Large language models (LLMs) have shown significant potential in guiding embodied agents to execute language instructions across a range of tasks, including robotic manipulation and navigation. However, existing methods are primarily designed for static environments and do not leverage the agent's own experiences to refine its initial plans. Given that real-world environments are inherently stochastic, initial plans based solely on LLMs' general knowledge may fail to achieve their objectives, unlike in static scenarios. To address this limitation, this study introduces the Experience-and-Emotion Map (E2Map), which integrates not only LLM knowledge but also the agent's real-world experiences, drawing inspiration from human emotional responses. The proposed methodology enables one-shot behavior adjustments by updating the E2Map based on the agent's experiences. Our evaluation in stochastic navigation environments, including both simulations and real-world scenarios, demonstrates that the proposed method significantly enhances performance in stochastic environments compared to existing LLM-based approaches. The code and supplementary materials are available at https://e2map.github.io/.

In vision-based robot localization and SLAM, Visual Place Recognition (VPR) is essential. This paper addresses the problem of VPR, which involves accurately recognizing the location corresponding to a given query image. A popular approach to vision-based place recognition relies on low-level visual features. Despite significant progress in recent years, place recognition based on low-level visual features is challenging when there are changes in scene appearance. To address this, end-to-end training approaches have been proposed to overcome the limitations of hand-crafted features. However, these approaches still fail under drastic changes and require large amounts of labeled data to train models, presenting a significant limitation. Methods that leverage high-level semantic information, such as objects or categories, have been proposed to handle variations in appearance. In this paper, we introduce a novel VPR approach that remains robust to scene changes and does not require additional training. Our method constructs semantic image descriptors by extracting pixel-level embeddings using a zero-shot, language-driven semantic segmentation model. We validate our approach in challenging place recognition scenarios using real-world public dataset. The experiments demonstrate that our method outperforms non-learned image representation techniques and off-the-shelf convolutional neural network (CNN) descriptors. Our code is available at https://github.com/woo-soojin/context-based-vlpr.

The presence of Non-Line-of-Sight (NLoS) blind spots resulting from roadside parking in urban environments poses a significant challenge to road safety, particularly due to the sudden emergence of pedestrians. mmWave technology leverages diffraction and reflection to observe NLoS regions, and recent studies have demonstrated its potential for detecting obscured objects. However, existing approaches predominantly rely on predefined spatial information or assume simple wall reflections, thereby limiting their generalizability and practical applicability. A particular challenge arises in scenarios where pedestrians suddenly appear from between parked vehicles, as these parked vehicles act as temporary spatial obstructions. Furthermore, since parked vehicles are dynamic and may relocate over time, spatial information obtained from satellite maps or other predefined sources may not accurately reflect real-time road conditions, leading to erroneous sensor interpretations. To address this limitation, we propose an NLoS pedestrian localization framework that integrates monocular camera image with 2D radar point cloud (PCD) data. The proposed method initially detects parked vehicles through image segmentation, estimates depth to infer approximate spatial characteristics, and subsequently refines this information using 2D radar PCD to achieve precise spatial inference. Experimental evaluations conducted in real-world urban road environments demonstrate that the proposed approach enhances early pedestrian detection and contributes to improved road safety. Supplementary materials are available at https://hiyeun.github.io/NLoS/.

Robots operating in human-centric or hazardous environments must proactively anticipate and mitigate dangers beyond basic obstacle detection. Traditional navigation systems often depend on static maps, which struggle to account for dynamic risks, such as a person emerging from a suddenly opening door. As a result, these systems tend to be reactive rather than anticipatory when handling dynamic hazards. Recent advancements in pre-trained large language models and vision-language models (VLMs) create new opportunities for proactive hazard avoidance. In this work, we propose a zero-shot language-as-cost mapping framework that leverages VLMs to interpret visual scenes, assess potential dynamic risks, and assign risk-aware navigation costs preemptively, enabling robots to anticipate hazards before they materialize. By integrating this language-based cost map with a geometric obstacle map, the robot not only identifies existing obstacles but also anticipates and proactively plans around potential hazards arising from environmental dynamics. Experiments in simulated and diverse dynamic environments demonstrate that the proposed method significantly improves navigation success rates and reduces hazard encounters, compared to reactive baseline planners. Code and supplementary materials are available at https://github.com/Taekmino/LaC.

