Intersection Management Research Articles

Vehicular Ad Hoc Networks Topology Optimization for Autonomous Intersection Management System: A Periodic Intervention-Based Approach

Vehicular Ad Hoc Networks Topology Optimization for Autonomous Intersection Management System: A Periodic Intervention-Based Approach

A Review of Expert Systems Integration in Signal Plan Optimisation

In urban networks, periodic peak traffic congestion often occurs during the day, namely in the morning and afternoon hours. Due to spatial constraints and the inability to increase capacity through physical road expansion, modern traffic management increasingly relies on Intelligent Transport Systems (ITS) solutions. One such solution is the integration of automatic licence plate recognition, an expert system and microsimulation tools aimed at optimising the network performance of signalised intersections within a network. Based on real-time and historical data on individual vehicle trajectories, the system predicts the route of each vehicle through the observed segment of the traffic network, determines the network load and proposes optimal signal plans. This paper provides an overview of conducted research related to the optimisation of signal plans utilising expert systems. Mathematical models for capacity and load determination, as well as computational intelligence-based systems used for signalised intersection management strategies, are described. Finally, the paper proposes a basic framework and guidelines related to the suggested system, highlighting open questions and potential challenges in its development.

Space-time graph path planner for unsignalized intersection management with a V2V agent coordination architecture

Reducing traffic congestion and increasing passenger safety are important objectives for emerging automated transportation systems. Autonomous intersection management systems (AIMS) enable large scale optimization of vehicle trajectories with connected and autonomous vehicles (CAVs). We propose a novel approach for computing the fastest waypoint trajectory in intersections using graph search in a discretized space-time graph that produces collision-free paths with variable vehicle speeds that comply with traffic rules and vehicle dynamical constraints. To assist our planner algorithm in decentralized scenarios, we also propose a multi-agent protocol architecture for vehicle coordination for trajectory planning using a vehicle-to-vehicle (V2V) network. The trajectories generated allow a much higher evacuation rate and congestion threshold, with lower O(N) algorithm runtime compared to the state of the art conflict detection graph platoon path planning method, even for large scenarios with vehicle arrival rate of 1/s and thousands of vehicles.

AROW: V2X-Based Automated Right-of-Way Algorithm for Cooperative Intersection Management

AROW: V2X-Based Automated Right-of-Way Algorithm for Cooperative Intersection Management

Constraint-Guided Behavior Transformer for Centralized Coordination of Connected and Automated Vehicles at Intersections.

The centralized coordination of Connected and Automated Vehicles (CAVs) at unsignalized intersections aims to enhance traffic efficiency, driving safety, and passenger comfort. Autonomous Intersection Management (AIM) systems introduce a novel approach for centralized coordination. However, existing rule-based and optimization methods often face the challenges of poor generalization and low computational efficiency when dealing with complex traffic environments and highly dynamic traffic conditions. Additionally, current Reinforcement Learning (RL)-based methods encounter difficulties around policy inference and safety. To address these issues, this study proposes Constraint-Guided Behavior Transformer for Safe Reinforcement Learning (CoBT-SRL), which uses transformers as the policy network to achieve efficient decision-making for vehicle driving behaviors. This method leverages the ability of transformers to capture long-range dependencies and improve data sample efficiency by using historical states, actions, and reward and cost returns to predict future actions. Furthermore, to enhance policy exploration performance, a sequence-level entropy regularizer is introduced to encourage policy exploration while ensuring the safety of policy updates. Simulation results indicate that CoBT-SRL exhibits stable training progress and converges effectively. CoBT-SRL outperforms other RL methods and vehicle intersection coordination schemes (VICS) based on optimal control in terms of traffic efficiency, driving safety, and passenger comfort.

