Connected and Autonomous Vehicles (CAVs) rely on the Controller Area Network (CAN) bus for critical communications but lack inherent security mechanisms, making them vulnerable to both Denial of Service (DoS) attacks and False Data Injection (FDI) attacks. This paper introduces a Trust-aware Federated Learning-Based Intrusion Detection System (Trustaware FL-IDS) framework that addresses these vulnerabilities while preserving data privacy and minimizing communication overhead. The core innovation is a dynamic trust-aware aggregation mechanism that assigns weights to client contributions based on validation accuracy, effectively mitigating the impact of compromised nodes. Extensive evaluation on both public CAN bus datasets and real-world autonomous vehicle data demonstrates that our approach significantly outperforms centralized IDS implementations, achieving up to $100 \%$ recovery rates against data poisoning attacks with $40 \%$ label manipulation. Notably, our analysis reveals that larger client networks ($\mathbf{1 0 0}$ vs. $\mathbf{1 0}$ clients) provide inherently stronger defenses and that label flip intensity has a greater impact on system performance than the proportion of compromised clients. The proposed framework offers a scalable, lightweight solution for real-time intrusion detection in resource-constrained vehicular environments, applicable in both urban and rural settings.

This article presents a scalable hierarchical distributed control framework to address the problem of collective navigation of a quadcopter swarm, which is a special form of Internet of Vehicles (IoV), in the presence of member drop-outs and interrobot communication link failures. The proposed control framework has three parts: 1) the swarm descriptors (the geometric interpretation of swarm statistics) estimator; 2) the cooperative swarm motion controller; and 3) the attitude controller. The proposed framework includes a novel distributed dynamic average consensus algorithm to estimate the swarm descriptors which represent the collective motion of the swarm in the abstract space (a lower dimensional space independent of the number and permutation of robots in the swarm). By employing a dynamic inversion approach, in the cooperative swarm motion controller, we design a control law that generates the desired thrust, yaw, pitch, and roll angle commands. In the attitude controller, we convert the desired commands from the cooperative swarm motion controller into the rolling, pitching, and yawing moments required to realize the collective motion of the swarm. The robustness of the proposed framework and the stability of the proposed swarm descriptors estimator, the cooperative swarm motion controller, and the attitude controller, and the overall cascaded system are mathematically proved. The significance of the proposed control framework is demonstrated via simulation in the presence of member robot drop-outs and interrobot communication link failures.

In this study, a novel time-driven mathematical model for trust is developed considering human–multi-robot performance for a Human–Robot Collaboration (HRC) framework. For this purpose, a model is developed to quantify human performance considering the effects of physical and cognitive constraints and factors such as muscle fatigue and recovery, muscle isometric force, human (cognitive and physical) workload, workloads due to the robots’ mistakes, and task complexity. The performance of multi-robot in the HRC setting is modeled based upon the rate of task assignment and completion as well as the mistake probabilities of the individual robots. The human trust in HRC setting with single and multiple robots is modeled over different operation regions, namely unpredictable region, predictable region, dependable region, and faithful region. The relative performance difference between the human operator and the robot is used to analyze the effect on the human operator’s trust in robots’ operation. The developed model is simulated for a manufacturing workspace scenario considering different task complexities and involving multiple robots to complete shared tasks. The simulation results indicate that for a constant multi-robot performance in operation, the human operator’s trust in robots’ operation improves whenever the comparative performance of the robots improves with respect to the human operator performance. The impact of robot hypothetical learning capabilities on human trust in the same HRC setting is also analyzed. The results confirm that a hypothetical learning capability allows robots to reduce human workloads, which improves human performance. The simulation result analysis confirms that the human operator’s trust in the multi-robot operation increases faster with the improvement of the multi-robot performance when the robots have a hypothetical learning capability. An empirical study was conducted involving a human operator and two collaborator robots with two different performance levels in a software-based HRC setting. The experimental results closely followed the pattern of the developed mathematical models when capturing human trust and performance in terms of human–multi-robot collaboration.

