Cooperative adaptive cruise control (CACC) is a promising intelligent vehicle technology for improving traffic flow stability, throughput, and safety. One major control objective of CACC is to ...guarantee <inline-formula> <tex-math notation="LaTeX">\mathcal {L}_{p} </tex-math></inline-formula> string stability, i.e., <inline-formula> <tex-math notation="LaTeX">\mathcal {L}_{p} </tex-math></inline-formula>-norm measured disturbance is uniformly bounded along the vehicle string. Most existing methods for string stability are laborious for implementation without considering either heterogeneous disturbances (e.g., tracking errors and unmodeled dynamics) or saturation constraints (e.g., input saturation). The decentralized model predictive control (MPC) method, which is a widely used feedforward control for string stability, suffers the burdens of computation cost and intervehicular communication. To fill these gaps, we distinguish different types of disturbances and use different ways to handle them. We use feedforward control for large yet infrequent disturbances and feedback control for small yet frequent disturbances. Different from MPC, our feedforward control is event-triggered so that the intervehicle communication and planning costs can be significantly reduced. Different from pure robust feedback control, our combination of feedback and feedforward control could reduce the conservation of the controller. Theoretical analysis and simulations show that the proposed method guarantees <inline-formula> <tex-math notation="LaTeX">\mathcal {L}_{p} </tex-math></inline-formula> string stability of vehicle platoons considering heterogeneous disturbances and saturation constraints.
•Conduct theoretical analysis of intersection capacity and vehicle delay under reservation-based control.•Propose a mixed integer linear programming (MILP) model that can dynamically form batches ...with the optimal sizes and determine service sequence of vehicles under varying traffic condition.•Simulation results show that:○The proposed optimization-based control performs best compared with reservation-based control and vehicle-actuated control.○Reservation-based control outperforms actuated control under low demand or under-saturated demand.○FCFS-based control is incapable of handling high demand and multiple conflicting streams.
Reservation-based methods with simple policies such as first-come-first-service (FCFS) have been proposed in the literature to manage connected and automated vehicles (CAVs) at isolated intersections. However, a comprehensive analysis of intersection capacity and vehicle delay under FCFS-based control is missing, especially under high traffic demand. To address this problem, this study adopts queueing theory and analytically shows that such method is incapable of handling high demand with multiple conflicting traffic streams. Furthermore, an optimization model is proposed to optimally serve CAVs arriving at an intersection for delay minimization. This study then compares the performance of the proposed optimization-based control with reservation-based control as well as conventional vehicle-actuated control at different demand levels. Simulation results show that the proposed optimization-based control performs best and it has noticeable advantages over the other two control methods. The advantages of reservation-based control are insignificant compared with vehicle-actuated control under high demand.
The platooning of connected and automated vehicles (CAVs) is expected to have a transformative impact on road transportation, e.g, enhancing highway safety, improving traffic efficiency, and reducing ...fuel consumption. One critical task of platoon control is to achieve string stability, for which various models and methods had been proposed. However, different types of definitions and analysis methods for string stability were proposed over the years and were not thoroughly compared. To fill these gaps, this paper aims to clarify the relationship of ambiguous definitions and various analysis methods, providing a rigorous foundation for future studies. A series of equivalences are summarized and discussed. The pros and cons of different analysis methods and definitions are discussed, too. All these discussions provide insights for practical selection of analyzing methods for vehicle platoons.
•Corridor-level cooperative trajectory optimization with CAVs.•Optimization achieves system optimality in terms of total vehicle delay.•Trajectories interactions are considered explicitly at the ...microscopic level.•Each ingress lane can be used for either through, right-turn or left-turn movement.
Trajectory planning for connected and automated vehicles (CAVs) has been studied at both isolated intersections and multiple intersections under the fully CAV environment in the literature. However, most of the existing studies only model limited interactions of vehicle trajectories at the microscopic level, without considering the coordination between vehicle trajectories. This study proposes a mixed-integer linear programming (MILP) model to cooperatively optimize the trajectories of CAVs along a corridor for system optimality. The car-following and lane-changing behaviors of each vehicle along the entire path are optimized together. The trajectories of all vehicles along the corridor are coordinated for system optimality in terms of total vehicle delay. All vehicle movements (i.e., left-turning, through, and right-turning) are considered at each intersection. The ingress lanes are not associated with any specific movement and can be used for all vehicle movements, which provides much more flexibility. Vehicles are controlled to pass through intersections without traffic signals. Due to varying traffic conditions, the planning horizon is adaptively adjusted in the implementation procedure of the proposed model to find a balance between solution feasibility and computational burden. Numerical studies validate the advantages of the proposed CAV-based control over the coordinated fixed-time control at different demand levels in terms of vehicle delay and throughput. The analyses of the safety time gaps for collision avoidance within intersection areas show the promising benefits of traffic management under the fully CAV environment.
Testing and evaluation is a critical step in the development and deployment of connected and automated vehicles (CAVs), and yet there is no systematic framework to generate testing scenario library. ...This study aims to provide a general framework for the testing scenario library generation (TSLG) problem with different operational design domains (ODDs), CAV models, and performance metrics. Given an ODD, the testing scenario library is defined as a critical set of scenarios that can be used for CAV test. Each testing scenario is evaluated by a newly proposed measure, scenario criticality, which can be computed as a combination of maneuver challenge and exposure frequency. To search for critical scenarios, an auxiliary objective function is designed, and a multi-start optimization method along with seed-filling is applied. Theoretical analysis suggests that the proposed framework can obtain accurate evaluation results with much fewer number of tests, if compared with the on-road test method. In part II of the study, three case studies are investigated to demonstrate the proposed method. Reinforcement learning based technique is applied to enhance the searching method under high-dimensional scenarios.
