A Multiagent Approach to Autonomous Intersection Management

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.

Toward autonomous vehicles: A survey on cooperative vehicle-infrastructure system

Cooperative vehicle-infrastructure system (CVIS) is an important part of the intelligent transport system (ITS). Autonomous vehicles have the potential to improve safety, efficiency, and energy saving through CVIS. Although a few CVIS studies have been conducted in the transportation field recently, a comprehensive analysis of CVIS is necessary, especially about how CVIS is applied in autonomous vehicles. In this paper, we overview the relevant architectures and components of CVIS. After that, state-of-the-art research and applications of CVIS in autonomous vehicles are reviewed from the perspective of improving vehicle safety, efficiency, and energy saving, including scenarios such as straight road segments, intersections, ramps, etc. In addition, the datasets and simulators used in CVIS-related studies are summarized. Finally, challenges and future directions are discussed to promote the development of CVIS and provide inspiration and reference for researchers in the field of ITS.

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.

A Collaborative Merging Method for Connected and Automated Vehicle Platoons in a Freeway Merging Area with Considerations for Safety and Efficiency

To solve the problems of congestion and accident risk when multiple vehicles merge into the merging area of a freeway, a platoon split collaborative merging (PSCM) method was proposed for an on-ramp connected and automated vehicle (CAV) platoon under a mixed traffic environment composed of human-driving vehicles (HDV) and CAVs. The PSCM method mainly includes two parts: merging vehicle motion control and merging effect evaluation. Firstly, the collision avoidance constraints of merging vehicles were analyzed, and on this basis, a following-merging motion rule was proposed. Then, considering the feasibility of and constraints on the stability of traffic flow during merging, a performance measurement function with safety and merging efficiency as optimization objectives was established to screen for the optimal splitting strategy. Simulation experiments under traffic demand of 1500 pcu/h/lane and CAV ratios of 30%, 50%, and 70% were conducted respectively. It was shown that under the 50% CAV ratio, the average travel time of the on-ramp CAV platoon was reduced by 50.7% under the optimal platoon split strategy compared with the no-split control strategy. In addition, the average travel time of main road vehicles was reduced by 27.9%. Thus, the proposed PSCM method is suitable for the merging control of on-ramp CAV platoons under the condition of heavy main road traffic demand.

A Cyber-Physical Framework for Optimal Coordination of Connected and Automated Vehicles on Multi-Lane Freeways

Uncoordinated driving behavior is one of the main reasons for bottlenecks on freeways. This paper presents a novel cyber-physical framework for optimal coordination of connected and automated vehicles (CAVs) on multi-lane freeways. We consider that all vehicles are connected to a cloud-based computing framework, where a traffic coordination system optimizes the target trajectories of individual vehicles for smooth and safe lane changing or merging. In the proposed framework, the vehicles are coordinated into groups or platoons, and their trajectories are successively optimized in a receding horizon control (RHC) approach. Optimization of the traffic coordination system aims to provide sufficient gaps when a lane change is necessary while minimizing the speed deviation and acceleration of all vehicles. The coordination information is then provided to individual vehicles equipped with local controllers, and each vehicle decides its control acceleration to follow the target trajectories while ensuring a safe distance. Our proposed method guarantees fast optimization and can be used in real-time. The proposed coordination system was evaluated using microscopic traffic simulations and benchmarked with the traditional driving (human-based) system. The results show significant improvement in fuel economy, average velocity, and travel time for various traffic volumes.

A simulation study of cooperative and autonomous vehicles (CAV) considering courtesy, ethics, and fairness