This paper proposes a vision-based control method for autonomous vehicle lane keeping. This paper aims to provide a reliable alternative solution for localization and path planning-related challenges in environments where GPS is unavailable or unreliable, such as rural areas with dense forest cover, where tree canopies may obstruct GPS signals, and urban landscapes characterized by tall buildings that can similarly disrupt navigation accuracy. The proposed method consists of a robust lane detection algorithm to generate a reference path of the vehicle and a model predictive controller (MPC) for tracking the reference path. The lane detector extracts lane markings from image frames to determine ego-lane boundaries from which the lane center is calculated in the vehicle’s coordinate frame. The MPC uses a kinematic vehicle model to generate the lateral and longitudinal control values necessary for smoothly tracking the reference path. The proposed technique has been implemented and tested on a Lincoln MKZ hybrid vehicle equipped with a computing platform having Intel’s quad-core Xeon processors. The developed framework was experimentally validated by deployment in a rural test track. Experimental results show that the proposed vision-based lane detection method performs well under various challenging road conditions such as shadows, road texture variations, interference from other road signs, and missing lane boundaries. The multi-threaded implementation of lane detection and the MPC allowed us to run the integrated system at the speed of 25 HZ. The integration of lane detection and MPC resulted in smoothly keeping the car in the center of the lane over a curved test track while respecting the physical constraints of the car.

Urban air mobility (UAM) is an emerging aviation concept envisioned to provide urban transportation with safe and on-demand air travel for both passengers and cargo. In UAM, maintaining a minimum separation between aircraft is a safety requirement that becomes challenging to meet amidst congested air traffic and off-nominal conditions, such as Loss of Control In-flight (LOC-I) of aircraft. Often, separation loss caused by LOC-I results in subsequent separation losses, triggering domino effects as one aircraft maneuvers to avoid another aircraft experiencing LOC-I in congested air traffic. This paper therefore presents an on-the-fly, integrated decision-making and trajectory planning framework to address the separation losses caused by LOC-I. A Behavior Tree-based decentralized decision-making is employed to execute a separation assurance task assigned by the provider of service to UAM (PSU), coordinating and selecting the necessary maneuvers based on Federal Aviation Administration (FAA) right-of-way rules to resolve the encountered separation losses. To execute the selected maneuver, an optimization-based receding horizon trajectory planning is employed to generate safe and optimal trajectories by taking into account the aircraft dynamics, maneuver direction, and minimum safe separation distance between aircraft. Further, a UAM simulation environment is developed for verification and testing of UAM traffic management algorithms, allowing the implementation of on-the-fly conflict detection, tasking, and planning algorithms to resolve the separation losses. The effectiveness of the proposed framework is demonstrated by considering various separation loss scenarios arising from LOC-I, such as overtaking, converging, and head-on separation losses, within the developed UAM simulation environment.

This article develops a novel active-learning technique for fault diagnosis of an initially unknown finite-state discrete event system (DES). The proposed method constructs a diagnosis tool (termed diagnoser), which is able to detect and identify occurred faults by tracking the observable behaviors of the system under diagnosis. The proposed algorithm utilizes an active-learning mechanism to incrementally collect the information about the system to construct the diagnoser. This is achieved by completing a series of observation tables in a systematic way, resulting in the construction of the diagnoser. It is proven that the proposed algorithm terminates after a finite number of iterations and returns a correctly conjectured diagnoser. The developed diagnoser is a deterministic finite-state automaton. Furthermore, we have proven that the developed diagnoser consists of a minimum number of states. A sufficient condition for diagnosability of the system under diagnosis is derived, which guarantees the diagnosis of faults within a bounded number of observations. The developed method is applied to two case-studies, illustrating the steps of the proposed algorithm and its capability of diagnosing multiple faults.

This paper proposes a novel data-driven technique, namely Online Learning for sensor Fault diagnosis Analysis (OLFA), to perform real-time fault analysis for autonomous cars. Considering the non-stationary properties of real-time sensor faults and the mapping relationship between sensors and feature variables, the proposed method decomposes the sensor fault diagnosis analysis problem into an online data stream classification and feature ranking problems. To detect and identify faults, a clustering-based data stream classification approach is developed to continuously capture and classify non-stationary sensor faults for autonomous cars with little intervention from human experts. An effective active learning method is extended and embedded into the proposed framework to minimize the need for prior knowledge about faults and enable the continual learning capability to adapt to and handle the non-stationary properties of sensor faults. Moreover, the proposed framework addresses the parameter optimization issue of existing machine learning based fault analysis techniques and employs feature ranking analysis to systematically analyze the possible source(s) of sensor faults. CAR Learning to Act (CARLA), a well-known realistic autonomous driving simulator, is used as the benchmark to perform the sensor fault injection and online data stream collection to evaluate the efficacy of OLFA. Analysis of the collected faulty datasets and experimental results, and comparison between OLFA and several state-of-the-art clustering-based approaches for fault classification, demonstrated the efficacy of the proposed framework in the domain of autonomous cars.

