Microsimulation Modeling of Coordination of Automated Guided Vehicles at Intersections

Abstract

One of the challenges with autonomous vehicles is their performance at intersections. An alternative control method for the coordination of autonomous vehicles at intersections is shown. The proposed approach was grounded in multiple-robot coordination and took into account vehicle dynamics as well as realistic communications constraints. The existing concept of decentralized navigation functions was combined with a sensing model, and a crossing strategy was developed. The simulation results showed that because of the proposed approach, vehicles had smoother trajectories when crossing at a four-way intersection. The proposed method was compared with adaptive traffic lights and roundabouts in terms of throughput. Results showed that using a decentralized navigation function for the coordination of autonomous vehicles improved their performance by reducing energy consumption and pollution emissions.

Review on Driverless Traffic from Management Perspective

“…Obeying these relations, vehicles are able to cross intersections safely. L. Makarem et al [13] proposed an approach based on navigation function to define priority. When the two cars are both in the crossroad area, the vehicle's navigation function will send their own route information to the intersection controller, and the probability of collision between the two cars will be calculated according to the vehicles' locations, driving directions and speed.…”

“…This concept is widely accepted by researchers for its efficiency improvements (throughput, average delay, etc. )and many improved approaches are proposed [10][11][12][13][14][15][16][17], which can be categorized into planning-based approaches [9][10][11][12] and hybrid approaches [13][14][15][16][17]. 1) Planning-based approaches…”

Validation of driving behaviour as a step towards the investigation of Connected and Automated Vehicles by means of driving simulators

“…The chosen measures were the Inter Quartile Range (IQR) value of follower's velocity, relative velocity and spacing; different thresholds have been settled, for every environment, to verify if they affect, or not, the results. The thresholds were [2,3,4] for the relative and follower's velocity, and [15,30,45] for the spacing.…”

“…The tool most used for CAV performance prediction in traffic engineering is microscopic simulation [3,4]. However, while software in the loop can provide an effective nano simulation representation of the automated vehicle, both micro and nano traffic simulation can be inadequate in order to take into account some relevant aspects of the CAVs impact, e.g., it cannot capture the nuances of real drivers interacting with automated vehicles.…”

“…As introduced above, many research papers [3,4,13] focus their attention on the calibration of microscopic traffic simulator to simulate the interaction between vehicles (driverless and not); in these environments a part of the unpredictability of human behaviour (as a driver or road user) is totally lost. A proper precaution could be then to try to involve humans in the simulations by means of virtual reality [14].…”