Distributed cooperative tracking control has emerged as a pivotal research focus in multi-agent systems, particularly for platoon control applications where its decentralized architecture offers significant advantages over centralized approaches. However, the direct exchange of sensitive data between agents raises critical privacy risks, hindering its broader adoption across safety-critical applications. This paper presents a privacy-preserving cooperative tracking framework that rigorously maintains bounded coupling errors, which is a crucial requirement for collision avoidance in vehicular platoons. Departing from conventional methods that compromise privacy through explicit state sharing for error mitigation, our proposed algorithm achieves dual objectives: maintaining prescribed error constraints while preserving agent state confidentiality in directed communication networks with time-varying interaction weights. We establish sufficient conditions for achieving cooperative-tracking consensus with predefined error constraints and characterize the quantitative relationship between the asymptotic convergence rate and control gain parameters. Furthermore, we analyse the privacy-preserving performance against internal and external adversaries, demonstrating that the probability of an adversary inferring states within a finite neighborhood of ground-truth values can be rendered arbitrarily small, even while adversaries retain access to identical communication data streams. This extends classical initial-state privacy to the entire operational timeline under time-varying directed topologies. Numerical examples including an application of cooperative adaptive cruise control demonstrate our proposed algorithm’s efficacy.

This paper presents an uncertainty-aware exploration framework for cooperative underwater operations using multiple unmanned underwater vehicles (UUVs). The proposed framework leverages prior environmental information to iteratively integrate task planning, task assignment, and prior belief updating, enabling efficient exploration in unknown underwater environments. An interest area selection strategy is proposed to balance the exploration of uncharted regions and the exploitation of areas with high target likelihood. To optimize interest area task allocation, a simultaneous auctionbased mechanism is developed that assigns each interest area to the most suitable UUV by maxing potential information gain while minimizing operational costs. Additionally, to address the computational constraints of UUV systems, a Sparse Gaussian Process (SGP) with variationally optimized inducing points is employed, enabling rapid and accurate fusion of real-time observations with prior environmental information. This approach facilitates dynamic updates of the probabilistic environment representation and interest point selection without compromising accuracy. Experimental results in the HoloOcean simulator demonstrate the framework’s effectiveness in refining the probabilistic environment representation, achieving efficient exploration and accurate target detection in complex underwater scenarios. The results highlight the framework’s capability to dynamically adapt to environmental uncertainties, showcasing its potential for underwater exploration applications.

Navigating restricted waterways is widely considered one of the most stressful phases for ship operators due to the confined navigable areas and the uncertainty of encounter situations. Most existing ship collision avoidance methods are developed for open-water navigation and do not adequately address the unique constraints and uncertainties associated with restricted environments. This paper proposes PROMPTER, a probabilistic motion planning framework designed to assist pilots in navigating restricted waterways. PROMPTER transforms motion planning into an inference task by integrating ship maneuverability, situational awareness, and path planning within a Bayesian factor graph, allowing optimal policies to be derived through posterior inference over control. Within the graph, operational restrictions are encoded as prior knowledge to constrain and guide the control inference process. Furthermore, we theoretically prove the closed-form solution and convergence of the constrained control inference. To ensure that PROMPTER is applicable in diverse operational conditions, we consider scenarios with both reliable and unreliable communication, leading to the development of cooperative and non-cooperative collision avoidance strategies. Experiments conducted in both synthetic and real-world restricted waterways demonstrate that PROMPTER outperforms existing methods by generating collision-free and kinematically feasible paths.

Extensive research has been conducted to explore vehicle lane changes, while the study on cut-ins has not received sufficient attention. The existing studies have not addressed the fundamental question of why studying cut-ins is crucial, despite the extensive investigation into lane changes. To tackle this issue, it is important to demonstrate how cut-ins, as a special type of lane change, differ from other lane changes. In this paper, we explore to compare driving characteristics of cut-ins and other lane changes based on naturalistic driving data. The highD dataset is employed to conduct the comparison. We extract all lane-change events from the dataset and exclude events that are not suitable for our comparison. Lane-change events are then categorized into the cut-in events and other lane-change events based on various gap-based rules. Several performance metrics are designed to measure the driving characteristics of the two types of events. We prove the significant differences between the cut-in behavior and other lane-change behavior by using the Wilcoxon rank-sum test. The results suggest the necessity of conducting specialized studies on cut-ins, offering valuable insights for future research in this field.

For a long period, automated vehicles (AVs) or vehicle platoons will coexist with human-driven vehicles (HDVs) in heterogeneous traffic flow, where the cut-in maneuver of human drivers can be frequently expected. In this paper, to understand and simulate the driver decisions on whether to continue the cut-in and when to execute the lane-change during the cut-in process, we propose a two-layer prediction-based decision model by integrating a dynamic prediction module, a continuity decision module, and an execution decision module. To our best knowledge, this is the first study to model the driver decision behavior of the cut-in process. Cut-in experiments are conducted to collect the decision and control data of drivers under one-and two-target-vehicle scenarios, which both include sixty sub-scenarios with different initial velocities, accelerations, or positions of the vehicles. We prove the effectiveness of the proposed model in simulating the driver decision behavior of the cut-in process by comparing the experimental and simulation results under various scenarios over different subjects. Besides, we analyze the effects of some model parameters on the model performance to show their ability to represent different driving styles.