Autonomous vehicles (AV) can be programmed to act cooperatively. Previous research on cooperative and autonomous vehicles (CAV) suggests they can substantially improve traffic system operations in terms of mobility and safety. However, these studies do not explicitly take each vehicle's potential gain/loss into consideration and ignore their individual levels of willingness to cooperate. They do not account for ethics and fairness either. In this study, several cooperation/courtesy strategies are proposed to address the above issues. These strategies are grouped into two categories based on non-instrumental and instrumental principles. Non-instrumental strategies make courtesy/cooperation decisions based on some courtesy proxies and a user-specified courtesy level, while instrumental strategies are based only on courtesy proxies related to local traffic performance. Also, a new CAV behavior modeling framework is proposed based on our previous work on cooperative car-following and merging (CCM) control. With such a framework, the proposed courtesy strategies can be easily implemented. The proposed framework and courtesy strategies are coded in SUMO microscopic traffic simulator. They are evaluated considering different levels of traffic demand on a freeway corridor consisting of a work zone and three weaving areas of different types. Interesting findings are drawn from the simulation results, one of which is that the instrumental Local Utilitarianism strategy performs the best in terms of mobility, safety, and fairness. In the future, auction-based strategies can be considered to model how CAV make decisions.

Research Structure and Trends of Smart Urban Mobility

Mobility is a subject of increasing importance in a time when cities have gained prominence, as they are home to over 56% of the world’s population and generate over 80% of global GDP. Urban planning principles have traditionally been developed to promote urban efficiency and enhance productivity. The emergence of ‘Smart Mobility’ has provided researchers and policy practitioners new ways to understand and plan cities. With rapid urbanization growth and the sustained mobility challenges faced in most global cities, this paper sets forth to understand and map the evolution of the concept of ‘Smart Urban Mobility’ through a bibliometric analysis and science mapping techniques using VOSviewer. In total, 6079 articles were retrieved from the Web of Science database over 5 decades, from 1968 to 2021, and divided into four sub-periods, namely 1968 to 2010, 2011 to 2015, 2016 to 2019, and 2020 to 2021. The paper provides a better understanding of the thematic focus and associated trends of smart mobility beyond technical issues related to Intelligent Transport Systems (ITS), where due to diverse dynamics, such as unprecedented growth and advancement in technologies, attention has extended to incorporating the impacts of the application of different technologies in urban mobility as well as associated fields. This paper further identifies major sources, authors, publications, and countries that have made more contributions to the development of this field. The findings of this study can help researchers better understand the evolution of the subject, and help policymakers make better-informed decisions on investable infrastructures for better mobility outcomes in urban regeneration pursuits and future cities.

Towards the Prioritised Use of Transportation Infrastructures: The Case of Vehicle-Specific Dynamic Access Restrictions in City Centres

One of the main problems that local authorities of large cities have to face is the regulation of urban mobility. They need to provide the means to allow for the efficient movement of people and distribution of goods. However, the provisioning of transportation services needs to take into account general global objectives, like reducing emissions and having more healthy living environments, which may not always be aligned with individual interests. Urban mobility is usually provided through a transport infrastructure that includes all the elements that support mobility. On many occasions, the capacity of the elements of this infrastructure is lower than the actual demand and thus different transportation activities compete for their use. In this paper, we argue that scarce transport infrastructure elements should be assigned dynamically and in a prioritised manner to transport activities that have a higher utility from the point of view of society; for example, activities that produce less pollution and provide more value to society. In this paper, we define a general model for prioritizing the use of a particular type of transportation infrastructure element called time-unlimited elements, whose usage time is unknown a priori, and illustrate its dynamics through two use cases: vehicle-specific dynamic access restriction in city centres (i) based on the usage levels of available parking spaces and (ii) to assure sustained admissible air quality levels in the city centre. We carry out several experiments using the SUMO traffic simulation tool to evaluate our proposal.

A Control Method with Reinforcement Learning for Urban Un-signalized Intersection in Hybrid Traffic Environment

To control autonomous vehicles (AVs) in urban unsignalized intersections is a challenging problem, especially in a hybrid traffic environment where self-driving vehicles coexist with human driving vehicles. In this study, a coordinated control method with proximal policy optimization (PPO) in Vehicle-Road-Cloud Integration System (VRCIS) is proposed, where this control problem is formulated as a reinforcement learning (RL) problem. In this system, vehicles and everything (V2X) was used to keep communication between vehicles, and vehicle wireless technology can detect vehicles that use vehicles and infrastructure (V2I) wireless communication, thereby achieving a cost-efficient method. Then, the connected and autonomous vehicle (CAV) defined in the VRCIS learned a policy to adapt to human driving vehicles (HDVs) across the intersection safely by reinforcement learning (RL). We have developed a valid, scalable RL framework, which can communicate topologies that may be dynamic traffic. Then, state, action and reward of RL are designed according to urban unsignalized intersection problem. Finally, how to deploy within the RL framework was described, and several experiments with this framework were undertaken to verify the effectiveness of the proposed method.