Realtime estimation of travel time is a key traffic parameter for designing and planning for transportation systems, particularly when providing mobility-on-demand (MOD) services. However, the analysis and prediction of travel time can be delayed significantly due to the complexity and huge computational requirements of microsimulation models. Thus, as an alternative solution, we propose a data-driven approach for the efficient and reliable prediction of travel time. Our approach takes advantage of the strengths of SVM and ARIMA for fully capturing the traffic patterns in the traffic data. We introduce a new parameter $\kappa$ into the SVM-ARIMA model to adjust the weight of the ARIMA component, which significantly improves the performance. We validate the performance of the proposed approach using data generated from a microsimulation platform. Our experimental results and comparisons with the existing ML-based methods demonstrates the efficacy of the proposed data-driven approach.

In this article, an adjustable autonomy framework is proposed for the human–robot collaboration (HRC) in which a robot uses a reinforcement learning mechanism guided by a human operator’s rewards in an initially unknown workspace. Within the proposed framework, the autonomy level of the robot is automatically adjusted in an HRC setting that is represented by a Markov decision process model. When the robot reaches higher performance levels, it can operate more autonomously in the sense that it needs less human operator intervention. A novel$Q$-learning mechanism with an integrated$\epsilon$-greedy approach is implemented for robot learning in order to capture the correct actions and robot’s mistakes as a basis for adjusting the robot’s autonomy level. The proposed HRC framework can adapt to changes in the workspace as well as changes in the human operator reward (scaling and shifting) mechanism, and can always adjust the autonomy level. The autonomy level of the robot is automatically lowered when the workspace changes to allow the robot to explore new actions in order to adapt to the new workspace. In addition, the human operator has the ability to reset/lower the autonomy level of the robot to enforce the robot to relearn the workspace if its performance is not satisfactory for the human operator. The developed algorithm is applied to a realistic HRC setting involving a humanoid robot, named Baxter. The experimental results are analyzed to assess the effectiveness of the proposed adjustable autonomy framework for different cases: for the case when the workspace does not change, then for the case when the robot autonomy level is reset/lowered by a human operator, and for the case when the workspace is changed by the introduction of new objects. The results confirm the capability of the developed framework to successfully adjust the autonomy level in response to changes in the human operator’s commands or the workspace.

Network fusion has been recently explored as an approach for improving pedestrian detection performance. However, most existing fusion methods suffer from runtime efficiency, modularity, scalability, and maintainability due to the complex structure of the entire fused models, their end-to-end training requirements, and sequential fusion process. Addressing these challenges, this paper proposes a novel fusion framework that combines asymmetric inferences from object detectors and semantic segmentation networks for jointly detecting multiple pedestrians. This is achieved by introducing a consensus-based scoring method that fuses pair-wise pixel-relevant information from the object detector and the semantic segmentation network to boost the final confidence scores. The parallel implementation of the object detection and semantic segmentation networks in the proposed framework entails a low runtime overhead. The efficiency and robustness of the proposed fusion framework are extensively evaluated by fusing different state-of-the-art pedestrian detectors and semantic segmentation networks on a public dataset. The generalization of fused models is also examined on new cross pedestrian data collected through an autonomous car. Results show that the proposed fusion method significantly improves detection performance while achieving competitive runtime efficiency.

This paper presents a novel method for pedestrian detection and tracking by fusing camera and LiDAR sensor data. To deal with the challenges associated with the autonomous driving scenarios, an integrated tracking and detection framework is proposed. The detection phase is performed by converting LiDAR streams to computationally tractable depth images, and then, a deep neural network is developed to identify pedestrian candidates both in RGB and depth images. To provide accurate information, the detection phase is further enhanced by fusing multi-modal sensor information using the Kalman filter. The tracking phase is a combination of the Kalman filter prediction and an optical flow algorithm to track multiple pedestrians in a scene. We evaluate our framework on a real public driving dataset. Experimental results demonstrate that the proposed method achieves significant performance improvement over a baseline method that solely uses image-based pedestrian detection.

The growth in the transportation sector has led to an enormous increase in the number of vehicles that ply our roads daily. Even though this advancement has provided numerous transportation modes, it has resulted in serious transportation issues including road congestion. Hence, estimating the number of vehicles on a road will enable traffic managers to take appropriate decisions to curb congestion. In this paper, we propose to use an extreme gradient boosting (XGBoost) algorithm to efficiently and accurately predict the hourly traffic volume. We investigate the effectiveness of the proposed method for different scenarios including how well it performs during extreme weather conditions and holidays. We further investigate the effect of ridge and LASSO regularization on the performance of XGBoost. We then propose a new approach for setting the LASSO regularization parameter in terms of the number of observations and predictors. The performance and computational efficiency of the proposed approach is evaluated on data collected from Interstate-94, Minnesota and the results are compared with existing methods. The results show that the proposed method provides a good balance between performance and computational efficiency.

