Network-level traffic signal control is an effective way to increase throughput and reduce congestion. The max-pressure algorithm, known for maximizing network throughput, has been widely studied. However, it requires accurate queue length and turn ratio measurements, and its theoretical guarantee is limited to feasible demand (i.e., demand within the capacity region) under the assumption of infinite queue capacity. To overcome these limitations, this study proposes a distributed joint admission and signal control algorithm for finite-capacity networks with both connected and regular vehicles. By using feedback from connected vehicles, the algorithm estimates queue lengths and turn ratios, reducing reliance on precise measurements. It also adaptively adjusts input flow rates to prevent oversaturation and ensure demand feasibility, even under high-demand conditions, while optimizing signal phases to ensure analytic performance. Using a Lyapunov drift optimization approach, we analytically prove a$[O(1/V), O(V)]$tradeoff between throughput and delay and establish degradation bounds that quantify the impact of queue length estimation errors on network performance. Simulations in a network with 256 origin-destination pairs show up to a 16.3% increase in throughput and reduced delays, especially in high-demand settings. The method also demonstrates strong resilience to sudden demand changes and incidents, ensuring quick recovery.

Using the backstepping technique, this paper formulates innovative adaptive finite-time stabilizing controllers for uncertain nonlinear systems featuring nonuniform input quantization and asymmetric, time-varying output constraints. These novel controllers leverage the consistent characteristics of both hysteresis quantizers and logarithmic quantizers. Quantization errors, when consistent, become unbounded and contingent on control input, rendering them incompatible with the growth conditions of nonlinear systems. Consequently, the developed adaptive controllers eliminate the reliance on growth conditions, effectively addressing the impact of unbounded quantization errors on finite-time stability. This adaptability allows the controllers to function effectively with systems employing either hysteresis quantizers or logarithmic quantizers. The paper establishes the convergence of these controllers through the finite-time Lyapunov stability theorem. It also provides a comprehensive guideline for tuning settling time, enabling fine-grained control over finite-time convergence and adjustable tracking error performance. Additionally, the controllers rigorously maintain system output within predefined limits. Their effectiveness and low computational burden are demonstrated through three comparative numerical simulations and a practical simulation in collision-free trajectory tracking control of an autonomous vehicle platoon using the vehicle motion software CarSim. These simulations confirm the advanced performance of the adaptive controllers.Note to Practitioners—This paper introduces an innovative approach to control uncertain nonlinear systems encountering intricate input quantization and output constraints. Employing the sophisticated backstepping technique, the authors present adaptive finite-time-stabilizing controllers engineered to address nonuniform input quantization and asymmetric, time-varying output restrictions. What distinguishes these controllers is their reliance on the consistent behavior exhibited by hysteresis and logarithmic quantizers. This unique feature equips them to effectively counteract unbounded quantization errors influenced by control input. Most notably, these controllers eliminate the conventional growth conditions typically demanded by nonlinear systems. As a result, they extend their applicability to a broad spectrum of systems employing either hysteresis or logarithmic quantizers. The research also provides practitioners with a valuable guideline for precisely adjusting settling time. This enables the attainment of desired convergence rates while permitting adaptable tracking error performance. Additionally, these controllers guarantee that the system’s output adheres to predefined limits. The practical significance of this study is highlighted through three comparative numerical simulations and a real-world application simulation. This real-world simulation involves collision-free trajectory tracking control of an autonomous vehicle platoon, executed using the vehicle motion software CarSim. These simulations unequivocally demonstrate the effectiveness and low computational burden of the developed controllers, thereby establishing them as a valuable resource for practitioners facing complex control challenges in various domains.

With the rapid development of V2V and V2I communication technologies and autonomous control systems, autonomous vehicles (AVs) are gaining increasing popularity. Small-spacing AV platoons offer advantages such as enhanced road capacity and energy efficiency. However, in non-ideal communication environments, packet loss can cause partial loss of trajectory information, resulting in discontinuous tracking. This may induce significant transients and trigger actuator saturation, aggravating traffic disturbances. In bidirectional platoons, where control signals propagate in both directions, the impact of such disruptions is further amplified due to mutual vehicle interdependence. This paper addresses these challenges by considering asymmetric actuator saturation, discontinuous tracking trajectories, and non-zero initial spacing errors in bidirectional AV platoons. A continuous control law is designed based on coupled sliding mode control, and Lyapunov stability theory is employed to ensure both trajectory tracking stability and string stability. Our contributions include the development of a modified spacing policy that not only eliminates large transients and string instability caused by non-zero initial spacing errors but also ensures rapid convergence to the desired spacing within a finite and adjustable time frame. Furthermore, a variant sigmoid function is introduced to actively smooth the discontinuous tracking trajectories, thereby reducing communication demands and suppressing transients. An auxiliary system is also designed to manage actuator saturation effectively, ensuring provable stability and fully leveraging actuator capabilities. Results demonstrate that the control strategy achieves both trajectory tracking stability and string stability, while also enabling rapid tracking performance and maintaining small spacing errors by making full use of actuator potential.

Connected vehicles (CVs) are conductive to promoting the transition from purely regular vehicles to purely connected autonomous vehicles where CVs are regarded as regular vehicles equipped with driver assistance systems (DASs). CVs can share status information (i.e., position, velocity, etc.) between each other through vehicle-to-vehicle communication technology, and DASs can provide CV drivers with motion suggestions (e.g., optimal velocity, etc.) based on the shared information. However, CV drivers may not completely follow these suggestions, and may combine them with their own driving experience and perception of traffic information which may be influenced by the interference of vehicles on the adjacent lane. Hence, this paper proposes a two-lane car-following model to simulate CVs under connected environment. The proposed model incorporates the compliance rate of CV drivers to DASs and considers the interference of vehicles on the adjacent lane to CV drivers by introducing the visual angle and its change rate of CV drivers. Linear stability analysis and numerical simulations of homogeneous and heterogeneous traffic flow are performed. Results show that the increases in the penetration rate of CVs and the compliance rate of CV drivers promote traffic stability, while the interference of vehicles on the adjacent lane reduces traffic stability.

