Selecting Flow Optimal System Parameters for Automated Driving Systems
Title | Selecting Flow Optimal System Parameters for Automated Driving Systems |
Publication Type | Conference Proceedings |
Year of Publication | 2019 |
Authors | Hauer, Florian, Raphael Stern, Alexander Pretschner |
Conference Name | 22nd International Conference on Intelligent Transportation Systems |
Publisher | IEEE |
Conference Location | New Zealand |
Keywords | ACC, adaptive cruise control, automotive, autonomous driving, autonomous driving system parameters, Driver assistance systems, highway assistants, model-based optimization, optimization and control, Safety, simulation and modeling, string stable, traffic, Traffic flow, Transportation |
Abstract | 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. |
Citation Key | node-62090 |