Pedestrians Localization in Non-Line-of-Sight (NLoS) regions within urban environments poses a significant challenge for autonomous driving systems. While mmWave radar has demonstrated potential for detecting objects in such scenarios, the 2D radar point cloud (PCD) data is susceptible to distortions caused by multipath reflections, making accurate spatial inference difficult. Additionally, although camera images provide high-resolution visual information, they lack depth perception and cannot directly observe objects in NLoS regions. In this paper, we propose a novel framework that interprets radar PCD through road layout inferred from camera for localization of NLoS pedestrians. The proposed method leverages visual information from the camera to interpret 2D radar PCD, enabling spatial scene reconstruction. The effectiveness of the proposed approach is validated through experiments conducted using a radar-camera system mounted on a real vehicle. The localization performance is evaluated using a dataset collected in outdoor NLoS driving environments, demonstrating the practical applicability of the method.

Autonomous vehicles are increasingly utilized in diverse industries, relying heavily on perception systems to interpret their surroundings for decision-making and control. While Line-of-Sight perception technologies have advanced significantly, Non-Line-of-Sight (NLoS) perception remains a critical challenge. Current systems struggle to detect objects in NLoS scenarios, such as pedestrians or vehicles suddenly appearing from behind obstacles, leading to accidents, particularly at narrow T-junctions in urban environments. To address this, mmWave radar has emerged as a promising sensor for NLoS perception due to its ability to capture reflections and estimate the location of dynamic objects in occluded areas. However, previous researches are limited to controlled settings or single objects, with challenges like multipath reflections requiring precise spatial analysis for real-world use. In this paper, we propose a localization method for multi-dynamic NLoS pedestrians using ray tracing on 2D radar point clouds obtained from mm Wave radar in outdoor environments. The approach involves inferring spatial information from static points, performing ray tracing for dynamic points, and applying noise filtering and clustering to estimate pedestrian locations. Validation on a custom-built test bed demonstrates the effectiveness of the method, establishing a foundation for advanced NLoS perception technologies in real-world driving.

Sound can be utilized to gather information about vehicles approaching a Non-Line-of-Sight (NLoS) region that remains hidden from Line-of-Sight (LoS) sensors due to its reflective and diffractive characteristics, like a radar. However, due to the inability to determine the location of NLoS vehicles in previous studies, it has not been possible to construct a sound-based active emergency braking system. This paper introduces a novel approach for localization of vehicles approaching in NLoS regions through sound. Specifically, a new particle filter method incorporating Acoustic-Spatial Pseudo-Likelihood (ASPLE) has been proposed to track objects using both acoustic and spatial information from the ego vehicle. Also, the Acoustic Recognition based Invisible-target Localization (ARIL) dataset, which is the firstly providing the location of the NLoS vehicle as ground truth using Bird’s Eye View camera, is proposed. The proposed method is validated using two datasets: the ARIL dataset and the Occluded Vehicle Acoustic Detection Dataset (OVAD) dataset. The proposed method exhibited remarkable performance in localizing NLoS targets in both datasets, predicting the location of the vehicle in the NLoS region. Lastly, the analysis of how the reflection of sound affects to the proposed method, highlighting variations based on the spatial situations, and demonstrate the empirical convergence of the method is described. Our code and dataset is available athttps://github.com/mingujeon/NLoSVehicleLocalization.

