traffic warnings, connect to smartphones, alert driver to other cars or dangers, keep the driver in the correct lane, or show what is in blind spots. At the same time, the distributed network in vehicles presents a key design challenge to realizing a safe and high performance operation.
The research objective of this project is to design new ADAS techniques that rely on low-power image recognition, machine learning techniques, and smart sensor fusion. The goal is to ultimately design an autonomous vehicle. This project has involved work on real vehicles, to deploy, test, and evaluate ADAS systems for autonomy. Ongoing work is looking into advanced sensor fusion techniques, sensor placement/selection problems, and resource-constrained deep learning for object detection.
J. Tunnell, Z. Asher, S. Pasricha, T. H. Bradley, “Towards Improving Vehicle Fuel Economy with ADAS”, SAE International Journal of Connected and Automated Vehicles, Oct 2018.
V. Kukkala, J. Tunnell, S. Pasricha, “Advanced Driver Assistance Systems: A Path Toward Autonomous Vehicles“, IEEE Consumer Electronics, Vol. 7, Iss. 5, Sept 2018.