Crowdsourcing data from connected and automated vehicles (CAVs) is a cost-efficient way to achieve high-definition maps with up-to-date transient road information. Achieving the map with deterministic latency performance is, however, challenging due to the unpredictable resource competition and distributional resource demands. In this paper, we propose CoMap, a new crowdsourcing high definition (HD) map to minimize the monetary cost of network resource usage while satisfying the percentile requirement of end-to-end latency. We design a novel CROP algorithm to learn the resource demands of CAV offloading, optimize offloading decisions, and proactively allocate temporal network resources in a fully distributed manner. In particular, we create a prediction model to estimate the uncertainty of resource demands based on Bayesian neural networks and develop a utilization balancing scheme to resolve the imbalanced resource utilization in individual infrastructures. We evaluate the performance of CoMap with extensive simulations in an automotive edge computing network simulator. The results show that CoMap reduces up to 80.4% average resource usage as compared to existing solutions.

Automating configuration is the key path to achieving zero-touch network management in ever-complicating mobile networks. Deep learning techniques show great potential to automatically learn and tackle high-dimensional networking problems. The vulnerability of deep learning to deviated input space, however, raises increasing deployment concerns under unpredictable variabilities and simulation-to-reality discrepancy in real-world networks. In this paper, we propose a novel RoNet framework to improve the robustness of neural-assisted configuration policies. We formulate the network configuration problem to maximize performance efficiency when serving diverse user applications. We design three integrated stages with novel normal training, learn-to-attack, and robust defense method for balancing the robustness and performance of policies. We evaluate RoNet via the NS-3 simulator extensively and the simulation results show that RoNet outperforms existing solutions in terms of robustness, adaptability, and scalability.

Cooperative perception is the key approach to augment the perception of connected and automated vehicles (CAVs) toward safe autonomous driving. However, it is challenging to achieve real-time perception sharing for hundreds of CAVs in large-scale deployment scenarios. In this paper, we propose AdaMap, a new high-scalable real-time cooperative perception system, which achieves assured percentile end-to-end latency under time-varying network dynamics. To achieve AdaMap, we design a tightly coupled data plane and control plane. In the data plane, we design a new hybrid localization module to dynamically switch between object detection and tracking, and a novel point cloud representation module to adaptively compress and reconstruct the point cloud of detected objects. In the control plane, we design a new graph-based object selection method to un-select excessive multi-viewed point clouds of objects, and a novel approximated gradient descent algorithm to optimize the representation of point clouds. We implement AdaMap on an emulation platform, including realistic vehicle and server computation and a simulated 5G network, under a 150-CAV trace collected from the CARLA simulator. The evaluation results show that, AdaMap reduces up to 49x average transmission data size at the cost of 0.37 reconstruction loss, as compared to state-of-the-art solutions, which verifies its high scalability, adaptability, and computation efficiency.

Autonomous vehicle (AV) platoons, especially those with the bidirectional communication topology, have significant practical value, as they not only increase link capacity and reduce vehicle energy consumption, but also reduce the consumption of communication resources. Small gaps between AVs in a platoon easily lead to emergency braking or even collisions between consecutive AVs. This paper applies barrier Lyapunov functions to collision avoidance between AVs in a bidirectional platoon during trajectory tracking. Based on backstepping technique, an adaptive collision-free platoon trajectory tracking control algorithm is developed to distributedly design control laws for each AV in the platoon. The control algorithm does not need to introduce additional car-following models to simulate AV driving, and only needs to integrate the position trajectories of consecutive AVs to avoid inter-vehicle collisions. Two sign functions are introduced into the control laws of each AV to ensure strong string stability for bidirectional AV platoons. Moreover, uncertainties and external disturbances in vehicle motion are effectively compensated by introducing adaptation laws. Strong string stability is rigorously proved. CarSIM-based comparison simulations verify the effectiveness of the proposed control algorithm in avoiding inter-vehicle collisions, compensating for uncertainties in vehicle motion, and suppressing the amplification of spacing errors along the platoon.

Connected autonomous vehicle technology is conductive to promoting the transition from traditional merging control (e.g., ramp metering) to automated merging control. This paper proposes a platoon-based hierarchical merging control algorithm for on-ramp vehicles to achieve automated merging control under connected traffic environment. The proposed algorithm optimizes merging maneuvers of on-ramp vehicles to smooth their merging trajectories without frequent decelerations or stops at the end of the ramp, and to minimize disruption to the mainline traffic in the merging zone. A tactical layer controller is designed to select pre-target merging gaps for on-ramp vehicles, in which the future motion (i.e., acceleration and deceleration) of mainline vehicles is considered through the grey prediction model. An operational layer controller is constructed based on model predictive control to adjust the speed of on-ramp vehicles in advance, and controls on-ramp vehicles to merge into the pre-target merging gaps under state constraints (i.e., safe headway, maximum speed and so on). Through numerical simulation, the effectiveness of the proposed algorithm is validated under different merging scenarios. It is shown that on-ramp vehicles smoothly merge into the mainline within the pre-target merging gap at the same speed as adjacent mainline vehicles. Compared with the baseline merging control algorithm, the proposed algorithm significantly reduces both fuel consumption and travel time of on-ramp vehicles, and improves passenger comfort.