In order to enhance driving convenience and safety, automotive Augmented Reality displays, e.g., head-up displays, have garnered attention and are gradually being deployed. However, when vehicles encounter uneven roads, vertical vibrations lead to mismatches between external physical objects and augmented reality overlay images, adversely affecting the AR display’s visibility. Resolving the problem is quite challenging because the optical system operates on a nanometer scale and is highly sensitive due to its multifunctional nature involving reflection and refraction through an intermediate medium. This paper aims to address the newly emerging problem of vertical mismatches in automotive AR displays. To tackle this issue, we begin by defining the problem and then examine the effectiveness of traditional control methods, on-policy and off-policy reinforcement learning as potential solutions. Finally, we validate our approach through experiments, demonstrating a significant reduction in vertical mismatches and an improvement in the overall visibility of automotive AR displays. Our findings provide valuable insights for enhancing driving convenience and safety in real-world conditions.

The dynamics of all real quadrotors inevitably differ even if they are the same product. In particular, the dynamics can change significantly during the flight due to additional device attachments or overheating motors. In this study, we focus on training a low-level controller, which operates in response to dynamics-changes without prior knowledge or fine-tuning of the parameters, using reinforcement learning. We randomize the dynamics of quadrotors in the simulator and train the policy based on dynamics information extracted from the state-action history through recurrent neural networks (RNNs). In addition, our experiment demonstrates the difficulties in applying existing actor-critic structures that extract dynamics information using end-to-end RNNs for unstable quadrotors; hence, we propose a novel structure with better performance. Finally, the excellent performance of the proposed controller is verified by testing experiments that stabilize quadrotors with different dynamics. The experiment videos and the code can be found at https://github.com/jackyoung96/RNN-Quadrotor-controller.

Robotic agents trained using reinforcement learning have the problem of taking unreliable actions in an out-of-distribution (OOD) state. Agents can easily become OOD in real-world environments because it is almost impossible for them to visit and learn the entire state space during training. Unfortunately, unreliable actions do not ensure that agents perform their original tasks successfully. Therefore, agents should be able to recognize whether they are in OOD states and learn how to return to the learned state distribution rather than continue to take unreliable actions. In this study, we propose a novel method for retraining agents to recover from OOD situations in a self-supervised manner when they fall into OOD states. Our in-depth experimental results demonstrate that our method substantially improves the agent’s ability to recover from OOD situations in terms of sample efficiency and restoration of the performance for the original tasks. Moreover, we show that our method can retrain the agent to recover from OOD situations even when in-distribution states are difficult to visit through exploration. Code and supplementary materials are available at https://github.com/SNUChanKim/SeRO.

Accurately estimating the value of an equipment is a significant challenge in the industrial environment. Conventional methods mainly considered discounting the value over time, but they are limited in that they do not consider the status of individual equipment. Recent developments in Industrial Internet of Things (IIoT) and AI technologies have opened up the possibility of real-time remote monitoring on the status of machinery, thus providing an opportunity to more accurately estimate the value of machine equipments. In this study, we designed a sensor that can acquire the vibration and magnetic field data of an equipment, with which we proposed a 1-D convolutional neural network that can classify the status of machinery based on the data obtained by the designed sensor. In addition, based on the results of the classification model, the cumulative fatigue of equipment was predicted using Pålmgren–Miner’s linear damage rule, with which we proposed a model for estimating the value of the movable property based on the cumulative fatigue.

3D LiDAR is widely used in autonomous systems such as self-driving cars and autonomous robots because it provides accurate 3D point clouds of the surrounding environment under harsh conditions. However, a high-resolution LiDAR is expensive and bulky. Although a low-resolution LiDAR is compact and affordable, the obtained point clouds are so sparse that it is difficult to extract features that are meaningful for highlevel tasks. To solve this problem, several upsampling-based approaches have been proposed by estimating high-resolution point clouds from low-resolution point clouds. However, most works have focused on upsampling object-level or synthetic point clouds obtained from CAD models. Additionally, these approaches have a high computational cost, which makes them unusable in real-time applications such as autonomous driving vehicles. In this paper, we propose a real-time upsampling method with LiDAR for outdoor environments. The proposed method builds on conditional neural processes that are capable of uncertainty quantification. With this probabilistic property, we can remove the upsampled points that have high uncertainty, thus achieving high accuracy. Additionally, the proposed method can be trained in a simulated environment, and then directly applied to the real world. The experimental results on a simulated environment and a real-world dataset show that the proposed method is significantly faster than the state-of-the-art methods while achieving comparable performance.

