Autonomous Intersection Management Research Articles

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.

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.

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.

Autonomous Intersection Management: Optimal Trajectories and Efficient Scheduling.

Intersections are at the core of congestion in urban areas. After the end of the Second World War, the problem of intersection management has benefited from a growing body of advances to address the optimization of the traffic lights' phase splits, timing, and offset. These contributions have significantly improved traffic safety and efficiency in urban areas. However, with the growth of transportation demand and motorization, traffic lights show their limits. At the end of the 1990s, the perspective of autonomous and connected driving systems motivated researchers to introduce a paradigm shift for controlling intersections. This new paradigm is well known today as autonomous intersection management (AIM). It harnesses the self-organization ability of future vehicles to provide more accurate control approaches that use the smallest available time window to reach unprecedented traffic performances. This is achieved by optimizing two main points of the interaction of connected and autonomous vehicles at intersections: the motion control of vehicles and the schedule of their accesses. Considering the great potential of AIM and the complexity of the problem, the proposed approaches are very different, starting from various assumptions. With the increasing popularity of AIM, this paper provides readers with a comprehensive vision of noticeable advances toward enhancing traffic efficiency. It shows that it is possible to tailor vehicles' speed and schedule according to the traffic demand by using distributed particle swarm optimization. Moreover, it brings the most relevant contributions in the light of traffic engineering, where flow-speed diagrams are used to measure the impact of the proposed optimizations. Finally, this paper presents the current challenging issues to be addressed.

Dynamic Trajectory-Based Traffic Dispersion Method for Intersection Traffic Accidents in an Intelligent and Connected Environment

Intersection traffic accidents (ITAs) are one of the major causes of nonrecurrent congestion in urban road networks because the intersection is the bottleneck of road traffic. Although there has been a proliferation of studies that confirm connected automated vehicle (CAV) technology demonstrates the potential to improve traffic mobility and safety performance at intersections, studies that handle ITAs using CAV technology are scarce. To fill this gap, this study proposes an intersection control system to coordinate vehicle trajectories and ensure operational efficiency and safety under the condition of typical ITAs. A route allocation judgment method based on the position of obstacles is proposed to avoid the accident vehicle. A spatiotemporal grid-based trajectory coordination model is proposed by formulating the route allocation. We utilize the .NET Framework to secondarily develop a Simulation of Urban Mobility-based simulation environment that examines the proposed method. The experimental results indicate that the presented method outperforms the signal control method in terms of reducing delay per vehicle and rescue time and increasing throughput. Consequently, we concluded that the proposed method could handle ITAs under an intelligent and connected environment and improve the safety and robustness of intelligent intersections. It is reasonably believed that the technique has potential applicability in autonomous intersection management.

Advantage Actor-Critic for Autonomous Intersection Management

With increasing urban population, there are more and more vehicles, causing traffic congestion. In order to solve this problem, the development of an efficient and fair intersection management system is an important issue. With the development of intelligent transportation systems, the computing efficiency of vehicles and vehicle-to-vehicle communications are becoming more advanced, which can be used to good advantage in developing smarter systems. As such, Autonomous Intersection Management (AIM) proposals have been widely discussed. This research proposes an intersection management system based on Advantage Actor-Critic (A2C) which is a type of reinforcement learning. This method can lead to a fair and efficient intersection resource allocation strategy being learned. In our proposed approach, we design a reward function and then use this reward function to encourage a fair allocation of intersection resources. The proposed approach uses a brake-safe control to ensure that autonomous moving vehicles travel safely. An experiment is performed using the SUMO simulator to simulate traffic at an isolated intersection, and the experimental performance is compared with Fast First Service (FFS) and GAMEOPT in terms of throughput, fairness, and maximum waiting time. The proposed approach increases fairness by 20% to 40%, and the maximum waiting time is reduced by 20% to 36% in high traffic flow. The inflow rates are increased, average waiting time is reduced, and throughput is increased.

Intersection Management Protocol for Mixed Autonomous and Human-Operated Vehicles

This paper presents a novel embedding protocol that allows for safe and efficient operation of the Hybrid Autonomous Intersection Management (H-AIM) protocol concurrently with actuated and adaptive signal controllers. The proposed protocol extends H-AIM to allow it to cope with some operational uncertainty that is common in actuated signal controllers. A novel approach for computing safety bounds on signal timing is presented as a way of insuring safety in the face of demand uncertainty. Experimental results show the feasibility and effectiveness of combining H-AIM with actuated controllers for various levels of connected and autonomous vehicle (CAV) market penetration and different combinations of common signal control schemes, namely, adaptive signal timing, fixed signal timing, and signal actuation. The benefits are presented in terms of delay improvement when common actuation protocols are used in conjunction with the H-AIM protocol. In contrast to previous reports, the results presented in this paper suggest that mixtures of turning movement assignments that are more permissive for CAVs and less permissive for human operated vehicles are often detrimental in terms of traffic delay. Nonetheless, when implemented on top of an actuated and adaptive controller, the extended H-AIM protocol is shown to never be detrimental while presenting statistically significant reductions in total delay when more than 15% of the traffic is composed of CAVs.

Autonomous Intersection Management by Using Reinforcement Learning

Developing a safer and more effective intersection-control system is essential given the trends of rising populations and vehicle numbers. Additionally, as vehicle communication and self-driving technologies evolve, we may create a more intelligent control system to reduce traffic accidents. We recommend deep reinforcement learning-inspired autonomous intersection management (DRLAIM) to improve traffic environment efficiency and safety. The three primary models used in this methodology are the priority assignment model, the intersection-control model learning, and safe brake control. The brake-safe control module is utilized to make sure that each vehicle travels safely, and we train the system to acquire an effective model by using reinforcement learning. We have simulated our proposed method by using a simulation of urban mobility tools. Experimental results show that our approach outperforms the traditional method.

Autonomous Intersection Management for Connected and Automated Vehicles: A Lane-Based Method

Most existing studies on autonomous intersection management (AIM) often focus on algorithms to accommodate conflicts among vehicles by assuming that the entrance lane and the exit lane of vehicles are exogenous inputs. This paper shows that allowing entrance lanes and exit lanes to be optimized can significantly improve traffic efficiency. In particular, this paper proposes “all-direction” lanes, where left-turn, through, and right-turn traffic is all allowed at the same lane. We develop two methods for optimizing entering time (i.e., when to enter the intersection) and route choice decisions (i.e., entrance lane and exit lane), including the sliding-time-window-based global optimum (GO-STW) and the first-come-first-served method with optimal route choices (FCFS-R). The developed lane-based methods can be formulated as mixed integer linear programming (MILP) problems, which can be solved using the CPLEX solver. A heuristic is further adopted to solve the MILP model in a timely manner, which illustrates the potential real-time applicability of the proposed method. Numerical analysis is conducted to examine performance and effectiveness of the proposed methods and heuristic. We found that the optimization of lane/route choices is often more critical than entering time.