Assessing the impact of communication delays for Autonomous Intersection Management systems

Communication is essential for Cooperative Intelligent Transportation Systems (C-ITS) to achieve better road efficiency, especially for Autonomous Intersection Management (AIM) which coordinates vehicles to pass the intersection safely and efficiently. Communication delays cause severe safety crises (i.e., collisions) and vehicular-performance degradation regarding intersection capacities and vehicular delays. Targeting the delays, network researchers have been working on low-latency communication technologies, and C-ITS researchers have proposed delay-tolerant AIM systems to avoid collisions in intersections. The impacts of communication delays are observed and discussed in the literature; however, models and assessments of the delay requirements for AIM are needed to provide insights for future network and C-ITS research. Here, we model the impact of communication delays on vehicular performance at an autonomous intersection and validate the models with the simulation results from over two million experiments, two types of multi-lane intersections (a typical 4-legged intersection and a roundabout), and four AIM systems. The simulations are conducted with SUMO simulator and AIM systems, where communication delays are inserted into the message exchanges during the simulation. The models are represented in linear, quintic, and cubic polynomials, showing that communication delay between 0 to 100 milliseconds is linearly related to vehicular performance in terms of intersection capacity and vehicular delay. According to the models, we show that by reducing communication delay from 100 to 10 milliseconds, the capacity degradation can be reduced from 7-10% to 0.7-1.0%. Moreover, communication delays must be less than 247 milliseconds to allow AIM systems to outperform traditional traffic lights.

An integrated intelligent intersection management and control system design

While a fair amount of study has been done recently on integrated management and control models for large-scale traffic systems, there is comparatively little information available regarding the design of management and control systems for small-scale intersections. This study aims to create the structural diagram of a management system for intersections and control technologies. It does this by designing three distinct systems: congestion prediction, traffic signal timing, and congestion flow dredging. This study searches the current state of common technology and chooses the intersection integrated management system by means of comparison. Fuzzy TOPSIS algorithm and ADP adaptive dynamic programming are chosen as the calculation techniques of signal lamp timing, while BP neural network and ATT-GCN network are chosen as the key algorithms of congestion prediction after thorough investigation. In the end, conventional traffic management techniques like forbidding left turns are used to direct cars that haven't arrived at the busy crossroads beforehand. This paper aims to select the best model for an intersection management system currently available from the common technologies. The idealized management system and its structure diagram are designed in a straightforward manner, and they will serve as a guide for future road and vehicular intersection design.

An adaptive coupled control method based on vehicles platooning for intersection controller and vehicle trajectories in mixed traffic

AbstractConnected and autonomous driving technologies offer a novel solution for intersection control optimization. Connected and autonomous vehicles (CAVs) can access signal plans and optimize trajectories to minimize delays and reduce fuel consumption. However, optimizing trajectories for individual vehicles significantly increases complexity, especially for joint optimization of traffic signals and vehicle trajectories. Given the current technical, regulatory, and policy constraints, a superior intersection management approach is necessary before fully automated driving is achieved. This paper introduces an adaptive coupling control (ACC) method based on vehicle platooning to optimize signal timings and vehicle trajectories in mixed traffic. Initially, vehicle platoon segmentation is conducted, led by CAVs. The study then proposes a single‐layer coupled optimization model based on vehicle platoons, simplifying the joint optimization model. To address logistic constraint difficulties, a linearization of the coupled model (LCM) method is developed. Numerical experiments demonstrate that the ACC method significantly reduces vehicle delay and fuel consumption. At high CAV penetration rates (0.8 < R <1) and high traffic volumes (over 900 pcu/h), vehicle platoon control delivers excellent performance, with delays and fuel consumption even lower than in a fully automated environment (R = 1). This surprising result suggests that the mixed platoon system (ACC method) positively impacts mixed traffic.