Attention-Based Fault-Tolerant Approach for Multi-Agent Reinforcement Learning Systems

The aim of multi-agent reinforcement learning systems is to provide interacting agents with the ability to collaboratively learn and adapt to the behavior of other agents. Typically, an agent receives its private observations providing a partial view of the true state of the environment. However, in realistic settings, the harsh environment might cause one or more agents to show arbitrarily faulty or malicious behavior, which may suffice to allow the current coordination mechanisms fail. In this paper, we study a practical scenario of multi-agent reinforcement learning systems considering the security issues in the presence of agents with arbitrarily faulty or malicious behavior. The previous state-of-the-art work that coped with extremely noisy environments was designed on the basis that the noise intensity in the environment was known in advance. However, when the noise intensity changes, the existing method has to adjust the configuration of the model to learn in new environments, which limits the practical applications. To overcome these difficulties, we present an Attention-based Fault-Tolerant (FT-Attn) model, which can select not only correct, but also relevant information for each agent at every time step in noisy environments. The multihead attention mechanism enables the agents to learn effective communication policies through experience concurrent with the action policies. Empirical results showed that FT-Attn beats previous state-of-the-art methods in some extremely noisy environments in both cooperative and competitive scenarios, much closer to the upper-bound performance. Furthermore, FT-Attn maintains a more general fault tolerance ability and does not rely on the prior knowledge about the noise intensity of the environment.

Junction Management for Connected and Automated Vehicles: Intersection or Roundabout?

The concept of signal-free management at road junctions is tailored for Connected and Automated Vehicles (CAVs), in which the conventional signal control is replaced by various right-of-way assignment policies. First-Come-First-Served (FCFS) is the most commonly used policy. In most proposed strategies, although the traffic signals are replaced, the organization of vehicle trajectory remains the same as that of traffic lights. As a naturally signal-free strategy, roundabout has not received enough attention. A key motivation of this study is to theoretically compare the performance of signalized intersection (I-Signal), intersection using FCFS policy (I-FCFS), roundabout using the typical major-minor priority pattern (R-MM), and roundabout adopting FCFS policy (R-FCFS) under pure CAVs environment. Queueing theory is applied to derive the theoretical formulas of the capacity and average delay of each strategy. M/G/1 model is used to model the three signal-free strategies, while M/M/1/setup model is used to capture the red-and-green light switch nature of signal control. The critical safety time gaps are the main variables and are assumed to be generally distributed in the theoretical derivation. Analytically, I-Signal has the largest capacity benefiting from the ability to separate conflict points in groups, but in some cases it will have higher delay. Among the other three signal-free strategies, R-FCFS has the highest capacity and the least average control delay, indicating that the optimization of signal-free management of CAVs based on roundabout setting is worthy of further study.

A Priority-Based Autonomous Intersection Management (AIM) Scheme for Connected Automated Vehicles (CAVs)

In this paper, we investigate the intersection traffic management for connected automated vehicles (CAVs). In particular, a decentralized autonomous intersection management scheme that takes into account both the traffic efficiency and scheduling flexibility is proposed, which adopts a novel intersection–vehicle model to check conflicts among CAVs in the entire intersection area. In addition, a priority-based collision-avoidance rule is set to improve the performance of traffic efficiency and shorten the delays of emergency CAVs. Moreover, a multi-objective function is designed to obtain the optimal trajectories of CAVs, which considers ride comfort, velocities of CAVs, fuel consumption, and the constraints of safety, velocity, and acceleration. Simulation results demonstrate that our proposed scheme can achieve good performance in terms of traffic efficiency and shortening the delays of emergency CAVs.