This paper presents a continuous dynamic average consensus (DAC) algorithm for a group of agents to estimate the average of their time-varying reference signals cooperatively. We propose a consensus algorithm that is robust to agents joining and leaving the network, at the same time, avoid the chattering phenomena and guarantee zero steady-state consensus error. Our algorithm is an edge-based protocol with smooth functions in its internal structure to avoid the chattering effect. Furthermore, each agent can only perform local computations and can only communicate with its local neighbors. For a balanced and strongly connected underlying communication graph, we provide the convergence analysis to determine the consensus design parameters that guarantee the estimate of the average to asymptotically converge to the average of the time-varying reference signals. We provide simulation results to validate the proposed consensus algorithm and perform a performance comparison of the proposed algorithm to existing algorithms in the literature.

In this work, we address the problem of detecting body parts of pedestrians using deep neural networks. In particular, we consider the occluded pedestrian detection problem in autonomous driving settings. While state-of-the-art deep neural models perform reasonably well for detecting full-body pedestrians, their performances are not satisfactory for occluded pedestrians. Introducing a new training strategy along with a fusion mechanism, we enhance the performance of the SSD-Mobilenet and the Faster R-CNN by utilizing body parts information to handle occluded pedestrians. We evaluate our method by training these two deep neural networks using a public dataset as well as our dataset. The performance of the two developed models is compared both in terms of detection accuracy and runtime efficiency.

In this paper, a time-driven performance-aware mathematical model for trust in the robot is proposed for a Human-Robot Collaboration (HRC) framework. The proposed trust model is based on both the human operator and the robot performances. The human operator's performance is modeled based on both the physical and cognitive performances, while the robot performance is modeled over its unpredictable, predictable, dependable, and faithful operation regions. The model is validated via different simulation scenarios. The simulation results show that the trust in the robot in the HRC framework is governed by robot performance and human operator's performance and can be improved by enhancing the robot performance.

With advances in technologies, robots can be employed in collaboration with human for completing the shared objective(s). This paper proposes a novel time-variant human cognitive performance modeling approach for human-robot collaborative actions. The proposed model considers human cognitive performance as a function of human cognitive workload, robot performance, and human physical performance. Novel about the proposed model is its ability to relate human cognitive workload and the task complexity to a utilization factor which is functionally correlated with the robot's mistake probability. The developed model is validated via a simulation environment and confirms that if the task complexity or the robot's mistake probability increases, human cognitive performance reduces over time.

Vision-based lane detection for intelligent vehicles is a well-researched problem in the past decades. However, there are still many road conditions in which the lane marking extraction is very challenging. In this paper, a new lane marking extraction algorithm that performs better than the traditional Canny edge detector and Hough transform based techniques is proposed. The proposed system uses bird's eye view image with a 2D Gabor filter for lane marker enhancement followed by a marking extraction approach and a Bezier curve fitting technique. Preliminary test results show that the proposed algorithm works very well on highways and urban roads despite various environmental challenges such as shadows due to trees or bridges, road texture variations, and lighting conditions. In addition, the algorithm can run in real time at a rate of 25 frames per second for images of size 1280×720 pixels. Testing computer has Intel Core i7 processor with 8GB RAM and 3.6GHz frequency.

Collision avoidance is an essential component in advanced driving assistance systems, as it ensures the safety of the vehicle in near crash or crash scenarios. In this study, a collision avoidance system for lane change events is proposed which plans the trajectory based on the level of danger. The danger level is computed by a fuzzy inference system developed with naturalistic driving data to better capture the real-world factors, which may cause an accident. In addition, a fault determination classifier is introduced in order to determine the responsible driver in a near crash event. This system is evaluated on simulated naturalistic near crash events and the results demonstrate good performance of the proposed system.