Enhancing intersection safety in autonomous traffic: A grid-based approach with risk quantification

Existing studies on autonomous intersection management (AIM) primarily focus on traffic efficiency, often overlooking the overall intersection safety, where conflict separation is simplified and traffic conflicts are inadequately assessed. In this paper, we introduce a calculation method for the grid-based Post Encroachment Time (PET) and the total kinetic energy change before and after collisions. The improved grid-based PET metric provides a more accurate estimation of collision probability, and the total kinetic energy change serves as a precise measure of collision severity. Consequently, we establish the Grid-Based Conflict Index (GBCI) to systematically quantify collision risks between vehicles at an autonomous intersection. Then, we propose a traffic-safety-based AIM model aimed at minimizing the weighted sum of total delay and conflict risk at the intersection. This entails the optimization of entry time and trajectory for each vehicle within the intersection, achieving traffic control that prioritizes overall intersection safety. Our results demonstrate that GBCI effectively assesses conflict risks within the intersection, and the proposed AIM model significantly reduces conflict risks between vehicles and enhances traffic safety while ensuring intersection efficiency.

The Effectiveness of Autonomous Intersections in a City

Vehicle navigation on roads is a complex problem that will probably be solved by using artificial intelligence in key roles. Today, there are cars capable of autonomous driving, but they are dependent on an old infrastructure that primarily includes intersections designed for human drivers. This paper opens a new chapter in the area of autonomous intersection management. Most research to date has looked at implementing a solution for a single intersection. We have created a simulation that runs in real-time, where up to several dozen intersections appear side by side. In this work, we conduct experiments to test the deployment of the autonomous algorithm in a city along with traffic lights. Autonomous intersections win with their efficiency, and in case of a limited budget, it's most advantageous to deploy them at the busiest intersections.

Pedestrian shuttle service optimization for autonomous intersection management

Autonomous intersection management (AIM) has been widely investigated over the past two decades as a solution for signal-free intersections in a fully connected and autonomous environment. However, existing AIM approaches lack consideration for crossing pedestrians, especially in a way that enables pedestrians to cross streets safely while optimizing upcoming vehicles at the intersection to maintain traffic continuity. To address this issue, this paper proposes an Automated Pedestrian Shuttle (APS) service system to transport pedestrians across the street. Using a coupled space–time network modeling method, an optimization model, which incorporates pedestrian ride strategies and aims to minimize the total crossing time, is first established for the APS operation scheme. A Shuttle-by-Shuttle-Planning (SBSP) algorithm is developed for model solving to determine the APS operation route and the departure time from each station along the route. Then, a timing schedule optimization model is proposed to adjust the departure time of APS at each station and the time of main-lane vehicles entering an intersection to avoid vehicle conflicts. To ensure that the main-lane vehicles can enter the intersection on time, a trajectory optimization model is proposed to provide a specific driving strategy for each main-lane vehicle. A rolling horizon strategy is adopted to bridge the different optimization time windows. Finally, the effectiveness of the proposed APS service system is demonstrated through numerical simulation experiments. The results show that (1) the APS service can effectively help pedestrians cross the street with minor delays to main-lane vehicles; (2) The dynamic-route scheme is adaptable to the needs of randomly arriving pedestrians due to its demand-responsive nature; (3) The service efficiency of the system is related to the number of APSs, and the more APSs, the higher the service efficiency under the premise of unsaturation; (4) The designed SBSP algorithm significantly reduces the model-solving time compared to the commercial solver, making the APS service system potentially applicable for field implementation.

Exploiting Traffic Light Coordination and Auctions for Intersection and Emergency Vehicle Management in a Smart City Mixed Scenario.

IoT (Internet-of-Things)-powered devices can be exploited to connect vehicles to smart city infrastructure, allowing vehicles to share their intentions while retrieving contextual information about diverse aspects of urban viability. In this paper, we place ourselves in a transient scenario in which next-generation vehicles that are able to communicate with the surrounding infrastructure coexist with traditional vehicles with limited or absent IoT capabilities. We focus on intersection management, in particular on reusing existing traffic lights empowered by a new management system. We propose an auction-based system in which traffic lights are able to exchange contextual information with vehicles and other nearby traffic lights with the aim of reducing average waiting times at intersections and consequently overall trip times. We use bid propagation to improve standard vehicle trip times while allowing emergency vehicles to free up the way ahead without needing ad hoc system for such vehicle, only an increase in their budget. The proposed system is then tested against two baselines: the classical Fixed Time Control system currently adopted for traffic lights, and an auction strategy that does not exploit traffic light coordination. We performed a large set of experiments using the well known MATSim transport simulator on both a synthetic Manhattan map and on a map we built of an urban area located in Modena, Northern Italy. Our results show that the proposed approach performs better than the classical fixed time control system and the auction strategy that does not exploit coordination among traffic lights.

