Cooperative driving at isolated intersections based on the optimal minimization of the maximum exit time

Abstract

Traditional traffic control systems based on traffic light have achieved a great success in reducing the average delay of vehicles or in improving the traffic capacity. The main idea of these systems is based on the optimization of the cycle time, the phase sequence, and the phase duration. The right-of-ways are assigned to vehicles of one or several movements for a specific time. With the emergence of cooperative driving, an innovative traffic control concept, Autonomous Intersection Management (AIM), has emerged. In the framework of AIM, the right-of-way is customized on the measurement of the vehicle state and the traffic control turns to determine the passing sequence of vehicles. Since each vehicle is considered individually, AIM faces a combinatorial optimization problem. This paper proposes a dynamic programming algorithm to find its optimal solution in polynomial time. Experimental results obtained by simulation show that the proper arrangement of the vehicle passing sequence can greatly improve traffic efficiency at intersections.

Optimizing Signalized Intersections Performance Under Conventional and Automated Vehicles Traffic

“…In subsequent publications, the AIM was extended to: make the control policy compatible with communicative semi-autonomous vehicles, (Au et al (2015); Dresner & Stone (2006a,b)); manage priorities for side traffic under unbalanced demand, (Au et al (2011)); improve estimation of vehicle arrivals to the stop bar, (Au & Stone (2010)); and coordinate a network of multiple interconnected intersections, (Hausknecht et al (2011)). Several other studies also supported intersection control logics without the presence of the traditional signal (Elhenawy et al (2015); Tachet et al (2016); Wu et al (2013); Yan et al (2009)). These types of algorithms may have limitations on minimizing delay because of: (1) frequent switches of right-of-way may disrupt platoons resulting in higher total travel time for the intersection; (2) eliminating traffic signal heads makes the system less expectable for conventional vehicles; (3) they do not consider the additional benefit stemming from optimizing AV trajectories.…”

Traffic management system for smart road networks reserved for self‐driving cars

“…current position, speed and destination) and to send guidance instructions; to do that, a constrained nonlinear optimisation problem that includes a risk function is solved for all vehicles in a model predictive control framework, in order to evaluate the optimal trajectories to be followed by vehicles to cross the intersection safely without much drop in their velocities. Other solutions under the framework of Autonomous Intersection Management (AIM) are [16] where a genetic algorithm to find an optimal or a near-optimal vehicle passing sequence for adjacent intersections is defined; [17] that proposes an ant colony system (ACS) to solve the control problem for a large number of vehicles and lanes; [18] in which a control strategy aimed at minimising the maximum exit time is obtained by applying dynamic programming; [19] where the coordination of multiple vehicles approaching an intersection is considered in a control-theoretical framework: a decentralised approach combining optimal control with model-based heuristics is proposed; [20] that presents a more general reservation protocol, named AIM*, in which the intersection manager assigns reservations to vehicles based on the priority assigned to each vehicle; this new protocol makes it possible to optimise reservations in real-time using a conflict point separation model.…”

“…As a matter of fact, (18) corresponds to two constraints which define the minimum distance between two vehicles on the same lane; in particular, they state that if two vehicles u and v are in the same lane (z u, l = z v, l ) and v precedes u (x u, v, l = 0), then u cannot enter the link (P h , P m ) until v exits the link (P k , P n ), being (P h , P k ) ∈ ℐ 0, l lane or (P h , P k ) ∈ ℐ 1, l lane , and (P h , P m ), (P k , P n ) ∈ ℒ; it is worth noting that the constraints in (18) are always satisfied (hence, they are not significant) if z u, l ≠ z v, l , i.e. when the two vehicles are not in the same lane.…”

“…ii. Constraints (18) are applied to all the vehicles on node pairs belonging to the set ℐ int , with the above mentioned fixed values of the variables z. iii. Constraints (19), (20), and (23) are not considered.…”

Methods for estimating vehicle queues at a marine terminal: A computational comparison

“…Especially, a micro level traffic simulation can capture the traffic characteristic of vehicle queues (Wu et al, 2013). Specifically, for a terminal gate system, simulation can curve not only the queue transient process in a dynamic situation, but also the detailed vehicle driving behaviour.…”