A Double-Level Model Checking Approach for an Agent-Based Autonomous Vehicle and Road Junction Regulations

Usually, the design of an Autonomous Vehicle (AV) does not take into account traffic rules and so the adoption of these rules can bring some challenges, e.g., how to come up with a Digital Highway Code which captures the proper behaviour of an AV against the traffic rules and at the same time minimises changes to the existing Highway Code? Here, we formally model and implement three Road Junction rules (from the UK Highway Code). We use timed automata to model the system and the MCAPL (Model Checking Agent Programming Language) framework to implement an agent and its environment. We also assess the behaviour of our agent according to the Road Junction rules using a double-level Model Checking technique, i.e., UPPAAL at the design level and AJPF (Agent Java PathFinder) at the development level. We have formally verified 30 properties (18 with UPPAAL and 12 with AJPF), where these properties describe the agent’s behaviour against the three Road Junction rules using a simulated traffic scenario, including artefacts like traffic signs and road users. In addition, our approach aims to extract the best from the double-level verification, i.e., using time constraints in UPPAAL timed automata to determine thresholds for the AVs actions and tracing the agent’s behaviour by using MCAPL, in a way that one can tell when and how a given Road Junction rule was selected by the agent. This work provides a proof-of-concept for the formal verification of AV behaviour with respect to traffic rules.

The Design of Performance Guaranteed Autonomous Vehicle Control for Optimal Motion in Unsignalized Intersections

The design of the motion of autonomous vehicles in non-signalized intersections with the consideration of multiple criteria and safety constraints is a challenging problem with several tasks. In this paper, a learning-based control solution with guarantees for collision avoidance is proposed. The design problem is formed in a novel way through the division of the control problem, which leads to reduced complexity for achieving real-time computation. First, an environment model for the intersection was created based on a constrained quadratic optimization, with which guarantees on collision avoidance can be provided. A robust cruise controller for the autonomous vehicle was also designed. Second, the environment model was used in the training process, which was based on a reinforcement learning method. The goal of the training was to improve the economy of autonomous vehicles, while guaranteeing collision avoidance. The effectiveness of the method is presented through simulation examples in non-signalized intersection scenarios with varying numbers of vehicles.

Development of a conflict-free unsignalized intersection organization method for multiple connected and autonomous vehicles

An effective traffic control strategy will improve travel reliability in urban transportation networks. Lack of coordination between vehicles, however, exacerbates congestion due mainly to frequent stops at unsignalized intersections. It is beneficial to develop a conflict-free cooperation method that collects basic safety message from multiple approaching Connected and Autonomous Vehicles (for short, CAVs) and guarantees efficient unsignalized intersection operations with safe and incident free vehicle maneuvers. This paper proposes an interspersed traffic organization method under controlled constraints. Firstly, relied on shared location technology and considered the operating characteristics of CAVs at unsignalized intersections to detect and analyze traffic conflicts to establish a right-of-way judgment model for CAVs. In order to further ensure the safety and operating efficiency of the vehicle, based on the judgment results of right-of-way judgment model, a vehicle speed guidance model is established for different traffic conditions. Taking the real city standard intersection as the experimental analysis object, through data collection and simulation experiment, the signal control method and the organization method proposed in this paper are compared and analyzed. The results showed that the traffic organization method proposed in this paper improves the operational efficiency of 46%, the average travel time is reduced by 6.54s, which is not only better than the signal control method, but also supports the development of car networking technology.

A comparative study of state-of-the-art driving strategies for autonomous vehicles

The autonomous vehicle is regarded as a promising technology with the potential to reshape mobility and solve many traffic issues, such as accessibility, efficiency, convenience, and especially safety. Many previous studies on driving strategies mainly focused on the low-level detailed driving behaviors or specific traffic scenarios but lacked the high-level driving strategy studies. Though researchers showed increasing interest in driving strategies, there still has no comprehensive answer on how to proactively implement safe driving. After analyzing several representative driving strategies, we propose three characteristic dimensions that are important to measure driving strategies: preferred objective, risk appetite, and collaborative manner. According to these three characteristic dimensions, we categorize existing driving strategies of autonomous vehicles into four kinds: defensive driving strategies, competitive driving strategies, negotiated driving strategies, and cooperative driving strategies. This paper provides a timely comparative review of these four strategies and highlights the possible directions for improving the high-level driving strategy design.

