Controlling mixed platoons, which consist of both connected and automated vehicles (CAVs) and human-driven vehicles (HDVs), poses significant challenges due to the uncertain and unknown human driving behaviors. Data-driven control methods offer promising solutions by leveraging available trajectory data, but their performance can be compromised by noise and attacks. To address this issue, this paper proposes a Robust Data-EnablEd Predictive Leading Cruise Control (RDeeP-LCC) framework based on data-driven reachability analysis. The framework over-approximates system dynamics under noise and attack using a matrix zonotope set derived from data, and develops a stabilizing feedback control law. By decoupling the mixed platoon system into nominal and error components, we employ data-driven reachability sets to recursively compute error reachable sets that account for noise and attacks, and obtain tightened safety constraints of the nominal system. This leads to a robust data-driven predictive control framework, solved in a tube-based control manner. Human-in-the-loop experiments demonstrate that theRDeeP-LCCmethod significantly improves robustness against noise and attacks, while enhancing tracking accuracy, control efficiency, energy economy, driving comfort, and driving safety.

The integration of connected and automated vehicles (CAVs) into traffic systems holds potential to mitigate undesired disturbances. Nevertheless, coexisting human-driven vehicles (HDVs) introduce complex behavioral disturbances, which has imposed critical challenges for control robustness. This study develops a computational framework based on policy iteration to derive robust control policies with optimized attenuation performance for nonlinear mixed traffic flow. Specifically, robust$H_{\infty }$control problem is solved by applying the framework of zero-sum game, whose solution at the Nash equilibrium is transformed into a Hamilton–Jacobi (HJ) inequality with a Hamiltonian constraint. For achieving desired attenuation performance, the value function is updated by gradient descent based on counterexamples violating Hamiltonian and monotonicity constraints, where the positive definiteness of the value function is ensured by convex neural networks, facilitating the analysis of control stability via Lyapunov methods. By utilizing constraint gaps, the attenuation level is optimized through the analytical formulae derived from the HJ inequality. The stability and algorithm convergence are proved. Experimental results demonstrate the capability of the learned controller to effectively attenuate disturbance propagation and stabilize mixed traffic flow.

Optimizing mixed vehicle platoons, which consist of connected and automated vehicles (CAVs) with human-driven vehicles (HDVs), is a critical challenge for intelligent transportation systems. While existing predictive control methods have improved modeling accuracy and control robustness, they are often constrained by their reliance on online optimization, limiting their applicability in real-time scenarios. To address this gap, this paper proposes a Robust Explicit Data-Driven Predictive Control (REDDPC) framework designed to provide robust and real-time control for mixed vehicle platoons. The framework begins by utilizing a deep Koopman operator network to learn the nonlinear dynamics of the system. Using this learned representation, the neural network-based control policy is then optimized through backpropagation, eliminating the need for online optimization. To enhance robustness, a reachability-based safety filter is integrated with the learned control policy to dynamically adjust control inputs, ensuring platoon safety under complex conditions. Simulation and experiment results demonstrate that the proposed method achieves superior tracking performance under noise, disturbance, and attack conditions, while significantly reducing online computational time, making it highly suitable for real-world deployment.

In a mixed traffic with connected automated vehicles (CAVs) and human-driven vehicles (HDVs), data-driven predictive control of CAVs promises system-wide traffic performance improvements. Yet, most existing approaches focus on a centralized setup, which is computationally unscalable while failing to protect data privacy. The robustness against unknown disturbances has not been well addressed either, causing safety concerns. In this paper, we propose a decentralized robustDeeP-LCC(Data-EnablEd Predictive Leading Cruise Control) approach for CAVs to smooth mixed traffic. In particular, each CAV computes its control input based on locally available data from its involved subsystem. Meanwhile, the interaction between neighboring subsystems is modeled as a bounded disturbance, for which appropriate estimation methods are proposed. Then, we formulate a robust optimization problem and present its tractable computational solutions. Compared with the centralized formulation, our method greatly reduces computation complexity with better safety performance, while naturally preserving data privacy. Extensive traffic simulations validate its wave-dampening ability, safety performance, and computational benefits.

Multi-lane formation control can significantly enhance the efficiency of traffic systems in multi-lane scenarios. However, existing multi-lane formation control methods are mostly developed for fully Connected and Automated Vehicle (CAV) environments and lack a mechanism for multi-lane formation control in mixed traffic. This paper proposes a CAV formation grouping method and an interaction mechanism of CAVs and Human-driven Vehicles (HDVs), which is suitable for different traffic volumes and penetration rates. Depending on the positional relationship between the CAV formation and HDVs, it flexibly chooses between conventional formation control methods or improved mixed traffic formation control methods. By conducting simulations at various input flow volumes and penetration rates, the applicability of this method to different conditions is verified. The paper also explores the extent to which multi-lane formation control methods can improve traffic efficiency and their relationship with traffic volume and penetration rate.

In this article, we present the first experimental results of data-driven predictive control for connected and autonomous vehicles (CAVs) in dissipating traffic waves. In particular, we consider a recent strategy of Data-EnablEd Predictive Leading Cruise Control (DeeP-LCC), which bypasses the need of identifying the driving behaviors of surrounding vehicles and directly relies on measurable traffic data to achieve safe and optimal CAV control in mixed traffic. We present the implementation details ofDeeP-LCC, including data collection, equilibrium estimation, and control execution. Based on a miniature experiment platform, we reproduce the phenomenon of stop-and-go waves in two typical traffic scenarios: 1) open straight-road scenario under external disturbances and 2) closed ring-road scenario with no bottlenecks. Our experiments clearly demonstrate thatDeeP-LCCenables one or a few CAVs to dissipate the traffic waves in both traffic scenarios. These experimental findings validate the great potential ofDeeP-LCCin smoothing practical traffic flow in the presence of noisy data, uncertain low-level vehicle dynamics, and communication and computation delays. The code and videos of our experimental results are available athttps://github.com/soc-ucsd/DeeP-LCC.

