Autonomous Intersection Management Research Articles

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.

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.

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.

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

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

Traffic efficiency and fairness optimisation for autonomous intersection management based on reinforcement learning

Autonomous Intersection Management (AIM) for high-level Connected and Automated Vehicles (CAVs) has evolved from rule-based to optimisation-based policies. However, at congested major-minor intersections, optimising solely for efficiency can negatively impact vehicle fairness. This study addresses this issue by proposing a deep reinforcement learning approach that optimises both traffic efficiency and fairness for AIM. In the modelled multi-objective Markov decision process, traffic fairness is measured by the difference between the crossing order and the approaching order of CAVs, while traffic efficiency is measured by average travel time. With unknown preferences of the objectives, Bellman optimality equation is generalised to obtain the optimal policies over the space of all possible preferences during the iterative training process. The effectiveness of the proposed method is evaluated in a simulated real-world intersection and compared with three benchmark policies, including the fairest policy for AIM: first-come-first-served. The learned policies perform best in reducing overall average vehicle delay, and demonstrate outstanding performance in balancing traffic fairness and efficiency.

STARC: Decentralized Coordination Primitive on Low-Power IoT Devices for Autonomous Intersection Management

Wireless communication is an essential element within Intelligent Transportation Systems and motivates new approaches to intersection management, allowing safer and more efficient road usage. With lives at stake, wireless protocols should be readily available and guarantee safe coordination for all involved traffic participants, even in the presence of radio failures. This work introduces STARC, a coordination primitive for safe, decentralized resource coordination. Using STARC, traffic participants can safely coordinate at intersections despite unreliable radio environments and without a central entity or infrastructure. Unlike other methods that require costly and energy-consuming platforms, STARC utilizes affordable and efficient Internet of Things devices that connect cars, bicycles, electric scooters, pedestrians, and cyclists. For communication, STARC utilizes low-power IEEE 802.15.4 radios and Synchronous Transmissions for multi-hop communication. In addition, the protocol provides distributed transaction, election, and handover mechanisms for decentralized, thus cost-efficient, deployments. While STARC’s coordination remains resource-agnostic, this work presents and evaluates STARC in a roadside scenario. Our simulations have shown that using STARC at intersections leads to safer and more efficient vehicle coordination. We found that average waiting times can be reduced by up to 50% compared to using a fixed traffic light schedule in situations with fewer than 1000 vehicles per hour. Additionally, we design platooning on top of STARC, improving scalability and outperforming static traffic lights even at traffic loads exceeding 1000 vehicles per hour.

Is All-Direction Turn Lane a Good Choice for Autonomous Intersections? A Study of Method Development and Comparisons

Autonomous intersection management (AIM) guides vehicles to pass through signal-free intersections individually. Since vehicles are permitted to turn in any direction from any lane in AIM, it is desired to erase lane changes on the entry lane to achieve high traffic efficiency. However, erasing lane changes exposes intersections to complex conflicts. To study the trade-off between lane changes on the entry lanes and conflicts inside the intersection, this paper makes a comprehensive comparison of two kinds of intersection systems. First, all-direction turn lanes (ADTL) and specific-direction turn lanes (SDTL) are designed to distinguish lane change behaviors. Second, a two-stage method is proposed to manage the oncoming vehicles at the intersection. The first stage is timing schedule optimization and the second stage is trajectory optimization. Then, a method based on Monte Carlo Tree Search (MCTS) is designed to solve the timing schedule optimization model at high traffic demands. Finally, two parts of simulation experiments are conducted in this paper. The first part verifies the performance of the MCTS-based method. The second part compares ADTL and SDTL in multiple scenarios. The results show that ADTL outperforms SDTL in the efficient system, but the opposite conclusion appears in the fault-tolerant system. Besides, combining ADTL with lane changes may bring out the full value of ADTL.

Periodic Multi-Agent Path Planning

Multi-agent path planning (MAPP) is the problem of planning collision-free trajectories from start to goal locations for a team of agents. This work explores a relatively unexplored setting of MAPP where streams of agents have to go through the starts and goals with high throughput. We tackle this problem by formulating a new variant of MAPP called periodic MAPP in which the timing of agent appearances is periodic. The objective with periodic MAPP is to find a periodic plan, a set of collision-free trajectories that the agent streams can use repeatedly over periods, with periods that are as small as possible. To meet this objective, we propose a solution method that is based on constraint relaxation and optimization. We show that the periodic plans once found can be used for a more practical case in which agents in a stream can appear at random times. We confirm the effectiveness of our method compared with baseline methods in terms of throughput in several scenarios that abstract autonomous intersection management tasks.