Joint Optimization of Intersection Control and Trajectory Planning Accounting for Pedestrians in a Connected and Automated Vehicle Environment

Connected and automated vehicle (CAV) technology makes it possible to track and control the movement of vehicles, thus providing enormous potential to improve intersection operations. In this paper, we study the traffic signal control problem at an isolated intersection in a CAV environment, considering mixed traffic including various types of vehicles and pedestrians. Both the vehicle delay and the pedestrian delay are incorporated into the model formulation. This introduces some additional complexity, as any benefits to pedestrians will come at the expense of higher delays for the vehicles. Thus, some valid questions we answer in this paper are as follows: Under which circumstances could we provide priority to pedestrians without over penalizing the vehicles at the intersection? How important are the connectivity and autonomy associated with CAV technology in this context? What type of signal control algorithm could be used to minimize person delay accounting for both vehicles and pedestrians? How could it be solved efficiently? To address these questions, we present a model that optimizes signal control (i.e., vehicle departure sequence), automated vehicle trajectories, and the treatment of pedestrian crossing. In each decision step, the weighted sum of the vehicle delay and the pedestrian delay (e.g., the total person delay) is minimized by the joint optimization on the basis of the predicted departure sequences of vehicles and pedestrians. Moreover, a near-optimal solution of the integrated problem is obtained with an ant colony system algorithm, which is computationally very efficient. Simulations are conducted for different demand scenarios and different CAV penetration rates. The performance of the proposed algorithm in terms of the average person delay is investigated. The simulation results show that the proposed algorithm has potential to reduce the delay compared to an actuated signal control method. Moreover, in comparison to a CAV-based signal control that does not account for the pedestrian delay, the joint optimization proposed here can achieve improvement in the low- and moderate-vehicle-demand scenarios.

The impact of emerging and disruptive technologies on freight transportation in the digital era: current state and future trends

Purpose

With various challenges in the digital era, stakeholders are expressing growing interests in understanding the impact of emerging and disruptive technologies on freight transportation. This paper provides a systematic literature review of the current state of affairs as well as future trends and aims to support stakeholders' decision-making in logistics management in the era of disruptive technologies.

Design/methodology/approach

Several recent and representative articles from academic, industrial and governmental perspectives were investigated to set the scene for this research and to serve as a baseline for electing nine emerging technologies, which were then used to conduct a systematic literature review covering the literature within the area during the past twelve years.

Findings

3D printing, artificial intelligence, automated robots, autonomous vehicles, big data analytics, blockchain, drones, electric vehicles and the Internet of Things were identified as the emerging technologies. The current state of existing research and potential future opportunities were analyzed.

Research limitations/implications

Since the potential literature body is almost impossible to fully cover, a tradeoff between the number of emerging technologies and the related literature reviewed has been performed. However, the paper provides a novel approach to select the emerging and disruptive technologies and a systematic literature review to fill the identified research gap in the related literature.

Practical implications

The research support various stakeholders to better capture the current status of and the future opportunities in freight transportation and gain a clearer understanding of the disruptive technologies as well as to guide them in how to deploy these initiatives in future decision-making.

Originality/value

By providing a systematic literature review on the trends, themes and research opportunities in the era of disruptive technologies, the papers bring about broad and comprehensive review on the impact of disruptive technologies on logistics and transportation as well as opportunities to support management decision support in the logistics industry.

Multicriteria Autonomous Vehicle Control at Non-Signalized Intersections

The aim of the paper is to describe a multicriteria model predictive control method for autonomous vehicles at non-signalized intersections. The centralized controller aims to describe control action for each autonomous vehicle to guarantee collision free passage. At the same time, performances are defined for the centralized Model Predictive Controller, namely the minimization of traveling time and energy consumption. Since these control goals are often conflicting, a scheduling variable is introduced to create a balance between them. Hence, the centralized controller can be tuned based on the importance of each control goal, which can be useful in urban environment where traffic densities may vary heavily depending on the period of the day. The effectiveness of the proposed centralized multicriteria controller is demonstrated through a complex simulation example in CarSim simulation environment using different tpye of autonomous vehicles.

