Yukiko Wakita

An optimal mandatory lane change decision model for autonomous vehicles in urban arterials

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.

Comparison of Zipper and Non-Zipper Merging Patterns Near Merging Point of Roads

At the merging point, vehicles on the main road must reduce their velocity to avoid slow merging vehicles from a branch road, which leads to a traffic jam. Some researchers point out the effectiveness of a zipper merging pattern for improving the traffic near the merging point. In this study, the authors discuss the effectiveness of a zipper merging pattern using cellular automata simulation. Firstly, the single and multiple vehicle following models are defined. The stability analysis of the models then gives the parameters. In the simulation, two merging patterns are compared. In the non-zipper merging pattern, two vehicles merge continuously from a branch road. In the zipper merging pattern, two vehicles merge between main road vehicles. The results show that the zipper merging pattern is more effective than the non-zipper merging pattern for reducing the traffic jam near the merging point.

Cellular Automata Simulation of Traffic Jam in Sag Section

A traffic congestion often occurs near a sag section on a freeway. Since a road gradient changes gradually in a sag section, a driver dose not notice it and therefore, a traffic congestion occurs. This paper describes the cellular automata simulation of the traffic flow through a sag section. The simulation is performed by the stochastic velocity model[4]. The results show that the effect of a sag zone to the traffic congestion becomes strong according to the increase of car density and that average velocity tends to decrease not at the sag section but at a little ahead point of sag section.

Road Network Determination by Cellular Automata Traffic Flow Simulation

Braesss Paradox pointed out that, when a new road is added for increasing road capacity, total capacity of whole road network is reduced in any case. It is pointed out by other study that Braesss Paradox disappears at the high traffic density. The aim of this study is to discuss the appearance of Braesss Paradox at the different traffic density. At the different traffic density, the road network is determined so as to maximize the traffic amount by the help of the optimization algorithm and the traffic simulator. The results show that the optimized road network depends on the traffic density.

Effectiveness of Information from Vehicles beyond Nearest Vehicle Ahead for Traffic Flow

A driver usually controls the vehicle according to only the information from the nearest leader vehicle. If the information from the other leader vehicles is also available, the driver can control the vehicle more adequately. The aim of this study is to discuss the effectiveness of the information from the other leader vehicles than the nearest one for the traffic flow. For this purpose, the traffic flow is modeled by using the Chandler-type multi-vehicle-following model. This model changes the vehicle acceleration rate according to the velocity differences between the vehicle and its multileader vehicles. After the model stability analysis, the traffic flow simulation is performed. The results reveal that the stable region of the model parameters expands according to the increase of the number of the leader vehicles.