Recent technical innovation has driven the evolution of autonomous vehicles. To improve safety as well as on-road vehicular experience, vehicles should be connected with each other or to vehicular networks. Some specification groups, e.g., IEEE and 3GPP, have studied and released vehicular communication requirements and architecture. IEEE’s Wireless Access in Vehicular Environment focuses on dedicated and short-range communication, while 3GPP’s New Radio V2X supports not only sidelink but also uplink communication. The 3GPP Release 16, which supports 5G New Radio, offers evolved functionalities such as network slice, Network Function Virtualization, and Software-Defined Networking. In this paper, we define and design a vehicular network architecture compliant with 5G core networks to enable and support autonomous driving. As a validation example, a high-definition map needs to contain the context of trajectory for localization and planning of autonomous driving vehicles. We also propose new methods by which autonomous vehicles can push and pull map content efficiently, without causing bottlenecks on the network core. We evaluate the performance of the proposed method via network simulations and our autonomous driving vehicle on the road. Experimental results indicate that the proposed method improves the performance of vehicular content delivery in real-world road environments.

Curb detection is an essential function of autonomous vehicles in urban areas. However, curbs are difficult to detect in complex urban environments in which many dynamic objects exist. Additionally, curbs appear in a variety of shapes and sizes. Previous studies have been based on the traditional pipeline, which consists of the extraction and aggregation of hand-crafted features that are then fed to classifiers. However, this sequential process is inefficient and designing the hand-crafted features is a complex process. Recently, this kind of process has been replaced by Deep Neural Networks (DNN), in which classifiers and features are learned from large-scale data. Very few works have exploited DNN for the curb detection problem. Most works use multi-modal sensor-based methods that combine images and accumulated 3D point clouds from LIDAR. However, these approaches require synchronization and calibration between sensors. In addition, they do not quantify the uncertainty of their predictions for autonomous system safety. In this paper, we present a two-stage DNN-based curb detection method that includes uncertainty quantification. An autoencoder-based network predicts the curbs, and then conditional neural processes rectify the predictions with uncertainty estimations. The experimental results show that our approach achieves high accuracy and recall in complex areas. We also constructed a large-scale dataset to create benchmarks consisting of approximately 5,224 scans with bird’s-eye view labels collected from urban areas. To the best of our knowledge, there are no public datasets for DNN-based curb detectors. The benchmarks and datasets are publicly available at https://github.com/YounghwaJung/curb_detection_DNN.

Autonomous vehicles must be able to understand the surrounding traffic flows and predict the future traffic conditions for planning a safe maneuver. During prediction, the action of autonomous vehicles should be considered, as it influences the interaction between vehicles sharing the same traffic scene and thus influences the future traffic flow. From this perspective, not only should the prediction be considered for planning, but also the action of autonomous vehicles generated by planning should be considered for traffic scene prediction. Therefore, prediction and planning must work interactively at every time step, considering results of each other. In this paper, we present a novel learning-based framework that simultaneously forecasts a nearby traffic scene and plans a maneuver of autonomous vehicle at every time step. Through experiments, we demonstrated that the proposed method exhibits better planning performance than baselines in complex traffic conditions involving various surrounding vehicles.

