
Daniel Carruth
Assistant Research Professor
Daniel Carruth is Associate Director for Advanced Vehicle Systems at the Center for Advanced Vehicular Systems and an Associate Research Professor at Mississippi State University. Dr. Carruth earned his B.S. in Computer Science and Engineering in 2001 and his Ph.D. from the Department of Psychology at Mississippi State University in 2008. Dr. Carruth co-chairs a NATO Applied Vehicle Technology Panel research task group (AVT-408) and previously contributed to the AVT-327 and AVT-341 groups focused on modeling and simulation of autonomous military ground vehicles. He is also leading an investigation of standards for modeling and simulation of off-road vehicles for ASAM. Dr. Carruth helped to found and organize the Summit on Advancing Modeling and Simulation for Autonomous Ground Vehicles (SAMS AGV). He has published over 150 conference proceedings and articles in journals such as Sensors, Electronics, Transportation Safety & Security, International Journal of Social Robotics, and International Journal of Industrial Ergonomics. He has received over $25 million in funding from agencies such as DoD, USDA, NIJ, BJA, and others. Dr. Carruth oversees projects in the CAVS DRIVE Lab, Collaborative Lab, and the Electronics and Sensors Lab.
Dr. Carruth’s research interests include modeling and simulation of autonomous ground vehicles in military off-road environments, human interaction with autonomous vehicles, use of UGVs by military and law enforcement, as well as physical and cognitive aspects of human task performance in law enforcement, military, and industrial work. He has active projects generating virtual environments and test standards for military off-road autonomous vehicles and building off-road autonomous vehicle platforms for real-world testing.
Projects
- Evaluating Sensitivity of Autonomous Algorithms to Sensor Error and Environmental Conditions
- Dynamic Task Allocation and Understanding of Situation Awareness Under Different Levels of Autonomy in Closed-Hatch Military Vehicles
- Recognizing and Reconstructing Distorted and Obscured Perceptual Sensor Data Resulting from Soiling of the Sensor
- A Shared Meta-Model Framework to Enable Multi-Directional Reliance for Effective Collaborative Human-Autonomy Teaming
- Enhancing Fault Tolerance and Resilience in Autonomous Ground Vehicles through Detection and Diagnosis of Physical and Digital Attacks on Perception Systems