Analysis of Human Driver Behavior in Highway Cut-in Scenarios

2017-01-1402

03/28/2017

Features
Event
WCX™ 17: SAE World Congress Experience
Authors Abstract
Content
The rapid development of driver assistance systems, such as lane-departure warning (LDW) and lane-keeping support (LKS), along with widely publicized reports of automated vehicle testing, have created the expectation for an increasing amount of vehicle automation in the near future. As these systems are being phased in, the coexistence of automated vehicles and human-driven vehicles on roadways will be inevitable and necessary. In order to develop automated vehicles that integrate well with those that are operated in traditional ways, an appropriate understanding of human driver behavior in normal traffic situations would be beneficial.
Unlike many research studies that have focused on collision-avoidance maneuvering, this paper analyzes the behavior of human drivers in response to cut-in vehicles moving at similar speeds. Both automated and human-driven vehicles are likely to encounter this scenario in daily highway driving. This research has identified several possible cut-in scenario configurations that can be experienced on the highway. Data have been collected from a diverse pool of human subjects using a driving simulator with preprogrammed scenarios. To understand each driver’s behavior in response to cut-in vehicles, a novel means of visualization and analysis based on relative positions is proposed in this paper. In addition, the paper provides information on driver decision making when encountering cut-in vehicles. The results could be employed as a set of guidelines for vehicle automation system behavior to ensure that they act in a manner consistent with human-driven vehicles.
Meta TagsDetails
DOI
https://doi.org/10.4271/2017-01-1402
Pages
9
Citation
Kim, S., Wang, J., Guenther, D., Heydinger, G. et al., "Analysis of Human Driver Behavior in Highway Cut-in Scenarios," SAE Technical Paper 2017-01-1402, 2017, https://doi.org/10.4271/2017-01-1402.
Additional Details
Publisher
Published
Mar 28, 2017
Product Code
2017-01-1402
Content Type
Technical Paper
Language
English