Autonomous driving with imitation learning is vulnerable to the quality of an expert dataset. Typical driving involves situations or online data that are biased toward specific scenarios such as lane following or stop. This property causes an imbalance in the driving dataset, and it is highly likely to deteriorate the performance of autonomous driving with imitation learning. In this paper, we propose a dataset self-balancing system with biased online data and an imbalanced dataset. By estimating the probability distribution of a dataset, we compute the probability and novelty of online data and then filter only qualified novel data. In addition, using the computed probability distribution, we determine the data that are non-informative in the current dataset and then exchange them with novel online data. At last, by retraining the driving neural network with high-entropy data batches, our method achieves incremental driving intelligence. We demonstrated the effectiveness of our method through open-loop evaluation and ablation studies in a CARLA simulator; the results show that our proposed system effectively balances the dataset with 100 scenarios and decreases test loss over time.

Curb detection and tracking is an essential component of autonomous vehicle operation in urban environments. Detecting curbs is a particularly challenging task in urban environments that contain countless dynamic objects. Previous studies have approached curb detection using different types of sensor such as cameras, radar, and LIDAR. Among these, LIDAR sensors have superior advantages in regard to detecting curbs because of their robustness in different weather conditions and they can provide accurate distance measurements. Previous methods based on LIDAR have exploited high-resolution 3D point clouds using high-cost LIDARs. However, the processing of large volumes of information is inefficient for autonomous driving technologies because of real time constraints. This paper presents a novel real-time curb detection and tracking algorithm that makes use of a low-resolution LIDAR. The proposed method consists of three steps. First is the extraction of curb candidates based on Principal Component Analysis (PCA) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Second is the selection of the optimal candidate using an optimization framework. Lastly is the tracking of the detected curbs. In the tracking module, we use a combination of spatial consistency and validation gate to track the curb in the occlusion region with dynamic objects. Experiments on a public dataset show that the proposed method achieved 91.54% and 89.76% F1score on the straight and curved road while running at about 18 ms per frame, thereby outperforming the state-of-the-art by a large margin. In addition, we integrated the proposed method with the localization module of our autonomous driving platform. The localization module integrated with the proposed method reduces the positional and lateral root mean square (RMS) error of the vehicle localization by 3.58% and 6.68% respectively. In addition, we compare this method with a deep neural network based method from the perspective of a safety-critical system such as self-driving cars.

Traffic safety and efficiency are major objectives for Intelligent Transportation Systems (ITS) [item 1) in the Appendix], [item 2) in the Appendix]. A lot of research has been done on autonomous vehicles and Advanced Driver Assistance Systems, which exploit a variety of sensors in order to support the driver and/or autonomous driving (e.g., [item 3) in the Appendix]–[item 7) in the Appendix]).

In this paper, we present a driverless taxi system for autonomous campus mobility services. College campuses have unique mobility requirements in terms of layout, population, and demand and patterns. It is typically recommended to minimize the presence of private automobiles on campuses due to teaching and research disturbances, visual degradation from parking provision, environmental pollution, and negative health effects. As an alternative to private automobiles, shared mobility systems have been considered for both campus and urban transportation. Conventional shuttle systems suffer from the first and last mile problem. A bicycle and pedestrian friendly policy is not a generalizable solution for all geographic locations and campus layouts. We suggest a driverless taxi service as an alternative point-to-point shared mobility system for campuses. We have demonstrated the feasibility of this service on a 4.5-km campus road at Seoul National University. The service has covered over 10 000 km autonomously since the first public demonstration was made in November 2015.