For simulation to be an effective tool for the development and testing of autonomous vehicles, the simulator must be able to produce realistic safety-critical scenarios with distribution-level ...accuracy. However, due to the high dimensionality of real-world driving environments and the rarity of long-tail safety-critical events, how to achieve statistical realism in simulation is a long-standing problem. In this paper, we develop NeuralNDE, a deep learning-based framework to learn multi-agent interaction behavior from vehicle trajectory data, and propose a conflict critic model and a safety mapping network to refine the generation process of safety-critical events, following real-world occurring frequencies and patterns. The results show that NeuralNDE can achieve both accurate safety-critical driving statistics (e.g., crash rate/type/severity and near-miss statistics, etc.) and normal driving statistics (e.g., vehicle speed/distance/yielding behavior distributions, etc.), as demonstrated in the simulation of urban driving environments. To the best of our knowledge, this is the first time that a simulation model can reproduce the real-world driving environment with statistical realism, particularly for safety-critical situations.
•Derivation of a probabilistic speed–density relation from microscopic car-following.•An analytical verification of the physical behavior of the proposed model.•An empirical investigation of the ...model and comparison against real-world traffic data.
Probabilistic models describing macroscopic traffic flow have proven useful both in practice and in theory. In theoretical investigations of wide-scatter in flow–density data, the statistical features of flow density relations have played a central role. In real-time estimation and traffic forecasting applications, probabilistic extensions of macroscopic relations are widely used. However, how to obtain such relations, in a manner that results in physically reasonable behavior has not been addressed. This paper presents the derivation of probabilistic macroscopic traffic flow relations from Newell’s simplified car-following model. The probabilistic nature of the model allows for investigating the impact of driver heterogeneity on macroscopic relations of traffic flow. The physical features of the model are verified analytically and shown to produce behavior which is consistent with well-established traffic flow principles. An empirical investigation is carried out using trajectory data from the New Generation SIMulation (NGSIM) program and the model’s ability to reproduce real-world traffic data is validated.
•1. Introduce average vehicle occupancy ratio into cost calculation to represent more realistic aspects of ridesharing costs subdued by ridesharing drivers and passengers.•Formulate a link-node ...complementarity representation for ridesharing user equilibrium.•Model the presence of HOT lanes.•Employ multi-start strategies to provide a sufficiently good initial solution to our network design problem.
Though the conventional network design is extensively studied, the network design problem for ridesharing, in particular, the deployment of high-occupancy toll (HOT) lanes, remains understudied. This paper focuses on one type of network design problem as to whether existing roads should be retrofit into HOT lanes. It is a continuous bi-level mathematical program with equilibrium constraints. The lower level problem is ridesharing user equilibrium (RUE). To reduce the problem size and facilitate computation, we reformulate RUE in the link-node representation. Then we extend the RUE framework to accommodate the presence of HOT lanes and tolls. Algorithms are briefly discussed and numerical examples are illustrated on the Braess network and the Sioux Falls network, respectively. Results show that carefully selecting the deployment of HOT lanes can improve the overall system travel time.
► We propose a new stochastic model of traffic flow with state dependent headways. ► The proposed model is consistent with the CTM in the mean dynamic sense. ► The proposed model implicitly ensures ...the non-negativity of traffic densities.
In a variety of applications of traffic flow, including traffic simulation, real-time estimation and prediction, one requires a probabilistic model of traffic flow. The usual approach to constructing such models involves the addition of random noise terms to deterministic equations, which could lead to negative traffic densities and mean dynamics that are inconsistent with the original deterministic dynamics. This paper offers a new stochastic model of traffic flow that addresses these issues. The source of randomness in the proposed model is the uncertainty inherent in driver gap choice, which is represented by random state dependent vehicle time headways. A wide range of time headway distributions is allowed. From the random time headways, counting processes are defined, which represent cumulative flows across cell boundaries in a discrete space and continuous time conservation framework. We show that our construction implicitly ensures non-negativity of traffic densities and that the fluid limit of the stochastic model is consistent with cell transmission model (CTM) based deterministic dynamics.
•Work schedule uncertainty prevents ridesharing between commuters with identical home and work locations.•Autonomous vehicles (AV) alleviate the risk of ridesharing because additional AVs can be ...easily repositioned if needed.•We develop evening commute bottleneck models with uncertain work end time.•We establish mode choice (ridesharing vs. travelling alone) equilibrium for round-trip commute.•Autonomous vehicles encourage commute ridesharing and reduce expected round-trip travel costs.
The uncertainty in work end time can prevent ridesharing between two commuters with identical home and work locations. This effect can be alleviated by autonomous vehicles (AV): when two commuters share a ride from home to work, if the realized work end times result in a long wait between the two, an additional AV can be easily repositioned from home to work, whereas with regular human-driven vehicles one has to either bear the long wait or request expensive taxi service. This observation implies that AVs have great potential for increasing commute ridesharing. To study this effect, we develop evening commute bottleneck models with uncertain work end time and establish mode choice equilibrium for the round-trip commute. We find that, with regular vehicles, because of the expected waiting cost caused by work end time uncertainty, commuters typically do not share rides even though they have the same home and work locations. With AVs, commuters always share rides for the morning commute, and they also share rides for the evening commute if their work end times turn out to be close; otherwise, they reposition an additional AV to pick one of them up. Our numerical examples show that AVs can encourage commute ridesharing and significantly reduce the expected round-trip travel costs for commuters.
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