Ford Multi-AV Seasonal Dataset

This article presents a challenging multi-agent seasonal dataset collected by a fleet of Ford autonomous vehicles (AVs) at different days and times during 2017–2018. The vehicles traversed an average route of 66 km in Michigan that included a mix of driving scenarios such as the Detroit airport, freeways, city centers, university campus, and suburban neighborhoods. Each vehicle used in this data collection was a Ford Fusion outfitted with an Applanix POS-LV GNSS/INS system, four HDL-32E Velodyne 3D-lidar scanners, six Point Grey 1.3 MP cameras arranged on the rooftop for 360° coverage, and one Point Grey 5 MP camera mounted behind the windshield for the forward field of view. We present the seasonal variation in weather, lighting, construction, and traffic conditions experienced in dynamic urban environments. We also include data from multiple AVs that were driven in close proximity. This dataset can help design robust algorithms for AVs and multi-agent systems. Each log in the dataset is time-stamped and contains raw data from all the sensors, calibration values, pose trajectory, ground-truth pose, and 3D maps. All data is available in rosbag format that can be visualized, modified, and applied using the open-source Robot Operating System (ROS). We also provide the output of reflectivity-based localization for bench-marking purposes. The dataset can be freely downloaded at avdata.ford.com .

Evaluation of Two Improved Schemes at Non-Aligned Intersections Affected by a Work Zone with an Entropy Method

The impact of work zones on traffic is a common problem encountered in traffic management. The reconstruction of roads is inevitable, and it is necessary and urgent to reduce the impact of the work zone on the operation of traffic. There are many existing research results on the influence of highway work zones, including management strategies, traffic flow control strategies, and various corresponding model theories. There are also many research results on the impacts of urban road and subway construction on traffic operation, including construction efficiency, economic impact, and travel matrix. However, there are few studies concerning the choice of work zone location, and most previous studies have assumed that the work zone choice was scientific and reasonable. Therefore, it is reasonable to choose the location of the work zone and to assess whether there is room for improvement in the road form of the work zone, but this remains a research gap. Therefore, we studied a seven-lane main road T-intersection in Xi’an, China, and investigated a work zone located at this intersection that caused a road offset, leading to the non-aligned flow of main traffic. We designed two road improvement schemes and multiple transition schemes, used VISSIM software to evaluate the traffic operation of the two schemes, and used the entropy method to choose the suitability of the two schemes under different conditions. According to the results, in the best case, the driving time, delay, and number of stops are reduced by 44%, 66%, and 92%.

n-Ary Cartesian Composition of Multiautomata with Internal Link for Autonomous Control of Lane Shifting

In this paper, which is based on a real-life motivation, we present an algebraic theory of automata and multi-automata. We combine these (multi-)automata using the products introduced by W. Dörfler, where we work with the cartesian composition and we define the internal links among multiautomata by means of the internal links’ matrix. We used the obtained product of n-ary multi-automata as a system that models and controls certain traffic situations (lane shifting) for autonomous vehicles.

Scheduling-Based Optimization for Motion Coordination of Autonomous Vehicles at Multilane Intersections

This paper considers the motion coordination problem of autonomous vehicles in an intersection of a traffic network. The featured challenge is the design of an intersection traffic manager, in the form of a supervisory control algorithm, that regulates the motion of the autonomous vehicles in the intersection. We cast the multivehicle coordination task as an optimization problem, with a one-dimensional search-space. A model- and optimization-based heuristic method is employed to compute the control policy that results in the collision-free motion of the vehicles at the intersection and, at the same time, minimizes their delay. Our approach depends on a computation framework that makes the need for complex analytical derivations obsolete. A complete account of the computational complexity of the algorithm, parameterized by the configuration parameters of the problem, is provided. Extensive numerical simulations validate the applicability and performance of the proposed autonomous intersection traffic manager.