In LIght Detection And Ranging (LIDAR)-based object detection, accurate object segmentation is of great importance, since segmentation is an essential preprocessing step for other perception tasks, such as classification and tracking. For segmenting objects, most of the previous methods have tried to eliminate the ground first, which typically incurs considerable overhead in computation and inaccuracy in object detection with point clouds gathered by using 3-D LIDARs. However, in many real-time applications, such as automated driving, segmentation should be performed within a specified time, because even a small delay in computation could result in vehicle collisions. In this paper, we propose a real-time and accurate object segmentation algorithm for 3-D point clouds, which does not carry out ground extraction as a first step. In the proposed algorithm, we generate candidate points of objects and find their borders based on the integrated structure of a 2-D grid and an undirected graph, which enables fast processing and yields an accurate segmentation result independent of ground extraction error. In order to enhance segmentation accuracy, we employ Gaussian process, which reduces over-segmentation that separates an object into multiple portions. We apply two types of Gaussian process models to alternately provide cues for merging adjacent over-segmented objects. Experimental results demonstrate that this paper achieves a real-time processing speed and higher segmentation accuracy than previous works in most evaluation metrics. With the application to tracking, we show that the enhanced segmentation accuracy increases the tracking accuracy by 11.4% even in the worst case.

As the around-view monitoring (AVM) system becomes one of the essential components for advanced driver assistance systems (ADAS), many applications using AVM such as parking guidance system are actively being developed. As a key step for such applications, detecting road markings robustly is a very important issue to be solved. However, compared to the lane marking detection methods, detection of non-lane markings, such as text marks painted on the road, has been less studied so far. While some of methods for detecting non-lane markings exist, many of them are restricted to roadways only, or work poorly on AVM images. In this paper, we propose an algorithm which can robustly detect non-lane road markings on AVM images. We first propose a difference-of-Gaussian based method for extracting a connected component set, followed by a novel grouping method for grouping connected components based on convexity condition. For a classification task, we exploit the Random Forest classifier. We demonstrate the robustness and detection accuracy of our methods through various experiments by using the dataset collected from various environments.

Parking slot detection algorithms using visual sensors have been required for various automated parking assistant systems. In most previous studies, popular feature detectors, such as the Harris corner or the Hough line detector, have been employed for detecting parking slots. However, these algorithms were originally designed to find distinct features and are inadequate for the short, curvy, faint and distorted parking-lines of long-range surround-view images, especially in around-view monitoring systems. In this paper, we propose a robust parking slot detection algorithm based on the line-segment-level clustering method. The proposed algorithm consists of line-segment detection with the proposed Directional-DBSCAN line-level feature-clustering algorithm and slot detection with slot pattern recognition. In comparison to other feature detectors, we show that the Directional-DBSCAN algorithm robustly extracts lines even when they are short and faint. Moreover, we verify that the parking-slot detection algorithm with pattern recognition can be applicable to diverse slot types and environments with experiments on abundant dataset.

As autonomous vehicles operating on the urban roads, being conscious of the road context is a crucial prerequisite to safely negotiate with the other vehicles. This paper proposes a probabilistic approach to infer the road context from the vehicle behaviors. Specifically, the consistencies of the randomly-observed vehicle states are extracted first, thereafter the road context is inferred in a probabilistic manner by coupling these consistencies. The feasibility of the proposed road context inference approach has been validated by the case study of an urban road that includes roundabout and T-junction. The experiments demonstrate that the inferred road context can be successfully applied for the autonomous vehicles in various aspects.

Accurate lateral localization of an ego-vehicle is one of the core technologies for autonomous driving. Conventional approaches have utilized GPS data, pre-built map information, and lane detection results to estimate the lateral location of an ego-vehicle. However, these approaches demonstrate several performance limitations due to inaccurate data from GPS, high costs for building and maintaining maps, and insufficient visual cues for handling various tasks in diverse driving environments. In this paper, we propose an accurate ego-lane recognition framework that utilizes multiple evidence from visual processing upon the theory of the Bayesian Network to overcome these limitation. We show that more accurate and reliable lateral localization results can be achieved by combining several visual cues, which increases confidence and reliability of the results. We also show that our approach can be applicable to various driving environments without maps because the framework analyzes multiple context information of driving environments simultaneously. We verify the robustness of our algorithm in various driving scenarios such as highways and wide/narrow urban roadways.