SDN-enabled Quantized LQR for Smart Traffic Light Controller to Optimize Congestion

Existing intersection management systems, in urban cities, lack in meeting the current requirements of self-configuration, lightweight computing, and software-defined control, which are necessarily required for congested road-lane networks. To satisfy these requirements, this work proposes effective, scalable, multi-input and multi-output, and congestion prevention-enabled intersection management system utilizing a software-defined control interface that not only regularly monitors the traffic to prevent congestion for minimizing queue length and waiting time but also offers a computationally efficient solution in real-time. For effective intersection management, a modified linear-quadratic regulator, i.e., Quantized Linear Quadratic Regulator (QLQR), is designed along with Software-defined Networking (SDN)-enabled control interface to maximize throughput and vehicles speed and minimize queue length and waiting time at the intersection. Experimental results prove that the proposed SDN-QLQR improves the comparative performance in the interval of 24.94%–49.07%, 35.78%–68.86%, 36.67%–59.08%, and 29.94%–57.87% for various performance metrics, i.e., average queue length, average waiting time, throughput, and average speed, respectively.

Learn to Bet: Using Reinforcement Learning to Improve Vehicle Bids in Auction-Based Smart Intersections.

With the advent of IoT, cities will soon be populated by autonomous vehicles and managed by intelligent systems capable of actively interacting with city infrastructures and vehicles. In this work, we propose a model based on reinforcement learning that teaches to autonomous connected vehicles how to save resources while navigating in such an environment. In particular, we focus on budget savings in the context of auction-based intersection management systems. We trained several models with Deep Q-learning by varying traffic conditions to find the most performance-effective variant in terms of the trade-off between saved currency and trip times. Afterward, we compared the performance of our model with previously proposed and random strategies, even under adverse traffic conditions. Our model appears to be robust and manages to save a considerable amount of currency without significantly increasing the waiting time in traffic. For example, the learner bidder saves at least 20% of its budget with heavy traffic conditions and up to 74% in lighter traffic with respect to a standard bidder, and around three times the saving of a random bidder. The results and discussion suggest practical adoption of the proposal in a foreseen future real-life scenario.

SMART TRAFFIC LIGHTS USING IOTTECHNOLOGIES : PROJECT CONCEPT AND PROGRESS

The rural exodus and population growth has led to the rapid expansion of cities without sometimes developing road infrastructure. This situation is at the origin of congestion in road traffic with a very high number of vehicles. In Africa, cities that have the financial means often use traffic lights with a predefined traffic plan which can be ineffective at sometimes of the day. In this paper, we propose a concept of wireless traffic light terminal, connected and self-sufficient in energy, for installation at intersections without public works on the road. This traffic light terminal is accessible remotely from police stations for its supervision and control. Thus, traffic management at an intersection is carried out remotely and in real time by an agent who selects a traffic plan adapted to the current situation. The objective is to centralize, with few personnel, the management of several intersections equipped with traffic light terminals, for better overall regulation of road traffic at low costs. Our work resulted in a proof of concept, for a four-lane crossroads, made with four Arduino electronic boards, four Lora wireless modules, a Raspberry-pi microcomputer, a camera and a 4G modem. For the software part, a server application developed with the node-red language, on the Raspberry-pi electronic card, provides the graphical interface for monitoring and control. The proposed system integrates a Cloud service that makes the human-machine interface for supervision and control remotely accessible via the Internet.