In this paper, driver intention estimation near a road intersection is presented, using discrete hidden Markov models (HMM) and the Hybrid State System (HSS) framework as basis. The development of Advanced Driver Assistance Systems (ADAS) has assisted drivers in many driving scenarios and resulted in safe driving. Developing techniques to estimate driver's intention leads to the advancement of ADAS. As a large number of accidents occur near road intersections, estimating the intention of a driver at an intersection is vital. The methods developed in this paper can be applied in ADAS to take appropriate measures in reducing accidents. The driver decisions are depicted as a Discrete State System (DSS) at a higher level and the continuous vehicle dynamics are depicted as a Continuous State System (CSS) at a lower level in the HSS framework. In the proposed technique, the vehicle's continuous observations including speed and yaw-rate, are used to estimate the driver's intention at each time step. In this work, the speed and yaw-rate are discretized in such a way that the important features about the driver's intention such as "go straight," "stop," "turn right," or "turn left" at the intersection, are abstractedly represented in the form of symbols. Naturalistic driving data, which is collected using a vehicle fitted with sensors, is used to train and test the developed model. The results from the proposed approach show high accuracy in estimating the driver's intention at a road intersection.

Advanced driver assistance systems are required to detect latent hazards posed by surrounding vehicles and generate an appropriate response to enhance safety. Lane changes constitute potentially risky maneuvers, as drivers involved encounter latent hazards due to surrounding vehicles. A careful study of lane change behavior is therefore essential in identifying potential abnormalities that may lead to various hazards, during the process of a lane change. In this study, an anomaly detection technique is used to compare snapshots of normal and dangerous lane change maneuvers, to identify the abnormal instances. A one-class support vector machine is used and tested for novelty identification of naturalistic driving study data. The results show that the technique is able to detect dangerous lane changes with high accuracy. In addition, results suggest that dangerous behavior could occur before, after or during a lane change maneuver.

This study considers offline identification of switched linear MIMO systems using measurements from their inputs and outputs. This is a class of non-convex optimization and ill-posed problems. To convert this optimization into a binary integer programming problem, the proposed approach assumes that the number of switches among the subsystems is upper-bounded. The state-space realization of each sub-system is found by the subspace identification. The proposed approach does not need the tuning of the moving window size or any penalization factor. The algorithm efficiency is evaluated through numerical simulations. The results indicate that the error of identification is small and the eigenvalues of sub-systems are estimated successfully.

This paper addresses the motion planning problem for two autonomous robots (the defender and the attacker) with competitive objectives, which are involved in a reach-avoid scenario. This adversarial aspect of the game makes the problem complex with high computational cost. To address this problem, we propose a novel symbolic approach for the robot motion planning and control of the robots, which can effectively manage the complexity of the problem. The basic idea is to partition the environment into convex regions, and then, capture the desired objectives of the defender and the adversarial behavior of the attacker with temporal logic formulas. We also use finite two-player zero-sum games as a tool for the robot decision-making over the partitioned space. An illustrative examples has been provided to detail the steps of the proposed algorithm and the simulation results are presented to verify the effectiveness of the proposed algorithm.

This paper proposes a diagnostics tool for a Discrete-Event System (DES) under uncertain activation conditions. This diagnosis tool, the diagnoser (as it is called), detects, identifies, and locates system faults in relation to a set of states of which the system under diagnosis could possibly be located, upon the diagnoser's instance of activation. This diagnoser is designed to diagnose system faults that occur prior to and/or after the diagnoser's activation; thus removing the procedural constraint of initializing the system and diagnoser synchronously. Illustrative examples are provided to detail the proposed diagnosis procedure.

Reverse Engineering of Gene Regulatory Networks (GRN), i.e. Finding appropriate mathematical models to understand complex cellular systems, can be used in disease diagnosis, treatment, and drug design. There are fundamental gaps in the construction of GRN with regard to modeling of hidden/delayed interactions. Addressing these deficiencies is critical to understanding complex intracellular processes and enabling full use of the vast and ever-growing amount of available genomic data. Current modeling strategies either ignore or oversimplify time delays resulted from transcription and translation processes during gene expression. In addition, many research works do not account hidden variables such as transcription factors, repressors, small metabolites, DNA, microRNA species that regulate themselves and other genes but are not readily detectable on micro array experiments. To capture the effect of these parameters, in this paper, we utilize our developed Partially Connected Artificial Neural Networks with Evolvable Topology (PANNET) to find a more comprehensive model of GRN by considering the effects of unknown hidden variables and different time delays. This method is innovative, since the structure of the network has memory and internal states, which can model the unknown hidden variables and time delays. We furthermore use a new evolutionary optimization based on variable-length Genetic Algorithm (GA) to find a sparse structure of PANNET to predict the gene expression levels accurately. Finally we demonstrate the capability of PANNET in constructing GRN, including the effect of different delays and unknown hidden variables through modeling of E. Coli SOS inducible DNA repair system.