As autonomous vehicles begin venturing on the urban road, rational decision making is essential for driving safety and efficiency. This paper presents a situation-aware decision making algorithm for autonomous driving on urban road. Specifically, an urban road situation model is proposed first for proper environment representation, thereafter the situation-aware decision making problem is modeled as a Partially Observable Markov Decision Process (POMDP) and solved in an online manner. The proposed algorithm has been extensively evaluated, which is general enough for autonomous driving in various urban road scenarios, including leader following, collision avoidance and traffic negotiation at both T-junction and roundabout.

In this paper, we present a multivehicle cooperative driving system architecture using cooperative perception along with experimental validation. For this goal, we first propose a multimodal cooperative perception system that provides see-through, lifted-seat, satellite and all-around views to drivers. Using the extended range information from the system, we then realize cooperative driving by a see-through forward collision warning, overtaking/lane-changing assistance, and automated hidden obstacle avoidance. We demonstrate the capabilities and features of our system through real-world experiments using four vehicles on the road.

This paper proposes a spatio-temporal motion feature detection and tracking method using range sensors working on a moving platform. The proposed spatio-temporal motion features are similar to optical flow but are extended on a moving platform with fusion of odometry and show much better classification accuracy with consideration of different uncertainties. In the proposal, the ego motion is compensated by odometry sensors and the laser scan points are accumulated and represented as space-time point clouds, from which the velocities and moving directions can be extracted. Based on these spatio-temporal features, a supervised learning technique is applied to classify the points as static or moving and Kalman filters are implemented to track the moving objects. A real experiment is performed during day and night on an autonomous vehicle platform and shows promising results in a crowded and dynamic environment.

In this paper, we propose a general autonomous parking system to enable drivers to get out of their cars wherever and whenever they want. We demonstrate our prototype system along with research issues, challenges, and applications.

In this paper, we attempt to develop a reusable framework of cooperative perception for vehicle control on the road that can extend perception range beyond line-of-sight and beyond field-of-view. For this goal, the following problems are addressed: map merging, vehicle identification, sensor multi-modality, impact of communications, and impact on path planning. We provide experimental results using a self-driving vehicle and manned vehicles equipped with the cooperative perception systems that we propose and implement.

The gradient adaptive lattice (GAL) algorithm is very attractive choice for active noise control of multiple sinusoidal interferences. In the GAL algorithm, a selection of step-size parameters trades off between convergence speed and steady-state performance. In this paper, we develop a variable step-size scheme for the filtered-x GAL (VSS-FxGAL) algorithm. This proposed algorithm achieves a good compromise between fast convergence speed and low steady-state mean-square error (MSE). In addition, comparing to the filtered-x affine projection (FxAP) algorithm, the proposed algorithm performs better when the filter input consists of multiple sinusoids.

The dynamics of multi-agent in nature have been largely studied for a long time to investigate how the aggregation of agents can move smoothly in complex environments without collision. The main insights can be summarized such that the aggregated dynamics of animals and particles can be explained by an individual's simple rules. In a similar vein, we conjecture that such simple rules for vehicle maneuvering can accommodate the fluid flow of traffic and reduce car accidents in highway and urban areas. In this paper, we first show the Reynolds' three rules are applicable to autonomous driving on a single lane. Moreover, we provide additional requirements and algorithms for multiple lanes. Based on these results, we show that the proposed nature-inspired driving maneuver can increase traffic flow by 1) mitigating shockwave at bottlenecks and 2) extending the perception range for better path planning, which requires the support of the vehicle autonomy and wireless communication, respectively. Finally, we prove the feasibility of our work with experiments using multiple UAVs.

