详情

原文传递 Driver Models for Both Human and Autonomous Vehicles.

题名：	Driver Models for Both Human and Autonomous Vehicles.
作者：	Ozguner, U.; Fisher, D.; Homaifar, A.; Lee, J.; Woods, D.
关键词：	Autonomous vehicles
摘要：	The goal of this project was to understand how multi-agent models of the driver and vehicle can inform design principles for optimized autonomous vehicle systems. In this project, a computational model for human behavior in pre-crash scenarios was developed and investigated. A multi-agent model with both human drivers and autonomous and semi-autonomous vehicles was considered. The model was build upon successful models used in our Defense Advanced Research Projects Agency (DARPA) Grand Challenge vehicles, and also incorporates results from our experience in automotive industry project. This model takes dynamic inputs about the changing situation and behavior of others, and uses mathematical or symbolic processing to carry out the functions required to simulate the perception, attention, cognition, and control behavior of interest. We integrate different component models, including control theory models, decision and judgment models; learning classifier systems, joint human-automation system models, and attention models, to build a comprehensive model needed to make predictions in pre-crash situations, and needed to make quantitative estimates of hypothesized safety improvements. These models have especially been used for investigating lane-change and merge type of activities, where crashes can occur. We have also considered convoy type of operations, a phenomena that we expect to be of increasing importance as driverless fleets become a possibility in the near future. A number of researchers in the Consortium have contributed to the regular joint discussions held. The majority of the activity under this Project was concentrated at OSU and NC A&T, and the Project Report combines two Chapters, summarizing the activities at both locations.
报告类型：	科技报告