Understanding the Determinants of Lane Inefficiency at Fully Actuated Intersections: An Empirical Analysis

As urban traffic challenges intensify, the growing interest for fully actuated control systems in intersection management is on the rise due to their capacity to adapt to dynamic traffic demands. These systems play a crucial role in sustainable traffic solutions, significantly reducing delays and emissions and enhancing overall system efficiency. The optimal performance of these systems relies on effectively facilitating vehicle discharge at the saturation flow rate throughout the green period. This study introduces a new parameter, lane inefficiency, evaluating vehicle discharge effectiveness by comparing saturation flow rate with instantaneous discharge for each green period. It provides a comprehensive assessment of green utilization for specific lanes. This study also explores the impact of signal control system parameters and traffic flow characteristics on lane inefficiency using principal component analysis (PCA) and multiple linear regression models. This approach holistically evaluates how both signal control system and traffic flow parameters collectively influence efficient green period utilization. The findings emphasize the impact of critical factors on lane inefficiency, including green time, the proportion of total unused green time to green time, total unused green time, the percentage of heavy vehicles in departing traffic, the ratio of effective green time to cycle time, the total time headways of the first four vehicles, and queue length. Decision makers need to pay due attention to these parameters to enhance intersection performance and foster a more sustainable urban transportation network.

A platoon ordering algorithm on reservation based intersection management systems

Traffic intersections act as pivotal junctions where various streams of traffic intersect and intersecting movements occur. When traffic volumes are high, intersections that are efficiently designed and equipped with intelligent traffic control systems can optimize the movement of vehicles. Accordingly, the time spent by vehicles at intersections increases, and these vehicles cause the vehicles behind them to slow down. This study aims to enhance the efficacy of reservation-based intersection control for platoons comprising vehicles arriving at intersections and turning in various directions. To assess the impact of different platooning algorithms on both the average speed of vehicles within the platoons and the total time spent at intersections, simulations were conducted using the SUMO environment. These simulations observed variations in the average speed values of vehicles and the duration spent by vehicles at intersections. The simulation results showed that the proposed reservation-based platoon ordering algorithm significantly contributed to increasing the average speed and reducing waiting time.

Adaptive Traffic Control Using Cooperative Communication Through Visible Light

The study aims to create a Visible-Light Communication (VLC) system for secure vehicle management at intersections. This involves enabling communication between vehicles and infrastructure (V2V, V2I, and I2V) using headlights, streetlights, and traffic signals. Mobile optical receivers gather data, determine their location, and read transmitted information through joint transmission. An intersection manager coordinates traffic and communicates with vehicles through embedded Driver Agents. The system utilizes a "mesh/cellular" hybrid network configuration and encodes data into light signals emitted by transmitters. Optical sensors and filtering properties enable reception and decoding. The study demonstrates bidirectional communication, employing queue/request/response mechanisms and relative pose concepts for safe vehicle passage. A deep reinforcement learning model controls traffic light cycles, validated via simulation in a Simulation of Urban Mobility simulator. Results show that this adaptive traffic control system effectively collects detailed vehicle data and ensures secure communication within the short-range mesh network.

Integrating Local Planners Into Autonomous Intersection Management: Bridging Global Planning and Dynamic Execution

Integrating Local Planners Into Autonomous Intersection Management: Bridging Global Planning and Dynamic Execution

Efficient Mixed Traffic Intersection Management under Uncertainty

Despite the extensive amount of research that exists regarding intersection crossing of Connected and Autonomous Vehicles, implementation of most of the already proposed theoretical algorithms is currently not realistic due to either their computational complexity, or strong assumptions about traffic data acquisition. An intermediary step would be to consider mixed traffic. Motivated by these facts, we study an unsignalized intersection crossing problem with mixed traffic and develop an online, safety-critical optimization scheme under localization and detection uncertainties. The centralized controller follows an Enhanced First-In-First-Out reservation policy and with the use of sequential optimization it ensures no more than one vehicle is present inside the protected zone of the intersection in order to avoid collisions, even when noisy measurements are present. Comparisons are made between different control and protected zone sizes and conclusions are drawn about the impact of uncertainties on the efficiency and robustness of the system.