Optimizing the safety-efficiency balancing of automated vehicle car-following

This paper proposes an approach to rationally set automated vehicles' car following behavior that explicitly balances between the competing considerations of safety (i.e. small probabilities of a high-consequence crash) and efficiency (guaranteed but small impacts on journey arrival time due to the choice of car following distance). The specification of safety and efficiency are both based on empirically supported concepts and data. In numerical analyses with empirical vehicle trajectories at two sites, we demonstrate intuitive response to systematic variation in numerical values selected as inputs, as well as whether the scope of the efficiency consideration is selfish or systemwide. The proposed balancing is aligned with the standard "Hand Rule" criterion to demonstrate that a duty of care has been met, in which a burden must be borne if it is less than the product of the probability of loss to a third party and the magnitude of loss. Thus the proposed approach is intended to be useful for designers of control algorithms for AVs to establish that they have met their duty of care, taking both safety and efficiency into account.

Efficient Management of Road Intersections for Automated Vehicles—The FRFP System Applied to the Various Types of Intersections and Roundabouts

In the last decade, automatic driving systems for vehicles circulating on public roads have become increasingly closer to reality. There is always a strong interest in this topic among research centers and car manufacturers. One of the most critical aspects is the management of intersections, i.e., who will have to go first and in what ways? This is the question we want to answer through this research. Clearly, the goal is to manage the intersection safely, making it possible to reduce road congestion, travel time, emissions, and fuel consumption as much as possible. The research is conducted by comparing a new management system with the systems already known in the state of the art for different types of intersections. The new system proposed by us is called FRFP (first to reach the end of the intersection first to pass). In particular, vehicles will increase or decrease their speed in collaboration with each other by making the right decision. The vehicle that can potentially reach the intersection exit first.

Reputation and Trust Approach for Security and Safety Assurance in Intersection Management System

Crossroads are the main traffic jam generators in densely populated cities. Unmanned vehicles and intelligent transportation systems can significantly reduce congestion and improve road safety by eliminating the main cause of traffic accidents—the human factor. However, full confidence in their safety is necessary. This paper addresses the contextual data integrity problem, when an unmanned autonomous vehicle transmits incorrect data due to technical problems, or malicious attacks. We propose an approach based on trust and reputation that allows detecting vehicles transmitting bogus data. To verify the feasibility of the approach on practice, we conducted both software and physical simulations using the model of intersection and unmanned autonomous vehicle models. The simulation results show that the approach applied allows detecting vehicles with bogus data and excluding them from the group, thus increasing the safety of the intersection traversal by other vehicles.

Distributed control system architecture for balancing and stabilizing traffic in the network of multiple autonomous intersections using feedback consensus and route assignment method

Autonomous and intelligent system show a remarkable step in urban traffic management. Autonomous Intersection Management (AIM) is an outstanding example of using an autonomous vehicle and wireless communication technology. The traffic performance of a single AIM system has been proved in many works however, traffic in the network of multiple AIMs is waiting for an implementation. Coordination of traffic between intersections in the network is an important step of managing the overall networked traffic throughput. The authors modeled the traffic network with the multi-agents concept and used the discrete consensus algorithm to coordinate between autonomous agents and implemented the rerouting algorithm in order to distribute the excessive traffics to neighbored intersections with the optimal condition. Our target is to have a balance traffic in each intersection and reaches the equilibrium where the stability has been not compromised. The results show that reaching consensus condition will bring the networked traffic to an equilibrium state where a peak traffic will not be happened. In addition, this method shows that when traffic in a network reached consensus, it will also converge to the Nash equilibrium in the finite time.

