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Vehicle-to-Vehicle (V2V) safety application research based on short range real-time communication has been researched for over a decade. Examples of V2V applications include Electronic Emergency Brake Light, Do Not Pass Warning, Lane Departure Warning, and Intersection Movement Assist. It is hoped that these applications, whether present as warning or intervention, will help reduce the incidence of traffic collisions, fatalities, injuries, and property damage. The safety benefits of these applications will likely depend on many factors, such as usability, market penetration, driver acceptance, and reliability. Some applications, such as DNPW and IMA, require a longer communication range to be effective. In addition, Dedicated Short Range Communications (DSRC) may be required to communicate without direct line of sight. The signal needs to overcome shadowing effects of other vehicles and buildings that are in the way.

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, a collection of vehicles traveling at high speeds and in close proximity, will require management techniques to ensure safe, efficient, and reliable vehicle interactions. We propose a shared-autonomy approach in which the strengths of both human drivers and machines are employed in concert for this management. A fuzzy logic-based control implementation is combined with a genetic algorithm to select the shared-autonomy architecture and sensor capabilities that optimize swarm operations.