Beyond the individual independent unmanned autonomous vehicle (UAV), cooperative control of multiple UAVs has started to receive significant attention from industry, academia and the military. For the sake of UAV cooperation, proper wireless communication is imperative, but incurs several problems. Among them, spatially secure group communication (SSGC), which must maximize spatial UAV group size while minimizing the communication boundary of the group, is a unique problem for multiple UAV control from a security perspective. In particular, the SSGC problem must be considered for military applications such as multiple unmanned aerial or ground vehicle control. In this paper, we investigate the SSGC problem. To provide a solution, an analytical framework is first presented to model the dynamics of multiple UAVs and SSGC. The theoretical analysis and simulation results regarding how communication affects group dynamics and spatial communication security are also discussed. Our contribution is to suggest a new way to view multiple UAV control with spatially secure communication, and to provide a distributed method to address the problem cooperatively.

Control networks are widely deployed on mass produced mobile systems such as automotive systems, airplanes, and mobile robots. Previous works on control networks have focused on guaranteeing controllability, predictability, and dependability in the time domain because control networks manipulate actuators directly, and hence are highly related to safety. However, in contrast to data networks, control networks severely affect the manufacturing cost, fuel efficiency, and space effectiveness of mobile systems due to additional network devices, control unit (CU) arrangements, and task assignments. These problems have become more important as CUs have been extensively deployed in real systems in the pursuit of intelligence and energy efficiency. Therefore, system design using control networks must be considered with respect to the time and space domains simultaneously. We propose a design method to minimize space resources due to control networks in the target system while satisfying time constraints. We formulate this problem as a matching problem and provide an effective solution. Through extensive simulations, we demonstrate that our methodology is very effective and scalable, and saves significant time, space, and cost in control networks.

Epidemic malicious codes including Internet worms and botnets have continuously evolved to be more intelligent and complicated. In particular, the recent distributed denial-of-service (DDoS) attack that occurred in United States and South Korea in July, 2009 gives an opportunity to reconsider the epidemic malicious code. Since automatic patching systems and intelligent intrusion detection and prevention systems mitigate rapid infection, fast infections such as Slammer-like worms cannot successfully spread. As of the 2009 July DDoS attack, malicious codes prefer hiding their malicious activities and trying to infect others silently until D-day. Since slow infection is difficult to detect by the current IDS or IPS, this infection strategy is likely to become prevalent. In a slow infection, the incubation period is a key factor in determining the extent to which an epidemic malicious code spreads. This study provides an analysis framework to understand the impact of incubation period in the spread of epidemic malicious code. Intuitively, a longer latent period increases the number of infected hosts, but the detection probability also increases. This trade-off suggests an optimal incubation period determination problem to maximize the number of infected hosts. Solving this problem is essential to predicting the explicit or implicit intention of attackers and to counteract against the attack in a strategic manner. Through analysis and simulations, we provide data and insight regarding epidemic malicious code that exploits incubation period.

In order to support the mobile and multimedia services efficiently on the next generation Internet and intranet, we need a more flexible and extensible network manager. We may consider the employment of TINA (Telecommunications Information Networking Architecture), which is the software architecture to extend the capability of the IN (intelligent network), to cope with the changes of service requirement efficiently. In order to employ the TINA-based network manager for the Internet or intranet, however we need a TINA-SNMP inter-working function because the current management standard of the Internet and intranet is the SNMP. We propose an object abstract translator, which works in the TINA-SNMP gateway for the TINA-based network manager. The proposed object abstract translator is designed to increase the flexibility of the TINA-based network manager, and to assure the independency of the TINA-based network manager from the various SNMP agent implementation technologies.

In order to guarantee the user-requested quality-of-service (QoS) and keep the network utilization at maximum, it is required to manage the network performance continuously after the network installation. The performance management function should provide useful information for the network expansion and the capacity reallocation in the future. Currently, TINA provides the specification of the management functions of configuration management, connection management, and fault management; but the management functions of performance management and security management are not well-defined yet. In this paper, we propose a TINA-based performance management architecture for the efficient performance management of the heterogeneous networks or NEs with TMN and SNMP management functions. The proposed architecture is based on the distributed processing concept of TMN performance management. The proposed architecture have been designed and implemented in multiprocess and multithread structure.