Vehicle platoons (VPs) are groups of vehicles driving together with a short inter-vehicle gap and a harmonized velocity. For a long period, the VPs and human-driven vehicles (HDVs) will coexist in mixed traffic flow, where the cut-in maneuver of the HDVs towards the VPs can be frequently expected. In this paper, to handle such cut-ins, we propose an intention prediction-based control method for the VPs by considering the tradeoff between the platoon integrity and traffic safety. Particularly, the proposed method is designed to prevent as many cut-ins as possible while taking care of the road safety. It consists of a cut-in prediction part, including intention and trajectory prediction algorithms, and a finite state machine (FSM)-based predictive control part, including a high-level FSM and a low-level predictive control. Driver-in-the-loop experiments were conducted in the VP-based driving scenarios to train the intention prediction algorithm and test the proposed method. We show the results detailing the control behavior of the proposed method in a no cut-in test, a mandatory cut-in test, and three discretionary cut-in tests. The results demonstrate that the proposed method can predict the cut-in intention of human drivers in real time. Besides, according to the prediction results, the proposed method can prevent cut-ins for the VPs while taking care of the road safety.

There is no doubt that in the near future, machines will share roads with human drivers [1] [2]. Therefore, the prediction of human drivers' lane changing behavior is imperative. Lane-change prediction is one of the most important ones. Both human drivers and autonomous vehicles should make sure that no other vehicle switches lanes or moves into the same region of the target lane as the ego vehicle. The existing short-term prediction algorithms can only provide a prediction horizon of 3 ~ 5s, leaving only a limited reaction time for drivers and autonomous path planning modules. Additionally, the majority of previous research analysed less on investigate lane segmentation or merging, simply the inference of lane shift in an expressway context. Most of earlier research only focused on the inference of lane-change in an expressway context, as opposed to the more typical urban environment. There are relatively few of these studies that can handle multi-scenario and scenario switching. In this paper, a Scenario Encoding Model (SEM) is proposed to help solve the problem of long-term lane-change prediction and the scenario switching problem in the existing short-term lane-change prediction. Even in the absence of road history data, the SEM can model the road scenario and encode the real-time road scene by using Self-Organizing Map (SOM) In the mean time, the established initial model has the ability to be further evolved into a historical bias model in the background of a large amount of road historical data. The evaluation test of this SEM has been done through the NGSIM dataset.

A vehicle platoon is a group of vehicles driving together with a harmonized speed and a short inter-vehicle gap by using vehicle automation and vehicle-to-vehicle communication. Platoons have to share road with human-driven vehicles (HDVs) and can only be applied in heterogeneous traffic flow for a long period. Driver cut-in behavior (DCB) towards a platoon can be frequently expected in such driving context. In this paper, to understand and simulate such behavior, we propose a platoon-oriented cut-in behavior (POCB) model by fusing a lateral and a longitudinal control model into the queuing network (QN) cognitive architecture. Platoon-oriented cut-in experiments are conducted to collect driver data under cut-in from back and front scenarios, which both include six sub-scenarios with different platoon gaps or initial velocities. We demonstrate the effectiveness of the proposed model in simulating the DCB towards platoons by comparing experimental and simulation results under various driving scenarios across different subjects.

Using brain signals rather than limbs to drive a vehicle may not only help persons with disabilities to acquire driving ability, but also provide a new alternative interface for healthy people to control a vehicle. However, the longitudinal driving performance of brain-controlled vehicles (BCVs) at a relatively high speed is not good enough. In this paper, to improve the performance of the longitudinal brain-control driving, we propose a new predictive control method based on the models of human behaviors and vehicle dynamics. The proposed method is designed to maintain rear-end safety of BCVs and driver ride comfort while ensuring the maximum control authority of brain-control drivers. Driver-and-hardware-in-the-loop experiments are conducted with different subjects under three kinds of scenarios to validate the proposed method. The results show that the proposed method is effective in maintaining rear-end safety and driver ride comfort while preserving driver intention.

Using brain signals rather than limbs to drive a vehicle can help persons with disabilities to extend their movement range and, thus, to improve their self-independence. However, the driving performance of brain-controlled vehicles (BCVs) is poor. In this paper, to improve the performance of BCVs, we propose a new shared control method based on the model predictive control (MPC) strategy. Particularly, to maintain the maximum control authority of brain-control drivers while ensuring the safety of BCVs, the MPC controller is designed by introducing a penalty on the deviation from drivers output in the cost function and setting safety constraints. Driver-and-hardware-in-the-loop experiments are conducted under two road-keeping scenarios and one obstacle-avoidance scenario with different subjects to validate the proposed method. The results demonstrate the effectiveness of the proposed method in avoiding roadway departures and obstacles while maintaining the control authority of users.

The control performance and safety of current brain-controlled mobile robots are limited. To address this problem, in this paper, we design an assistive controller based on the model predictive control method. The proposed controller fuses tracking user intention and guaranteeing safety of brain-controlled mobile robots into an optimization problem. In this way, the proposed controller can make users control a brain-controlled mobile robot as much as possible given the mobile robot is safe. The experimental results show that the proposed controller can improve the control performance of the brain-controlled simulated mobile robot and guarantee its safety.