CSIP

Intersection management is one of the main challenging issues in road safety because intersections are a leading cause of traffic congestion and accidents. In fact, more than 44% of all reported crashes in the U.S. occur around intersection areas, which, in turn, has led to 8,500 fatalities and approximately 1 million injuries every year. With vehicles expected to become self-driving, the question is whether high throughput can be obtained through intersections while keeping them safe. A spatio-temporal intersection protocol named the Ballroom Intersection Protocol (BRIP) [8] was recently proposed in the literature to address this situation. Under this protocol, automated and connected vehicles arrive at and go through an intersection in a cooperative fashion with no vehicle needing to stop, while maximizing the intersection throughput. Though no vehicles run into one another under ideal environments with BRIP, vehicle accidents can occur when the self-driving vehicles have location errors and/or control system failure. In this article, we present a safe and practical intersection protocol named the Configurable Synchronous Intersection Protocol (CSIP). CSIP is a more general and resilient version of BRIP. CSIP utilizes a certain inter-vehicular distance to meet safety requirements in the presence of GPS inaccuracies and control failure. The inter-vehicular distances under CSIP are much more acceptable and comfortable to human passengers due to longer inter-vehicular distances that do not cause fear. With CSIP, the inter-vehicular distances can also be changed at each intersection to account for different traffic volumes, GPS accuracy levels, and geographical layout of intersections. Our simulation results show that CSIP never leads to traffic accidents even when the system has typical location errors, and that CSIP increases the traffic throughput of the intersections compared to common signalized intersections.

SC-M*: A Multi-Agent Path Planning Algorithm with Soft-Collision Constraint on Allocation of Common Resources

Multi-agent path planning (MAPP) is increasingly being used to address resource allocation problems in highly dynamic, distributed environments that involve autonomous agents. Example domains include surveillance automation, traffic control and others. Most MAPP approaches assume hard collisions, e.g., agents cannot share resources, or co-exist at the same node or edge. This assumption unnecessarily restricts the solution space and does not apply to many real-world scenarios. To mitigate this limitation, this paper introduces a more general class of MAPP problems—MAPP in a soft-collision context. In soft-collision MAPP problems, agents can share resources or co-exist in the same location at the expense of reducing the quality of the solution. Hard constraints can still be modeled by imposing a very high cost for sharing. This paper motivates and defines the soft-collision MAPP problem, and generalizes the widely-used M* MAPP algorithm to support the concept of soft-collisions. Soft-collision M* (SC-M*) extends M* by changing the definition of a collision, so paths with collisions that have a quality penalty below a given threshold are acceptable. For each candidate path, SC-M* keeps track of the reduction in satisfaction level of each agent using a collision score, and it places agents whose collision scores exceed its threshold into a soft-collision set for reducing the score. Our evaluation shows that SC-M* is more flexible and more scalable than M*. It can also handle complex environments that include agents requesting different types of resources. Furthermore, we show the benefits of SC-M* compared with several baseline algorithms in terms of path cost, success rate and run time.

A Yielding Protocol that Uses Inter-Vehicle Communication to Improve the Traffic of Vehicles on a Low-Priority Road at an Unsignalized Intersection

Self-driven vehicles are being actively developed. When widespread, they will help reduce the number of traffic accidents and ease traffic congestion. They will coexist with human-driven vehicles for years. If there is a mismatch between human drivers’ operations and the judgments of self-driven vehicles, congestion may arise at an unsignalized intersection, in particular, where roads are prioritized. Vehicles on the low-priority road attempting to cross, or turn to, the priority road can significantly reduce the traffic flow. We have proposed a yielding protocol to deal with this problem and evaluated it using a simulation that focused on traffic flow efficiency at an intersection. In the simulation, we have varied the number of vehicles coming into the roads and the percentage of self-driven vehicles and confirmed that the proposed yielding protocol could improve the traffic flow of vehicles on the low-priority road.

Virtual Belt Algorithm for the Management of Isolated Autonomous Intersection

To enhance traffic efficiency, in this paper, a novel virtual belt algorithm is proposed for the management of an isolated autonomous intersection. The proposed virtual belt algorithm consists of an offline algorithm and an online algorithm. Using the offline algorithm, the considered intersection can be modeled as several virtual belts. The online algorithm is designed for the real-time application of the virtual belt algorithm. Compared with the related algorithms, the main advantage of the proposed algorithm is that, there are several candidate trajectories for each approaching vehicle. Thus, there are more opportunities for an approaching vehicle to obtain a permission to pass an intersection, which is effective to improve traffic efficiency. The proposed algorithm is validated using numerical simulations conducted by Matlab and VISSIM. The simulation results show that the proposed algorithm is effective for autonomous intersection management.