An optimal mandatory lane change decision model for autonomous vehicles in urban arterials. Issue 4 (4th July 2017)

• Record Type: Journal Article
• Authors: Cao, Peng; Hu, Yubai; Miwa, Tomio; Wakita, Yukiko; Morikawa, Takayuki; Liu, Xiaobo
• Is Part Of: Journal of intelligent transportation systems. Volume 21:Issue 4(2017)
• Page Start: 271
• Page End: 284
• Publication Date: 2017-07-04
• Subjects: autonomous vehicles -- lane-changing instruction -- urban arterials; Intelligent transportation systems -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.312
• DOI: 10.1080/15472450.2017.1315805
• Languages: English
• ISSNs: 1547-2450

Abstract

ABSTRACT: Autonomous driving has become a popular topic in both industry and academia. Lane-changing is a vital component of autonomous driving behavior in arterial road traffic. Much research has been carried out to investigate discretionary lane changes for autonomous vehicles. However, very little research has been conducted on assisting autonomous vehicles in making mandatory lane changes (MLCs), which is the core of optimal lane-specific route planning for autonomous vehicles. This research aims to determine the best position for providing MLC instruction to autonomous vehicles. In this article, an optimization model is formulated to determine the optimal position at which an instruction to change lanes should be given through automotive navigation systems. First, the distribution of time spent waiting for safe headway to make a lane change is modeled as an exponential distribution. Lane-specific travel times are then calculated for vehicles in various situations by applying traffic shockwave theory and horizontal queuing theory. Finally, the expected travel time is derived for a vehicle receiving a lane change instruction to change lanes at an arbitrary position along the road. The proposed model is validated by a comparison with a simulation model in VISSIM. Additional experiments show that the instruction should be given earlier in the case of denser traffic or higher travel speed in the target lane and that vehicles can save considerable time, if they follow the guidance provided by the proposed model. The proposed model can be applied to guide autonomous vehicles to travel an optimal route.