Optimization-Based Intersection Control for Connected Automated Vehicles and Pedestrians

Traffic at larger or busier urban intersections is currently coordinated using traffic signals to prevent dangerous traffic situations and to regulate the flow of traffic. In future scenarios with 100% connected automated vehicles, conventional traffic signals could be replaced, and vehicles at intersections could be seamlessly coordinated via vehicle-to-vehicle and vehicle-to-infrastructure communication. In the past two decades, many such control strategies have been presented, commonly referred to as autonomous intersection management (AIM). In recent years, an evolution from simpler first come, first served to more sophisticated optimization-based AIM strategies can be observed. Optimization-based AIM can significantly improve capacity and reduce delays as compared to slot-based strategies and conventional traffic signal control (TSC). In addition, it allows for prioritizing road users. This paper is among the first to consider pedestrians in optimization-based AIM. The proposed approach consists of a signal-free vehicle control in combination with pedestrian signal phases that are fully integrated into the optimization problem. Since the communication range of the controller is limited in real-world applications, a rolling horizon scheme is presented and explained in detail. The presented strategy is implemented and evaluated using a microscopic traffic simulation framework. Results show that vehicle delays can be significantly reduced and vehicle capacity can be increased compared to fully actuated TSC, while pedestrian waiting times are comparable. In addition, focus is put on how vehicle and pedestrian delays can be balanced in the presented setup. Three different control parameters can be adjusted, which need to be tuned based on the considered demand scenario.

Hierarchical Cooperation and Load Balancing for Scalable Autonomous Vehicle Routing in Multi-Access Edge Computing Environment

When Connected Autonomous Vehicles (CAVs) request routing in a driving environment with Autonomous Intersection Management (AIM) systems, a routing planner collects the demands and optimizes the routes in a coordinated manner to reduce the overall travel times. In practice, however, the routing demands are massive, especially in a large-scale traffic network. As a result, the centralized routing planner fails to scale out to accommodate the growing requests, causing a severe scalability issue. This paper presents a holistic solution for scalable CAV routing by enabling hierarchical cooperation and load balancing in the Multi-Access Edge Computing (MEC) environment. The proposed system cooperatively plans CAV routes and dynamically balances loads in MECs to handle massive requests. According to the experiments, our system has a 15.68X higher routing capacity than the centralized routing system, and load balancing reduces 14.51% computation time of routing. The experiments show that our system is scalable for massive autonomous vehicle routing.

Collection Auctions-Based Autonomous Intersection Management

The traffic management system holds immense importance due to its significant impact on human living standards. With the advent of advanced technologies such as natural language processing and autonomous vehicles, this study proposes a novel cooperative traffic management system based on collection auctions at an isolated unsignalized intersection, taking into account the users’ preferences for passing the intersection while being subject to their social credits. Once vehicles enter the vehicle-to-infrastructure communication zone, drivers provide the intersection control center with their bidding information, which reflects their urgency for right-of-way. According to the traffic and biding information, the vehicles’ passing sequence is optimized by the control center, in order to maximize the drivers’ average satisfaction. To demonstrate the effectiveness of the proposed system, a series of simulation experiments were conducted under varying traffic volumes. The simulation results were then compared with several other traffic control systems from the literature. It was shown that the proposed algorithm demonstrates superior performance in terms of computational time, traffic delay, and drivers’ personal satisfaction.

Cooperative Platoon Formation of Connected and Autonomous Vehicles: Toward Efficient Merging Coordination at Unsignalized Intersections

This paper presents a Vehicle-Platoon-Aware Bi-Level Optimization Algorithm for Autonomous Intersection Management (VPA-AIM) to coordinate the merging of Connected and Automated Vehicles at unsignalized intersections. The constraint-coupled bi-level optimization is operated within a rolling horizon to balance traffic performance and computational efficiency. In each decision step, the platoon formation scheme is incorporated into an upper-level traffic scheduling model as decision variables to pursue an optimal schedule from a systemic view. Meanwhile, the passing sequence and timeslots of vehicles are jointly optimized with the platoon configuration scheme by virtue of real-time traffic states to improve operational efficiency and fairness. After that, a lower-level trajectory planning model will generate dynamically-feasible and energy-efficient trajectories according to the given schedule and coupling constraints with the objective of improving space utilization to prevent spillbacks. Moreover, the quantifiable connection between the makespan of traffic scheduling schemes and the occurrence of spillbacks is established, demonstrating that the cooperative platoon formation strategy is effective in avoiding and mitigating spillbacks in normal and saturated traffic states. Additionally, the proposed algorithm can be extended to mixed traffic scenarios. Numerical experiments are conducted on extensive scenarios with different arrival flows, where the Constraint Programming technique is employed to produce the optimal schedule. Experimental results indicate the superiority of the proposed approach in optimality and stability with reasonable sub-second computation time for real-life applications.

