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Title: Selecting Flow Optimal System Parameters for Automated Driving Systems
Driver assist features such as adaptive cruise control (ACC) and highway assistants are becoming increasingly prevalent on commercially available vehicles. These systems are typically designed for safety and rider comfort. However, these systems are often not designed with the quality of the overall traffic flow in mind. For such a system to be beneficial to the traffic flow, it must be string stable and minimize the inter-vehicle spacing to maximize throughput, while still being safe. We propose a methodology to select autonomous driving system parameters that are both safe and string stable using the existing control framework already implemented on commercially available ACC vehicles. Optimal parameter values are selected via model-based optimization for an example highway assistant controller with path planning.
Authors:
; ;
Award ID(s):
1743772
Publication Date:
NSF-PAR ID:
10195261
Journal Name:
2019 IEEE Intelligent Transportation Systems Conference (ITSC)
Page Range or eLocation-ID:
3776 to 3781
Sponsoring Org:
National Science Foundation
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