Connected and automated vehicles have shown great potential in improving traffic mobility and reducing emissions, especially at unsignalized intersections. Previous research has shown that vehicle passing order is the key influencing factor in improving intersection traffic mobility. In this paper, we propose a graph-based cooperation method to formalize the conflict-free scheduling problem at an unsignalized intersection. Based on graphical analysis, a vehicle’s trajectory conflict relationship is modeled as a conflict directed graph and a coexisting undirected graph. Then, two graph-based methods are proposed to find the vehicle passing order. The first is an improved depth-first spanning tree algorithm, which aims to find the local optimal passing order vehicle by vehicle. The other novel method is a minimum clique cover algorithm, which identifies the global optimal solution. Finally, a distributed control framework and communication topology are presented to realize the conflict-free cooperation of vehicles. Extensive numerical simulations are conducted for various numbers of vehicles and traffic volumes, and the simulation results prove the effectiveness of the proposed algorithms.

Cooperative formation and control of autonomous vehicles (AVs) promise increased efficiency and safety on public roads. In single-lane mixed traffic consisting of AVs and human-driven vehicles (HDVs), the prevailing platooning of multiple AVs is not the only choice for cooperative formation. In this paper, we investigate how different formations of AVs impact traffic performance from a set-function optimization perspective. We first reveal a stability invariance property and a diminishing improvement property of noncooperative formation when AVs adopt an independently designed Adaptive Cruise Control (ACC) strategy. Then, we focus on the case of cooperative formation where AVs utilize a centralized optimal controller. We further investigate the corresponding optimal formation of multiple AVs using set-function optimization. Two predominant optimal formations,i.e., uniform distribution and platoon formation, emerge from extensive numerical experiments. Interestingly, platooning might have the least potential to improve traffic performance when HDVs have poor string stability behavior. These results suggest more opportunities for cooperative formation of AVs, beyond platooning, in practical mixed traffic flow.

Connected and autonomous vehicles (CAVs) have great potential to improve road transportation systems. Most existing strategies for CAVs’ longitudinal control focus on downstream traffic conditions, but neglect the impact of CAVs’ behaviors on upstream traffic flow. In this paper, we introduce a notion of Leading Cruise Control (LCC), in which the CAV maintains car-following operations adapting to the states of its preceding vehicles, and also aims to lead the motion of its following vehicles. Specifically, by controlling the CAV, LCC aims to attenuate downstream traffic perturbations and smooth upstream traffic flow actively. We first present the dynamical modeling of LCC, with a focus on three fundamental scenarios: car-following, free-driving, and Connected Cruise Control. Then, the analysis of controllability, observability, and head-to-tail string stability reveals the feasibility and potential of LCC in improving mixed traffic flow performance. Extensive numerical studies validate that the capability of CAVs in dissipating traffic perturbations is further strengthened when incorporating the information of the vehicles behind into the CAVs’ control.

Connected and automated vehicles (CAVs) have a great potential to improve traffic efficiency in mixed traffic systems, which has been demonstrated by multiple numerical simulations and field experiments. However, some fundamental properties of mixed traffic flow, including controllability and stabilizability, have not been well understood. This paper analyzes the controllability of mixed traffic systems and designs a system-level optimal control strategy. Using the Popov-Belevitch-Hautus (PBH) criterion, we prove for the first time that a ring-road mixed traffic system with one CAV and multiple heterogeneous human-driven vehicles is not completely controllable, but is stabilizable under a very mild condition. Then, we formulate the design of a system-level control strategy for the CAV as a structured optimal control problem, where the CAV’s communication ability is explicitly considered. Finally, we derive an upper bound for reachable traffic velocity via controlling the CAV. Extensive numerical experiments verify the effectiveness of our analytical results and the proposed control strategy. Our results validate the possibility of utilizing CAVs as mobile actuators to smooth traffic flow actively.

The emergence of autonomous vehicles (AVs) is expected to revolutionize road transportation in the near future. Although large-scale numerical simulations and small-scale experiments have shown promising results, a comprehensive theoretical understanding to smooth traffic flow via AVs is lacking. In this article, from a control-theoretic perspective, we establish analytical results on the controllability, stabilizability, and reachability of a mixed traffic system consisting of human-driven vehicles and AVs in a ring road. We show that the mixed traffic system is not completely controllable, but is stabilizable, indicating that AVs can not only suppress unstable traffic waves but also guide the traffic flow to a higher speed. Accordingly, we establish the maximum traffic speed achievable via controlling AVs. Numerical results show that the traffic speed can be increased by over 6% when there are only 5% AVs. We also design an optimal control strategy for AVs to actively dampen undesirable perturbations. These theoretical findings validate the high potential of AVs to smooth traffic flow.

Connected and automated vehicles (CAVs) have a great potential to actively influence traffic systems. This has been demonstrated by large-scale numerical simulations and small-scale real experiments, whereas a comprehensive theoretical analysis is still lacking. In this paper, we focus on mixed traffic systems with one single CAV and heterogeneous human-driven vehicles, and present rigorous controllability analysis and optimal controller synthesis. Using the PBH controllability criterion, we reveal controllability properties of a linearized mixed traffic system in a ring road. It is proved that the mixed traffic flow can be stabilized by one single CAV under a very mild condition. We formulate the problem of designing CAV control strategies under a pre-specified communication topology as structured optimal controller synthesis. This formulation considers a system-level performance index that allows the CAV to actively dampen undesired perturbations in traffic flow. Numerical experiments verify the effectiveness of our results.