Sharing Traffic Priorities via Cyber–Physical–Social Intelligence: A Lane-Free Autonomous Intersection Management Method in Metaverse

Replacing traffic signals with roadside vehicle-to-infrastructure systems in the era of connected and autonomous vehicles (CAVs) is promising. Managing CAVs in a signal-free intersection, known as autonomous intersection management (AIM), controls the driving behavior of each intersection-traverse CAV to maximize the throughput. Although AIM improves the gross throughput, the fairness of each individual vehicle in its right of way is not seriously considered. This study sets up an AIM system in the cyber–physical–social space to trade traverse priorities quantitatively and fairly. To that end, one needs an AIM method that is optimal and stable, otherwise no convincing trades of traverse priorities could be made. This study proposes a near-optimal lane-free AIM method based on numerical optimal control, wherein log-exp functions are deployed to convexify nondifferentiable collision-avoidance constraints. Besides that, a parameterized social force model (SFM) is proposed to provide a tunable initial guess for numerical optimal control. By tuning the urgency weights in SFM, one may get cooperative trajectories in different homotopy classes, which are further utilized to decide the amount of virtual currency to reward those CAVs who tend to share their traverse priorities. The overall method improves the traverse throughput with individual fairness respected. In experiencing this system, passengers learn how to behave with politeness when they drive manually. Experiments show the efficiency and robustness of the AIM method and also show the efficacy of the overall priority-sharing system.

Joint optimization for autonomous intersection management and trajectory smoothing design with connected automated vehicles

Trajectory smoothing design (TSD) may significantly reduce fuel consumption and improve driving comfort at intersections. In this paper, a mixed integer linear programming (MILP) model with discrete time is formulated to jointly optimize autonomous intersection management and TSD, aiming to improve traffic efficiency, fuel economy and driving comfort simultaneously. Driving safety of car-following and collision avoidance at conflict points, diverge points and converge points, as well as constraints of acceleration and jerk are considered. To reasonably describe vehicle movement within intersection areas, the vehicle trajectory within the intersection is treated as a channel considering the vehicle width. A rolling horizon framework is used to solve the model. We have compared the traffic efficiency, fuel economy, monetary cost and driving comfort of the joint optimization model with that of the state-of-the-art two-stage strategy. Finally, sensitivity analysis with respect to left-turn ratio, weighted coefficient of TSD and control zone length is conducted.

A Two-Stage Optimization Method for Schedule and Trajectory of CAVs at an Isolated Autonomous Intersection

Autonomous intersection management has become a state-of-the-art control strategy customized for connected and autonomous vehicles. Combining the advantages of tile-based and conflict point-based approaches, this paper proposes a two-stage optimization method based on a developed intersection modeling approach. The first stage is a timing schedule optimization model, assigning vehicle arrival times at an intersection. Based on the output of the first stage, the second stage is a trajectory optimization model, which gives the eco-driving strategies. Moreover, a rolling optimization with a variable cycle length is adopted to run the method continuously. Simulation results show that the proposed method outperforms the genetic algorithm-based method in terms of computation time, and can reduce vehicle delay and fuel consumption by 89.48% and 46.84%, respectively, under different traffic demands compared to the first-come-first-serve method. Furthermore, the performance of the proposed method under asymmetric traffic demand is discussed. Sensitivity analyses suggest that (1) a long cycle length benefits the proposed method within certain limits and (2) a proper deceleration within the intersection can balance traffic delay with fuel consumption. In addition, an additional model with a heuristic rule is compared with the original timing schedule optimization model. It is found that reducing binaries in the first stage can make a tradeoff between the quality of the solution and efficiency, which can be used in conjunction with long cycles.

A Safe and Robust Autonomous Intersection Management System Using a Hierarchical Control Strategy and V2I Communication

Connected autonomous vehicles can significantly improve the safety and mobility of urban transportation systems. However, these systems are vulnerable to model uncertainties, wireless communication impairments, and external disturbances. In this article, we propose a new autonomous intersection management (AIM) system, called hierarchical model predictive control (HMPC). In HMPC, the intersection coordination unit (ICU) in a global centralized layer is responsible for assigning a safe speed to each vehicle while minimizing the system's cost. In the Local decentralized layer, each vehicle is responsible for tracking the reference speed assigned by the ICU, while avoiding collisions. In our method, each vehicle can use its own sensors to monitor its close surroundings, and take its own decisions on its movements, independent on the control decisions sent from the ICU. We investigate the safety, scalability and robustness of HMPC compared with two well-known AIM methods based on centralized and decentralized control strategies. For the evaluation, we use simulation of urban mobility (SUMO). Further, we study the scalability and performance of the algorithms in the presence of communication impairments associated with wireless channels. Our simulation results show that HMPC can safely handle high traffic flow rates. Also, HMPC is robust to uncertainties caused by the wireless communication.