Turning the corner: improved intersection control for autonomous vehicles

As traffic congestion increases day by day, it becomes necessary to improve the existing roadway facilities to maintain satisfactory operational and safety performances. New vehicle technologies, such as Connected and Autonomous Vehicles (CAV) have a potential to significantly improve transportation systems. Using the advantages of CAVs, this study developed signalized intersection control strategy algorithm that optimizes the operations of CAVs and allows signal priority for connected platoons. The algorithm was tested in VISSIM microsimulation using a real-world urban corridor. The tested scenarios include a 2040 Do-Nothing scenario, and CAV alternatives with 25%, 50%, 75% and 100% CAV penetration rate. The results show a significant reduction in intersection delays (26% - 38%) and travel times (6% - 20%), depending on the penetration rate, as well as significant improvements on the network-wide level. CAV penetration rates of 50% or more have a potential to significantly improve all operational measures of effectiveness.

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Intelligently managing mixed traffic flows at intersections Proposal of a platoon-based round-robin algorithm with priorities

10.36227/techrxiv.13525853.v1 ◽

2021 ◽

Author(s):

Hossein Moradi ◽

Sara Sasaninejad ◽

Sabine Wittevrongel ◽

Joris Walraevens

Keyword(s):

Traffic Control ◽

Autonomous Vehicles ◽

State Of The Art ◽

Control Methods ◽

Mixed Traffic ◽

Traffic Flows ◽

Traffic Conditions ◽

Advisory System ◽

Intersection Control ◽

Integration Effort

The importance of addressing the complexities of mixed traffic conditions by providing innovative approaches, models, and algorithms for traffic control has been well highlighted in the state-of-the-art literature. Accordingly, the first aim of this study has been to enhance the traditional intersection control methods for the incorporation of autonomous vehicles and wireless communications. For this purpose, we have introduced a novel framework labeled by “PRRP-framework”. The PRRP-framework also enables flexible preferential treatments for some special vehicles within an implementable range of complexity while it addresses the stochastic nature of traffic flow. Moreover, the PRRP-framework has been coupled with a speed advisory system that brings complementary strengths leading to even better performance. Further simulations reported in this manuscript, confirmed that such an integration effort is a prerequisite to move towards sustainable results.

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An Unmanaged Intersection Protocol and Improved Intersection Safety for Autonomous Vehicles

Multi-Agent Systems for Traffic and Transportation Engineering - Advances in Mechatronics and Mechanical Engineering ◽

10.4018/978-1-60566-226-8.ch009 ◽

2009 ◽

pp. 193-217

Author(s):

Kurt Dresner ◽

Peter Stone ◽

Mark Van Middlesworth

Keyword(s):

Autonomous Vehicles ◽

Management System ◽

Original Work ◽

Control Mechanism ◽

Peer To Peer ◽

Traffic Lights ◽

Intersection Safety ◽

Intersection Control ◽

Intersection Management ◽

Centralized System

Fully autonomous vehicles promise enormous gains in safety, efficiency, and economy for transportation. In previous work, the authors of this chapter have introduced a system for managing autonomous vehicles at intersections that is capable of handling more vehicles and causing fewer delays than modern- day mechanisms such as traffic lights and stop signs [Dresner & Stone 2005]. This system makes two assumptions about the problem domain: that special infrastructure is present at each intersection, and that vehicles do not experience catastrophic physical malfunctions. In this chapter, they explore two separate extensions to their original work, each of which relaxes one of these assumptions. They demonstrate that for certain types of intersections—namely those with moderate to low amounts of traffic—a completely decentralized, peer-to-peer intersection management system can reap many of the benefits of a centralized system without the need for special infrastructure at the intersection. In the second half of the chapter, they show that their previously proposed intersection control mechanism can dramatically mitigate the effects of catastrophic physical malfunctions in vehicles such that in addition to being more efficient, autonomous intersections will be far safer than traditional intersections are today.

