title: Optimization of shared autonomy vehicle control architectures for swarm operations.
abstract: The need for greater capacity in automotive transportation (in the midst of constrained resources) and the convergence of key technologies from multiple domains may eventually produce the emergence of a "swarm" concept of operations. The swarm, which is a collection of vehicles traveling at high speeds and in close proximity, will require technology and management techniques to ensure safe, efficient, and reliable vehicle interactions. We propose a shared autonomy control approach, in which the strengths of both human drivers and machines are employed in concert for this management. Building from a fuzzy logic control implementation, optimal architectures for shared autonomy addressing differing classes of drivers (represented by the driver's response time) are developed through a genetic-algorithm-based search for preferred fuzzy rules. Additionally, a form of "phase transition" from a safe to an unsafe swarm architecture as the amount of sensor capability is varied uncovers key insights on the required technology to enable successful shared autonomy for swarm operations.
authors: Department of Aeronautics and Astronautics, Purdue University, West Lafayette, IN 47906, USA. Aaron.J.Sengstacken@jpl.nasa.gov
authors: Aaron J
authors: AJ
authors: Sengstacken
authors: Daniel A
authors: DA
authors: DeLaurentis
authors: Mohammad R
authors: MR
authors: Akbarzadeh-T
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001336:Automobiles; D003661:Decision Support Techniques; D017143:Fuzzy Logic; D012371:Robotics / Q000379:methods*