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Human-like Speed Modelling for Autonomous Vehicles during Car-Following at Intersections

Canadian Journal of Civil Engineering ◽

10.1139/cjce-2020-0761 ◽

2021 ◽

Author(s):

Mostafa H Tawfeek

Keyword(s):

Machine Learning ◽

Autonomous Vehicles ◽

Machine Learning Techniques ◽

Behavioral Measures ◽

Car Following ◽

Learning Techniques ◽

Proposed Model ◽

Intersection Control

This study aims at modelling drivers’ speed in car-following during braking situations at intersections to estimate a safe comfortable human-like speed at the minimum car-following distance for Autonomous Vehicles (AV). Several car-following behavioral measures at different times before reaching the minimum following distance and the intersection control type (signalized or unsignalized) were extracted to train the model using three machine learning techniques. The results showed that the XGBoost model is superior when compared to other techniques with R-squared values of 0.99 and 0.97 for training and testing datasets. The results also indicated that the control type impact driver speed at the minimum following distance. The modelled speed will provide more comfortable speed to the AV riders and will not violate the expectations of the surrounding traditional vehicle drivers. Also, the proposed model can be adopted to enhance current car-following models by considering the effect of intersections and its control type.

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Cooperative Schedule-Driven Intersection Control with Connected and Autonomous Vehicles

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ◽

10.1109/iros40897.2019.8967975 ◽

2019 ◽

Author(s):

Hsu-Chieh Hu ◽

Stephen F. Smith ◽

Rick Goldstein

Keyword(s):

Autonomous Vehicles ◽

Intersection Control

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SUSTAINABILITY EFFECTS OF NEXT-GENERATION INTERSECTION CONTROL FOR AUTONOMOUS VEHICLES

Transport ◽

10.3846/16484142.2015.1080760 ◽

2015 ◽

Vol 30 (3) ◽

pp. 342-352 ◽

Cited By ~ 15

Author(s):

Zhixia (Richard) Li ◽

Madhav V. Chitturi ◽

Lang Yu ◽

Andrea R. Bill ◽

David A. Noyce

Keyword(s):

Energy Consumption ◽

Autonomous Vehicles ◽

High Volume ◽

Ambient Air ◽

Next Generation ◽

Pm 2.5 ◽

Time Space ◽

Moderate Volume ◽

Vehicle Technologies ◽

Intersection Control

Transportation sustainability is adversely affected by recurring traffic congestions, especially at urban intersections. Frequent vehicle deceleration and acceleration caused by stop-and-go behaviours at intersections due to congestion adversely impacts energy consumption and ambient air quality. Availability of the maturing vehicle technologies such as autonomous vehicles and Vehicle-To-Vehicle (V2V) / Vehicle-To-Infrastructure (V2I) communications provides technical feasibility to develop solutions that can reduce vehicle stops at intersections, hence enhance the sustainability of intersections. This paper presents a next-generation intersection control system for autonomous vehicles, which is named ACUTA. ACUTA employs an enhanced reservation-based control algorithm that controls autonomous vehicles’ passing sequence at an intersection. Particularly, the intersection is divided into n-by-n tiles. An intersection controller reserves certain time-space for each vehicle, and assures no conflict exists between reservations. The algorithm was modelled in microscopic traffic simulation platform VISSIM. ACUTA algorithm modelling as well as enhancement strategies to minimize vehicle intersection stops and eventually emission and energy consumption were discussed in the paper. Sustainability benefits offered by this next-generation intersection were evaluated and compared with traditional intersection control strategies. The evaluation reveals that multi-tile ACUTA reduces carbon monoxide (CO) and Particulate Matter (PM) 2.5 emissions by about 5% under low to moderate volume conditions and by about 3% under high volume condition. Meanwhile, energy consumption is reduced by about 4% under low to moderate volume conditions and by about 12% under high volume condition. Compared with four-way stop control, single-tile ACUTA reduces CO and PM 2.5 emissions as well as energy consumption by about 15% under any prevailing volume conditions. These findings validated the sustainability benefits of employing next-generation vehicle technologies in intersection traffic control. In addition, extending the ACUTA to corridor level was explored in the paper.

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