Detecting occlusion edges from videos is a critical yet under-explored task, with recent works focusing on single-image occlusion edge detection while ignoring dynamic patterns in videos. In videos, occlusion edges typically occur when a moving object occludes either the background or another object, resulting in two types of occlusion edges:object-background (OB) edgesthat are characterized by appearance contrast and motion, and object-object (OO) edgesthat face ambiguity. Inspired by these observations, we propose a novel Motion and Spatio-Temporal Aggregated Network (MaSTAN) and treat the two edge types differently for more effectively detecting occlusion edges in videos. Specifically, we first extract spatial semantics and motion patterns from the videos and propose a novel Temporal Feature Propagation module (TFP) for temporal cue aggregation. Next, we put forward a Dual-branch Gated-attention Decoder (DG-Decoder) to generate edge-specific features for predicting the final occlusion edge maps. Extensive experiments on the OVIS-OE benchmark, the first large benchmark dedicated to video occlusion edge detection, demonstrate that MaSTAN achieves state-of-the-art performance, significantly advancing the capability of occlusion edge detection in video. The source code and benchmark will be made publicly available.

This study focuses on trajectory planning and motion control policies in autonomous racing, which necessitates pushing the capacity boundaries of racing vehicles to achieve maximum speeds and minimal lap times. We propose an innovative planning control framework that integrates risk-conscious mutations in jump-start reinforcement learning (RCM-JSRL) and nonlinear model predictive control (NMPC). The RCM-JSRL algorithm incorporates jump-start curriculum learning and the risk-conscious genetic algorithm into reinforcement learning, leveraging prior expert knowledge and a curiosity-driven exploration mechanism to enhance training efficiency while avoiding excessively conservative policy generation in high-complexity and high-risk scenarios. NMPC generates locally optimal control commands that adhere to vehicle dynamics constraints while following the designated trajectory. Following training on track maps with varying difficulty levels, the proposed controller successfully executes a superior policy compared to the guide policy, providing evidence of its effectiveness and scalability. It is our belief that this technology can be applied in everyday driving scenarios, improving efficiency under special conditions, ensuring stability in critical situations, and broadening the scope of autonomous driving applications.

This study introduces the Metacognitive-Attribution Inspired Reinforcement Learning (MAIRL) approach, designed to address unprotected interactive left turns at intersections—one of the most challenging tasks in autonomous driving. By integrating the Metacognitive Theory and Attribution Theory from the psychology field with reinforcement learning, this study enriches the learning mechanisms of autonomous vehicles with human cognitive processes. Specifically, it applies Metacognitive Theory’s three core elements—Metacognitive Knowledge, Metacognitive Monitoring, and Metacognitive Reflection—to enhance the control framework’s capabilities in skill differentiation, real-time assessment, and adaptive learning for interactive motion planning. Furthermore, inspired by Attribution Theory, it decomposes the reward system in RL algorithms into three components: 1) skill improvement, 2) existing ability, and 3) environmental stochasticity. This framework emulates human learning and behavior adjustment, incorporating a deeper cognitive emulation into reinforcement algorithms to foster a unified cognitive structure and control strategy. Contrastive tests conducted in various intersection scenarios with differing traffic densities demonstrated the superior performance of the proposed controller, which outperformed baseline algorithms in success rates and had lower collision and timeout incidents. This interdisciplinary approach not only enhances the understanding and applicability of RL algorithms but also represents a meaningful step towards modeling advanced human cognitive processes in the field of autonomous driving.

This paper introduces DysArinVox, a new pathological speech corpus in Chinese.It included 173 participants from 27 healthy individuals and 146 voice disorders, whose various types and severities of vocal impairments as diagnosed by speech pathology experts via auditory perceptual evaluations and laryngoscopic imagery.DysArinVox is designed to provide a high-quality Chinese resource for AI-driven diagnostics and prognostics.To ensure the efficiency of corpus collection, we meticulously crafted recording scripts represent Mandarin phonetically, ensuring comprehensive syllable representation with minimal lexical complexity.Additionally, incorporating laryngoscopic images of patients into the dataset offers extra visual information, facilitating the development of advanced diagnostic frameworks.To our knowledge, this database represents the most comprehensive corpus of Chinese pathological speech to date.

The emergency collision avoidance capabilities of autonomous vehicles (AVs) are crucial for enhancing their active safety performance, particularly in extreme scenarios where standard methods fall short. This study introduces an Extreme Maneuver Controller (EMC) for AVs, utilizing reachability-guided reinforcement learning (RL) to address these challenging situations. By applying pseudospectral methods, we solve the minimum backward reachable tube (Min-BRT) to identify regions where conventional avoidance maneuvers are infeasible, establishing a theoretical basis for triggering extreme maneuvers. A novel controller, employing reachability-guided RL, enables vehicles to execute extreme maneuvers to escape these critical regions. During training, the value function derived from the Min-BRT solution informs the initialization of the Critic networks, enhancing training efficiency. Real-world scenario-based experimental results with actual vehicles validate that the proposed policy, effectively executes beyond-the-limit maneuvers, mitigating collision risks under emergency condition. Furthermore, these extreme maneuvers are executed with minimal deviation from the original driving objectives, ensuring a smooth and stable transition upon completion of extreme maneuvers.

Vocoder-based speech synthesis has become a promising technique to accommodate the demands of high-quality speech analysis, manipulation, and synthesis. However, most existing works focus on how to synthesize normal human voice with high signal-to-noise ratio, neglecting individuals' pathological voice disorder in speech interaction. In this work, we propose a non-linear voice repair vocoder for pathological vowels and sentences, which takes the pathological speech as input and generates high-quality repaired speech. Our approach is specifically designed to enhance the speech quality and intelligibility for individuals with voice disorders. We employ amplitude modulated-frequency modulated (AM-FM) and Teager energy operation techniques to enhance the quality of pitch and spectral envelope. To tackle the instability and fracture problem of pitch, we present spectral tracking algorithm, which not only avoids dramatic change in the edge of voice, but also reduces the errors of half-pitch. Furthermore, we design a spectral reconstruction algorithm, which can effectively rebuild the spectral structure by energy operation to accomplish spectral envelope repair. The proposed PVR-Vocoder shows exceptional performance in pathological voice intelligibility enhancement according to various quality measures including objective indicators, subjective evaluation, and spectrum observations.

This study focuses on the motion planning and risk evaluation of unprotected left turns at occluded intersections for autonomous vehicles. In this paper, we present an interactive motion planning controller that combines Cross-Observability Optimistic-Pessimistic Safe Reinforcement Learning (COOP-SRL) and Nonlinear Model Predictive Control (NMPC), with consideration of the uncertain potential risk of occluded zone, the trade-off between safety and efficiency, and the dynamic interaction between vehicles. The proposed COOP-SRL algorithm integrates fully and partially observable policies through cross-observability soft imitation learning to leverage the expert guidance and improve learning efficiency. Moreover, the optimistic exploration policy and pessimism safe constraint are adopted to provide an adaptive safe strategy without hindering the exploration during learning process. Finally, the evaluations of the proposed controller were conducted in occluded intersection scenarios with various traffic density level, which indicate that the proposed method outperforms both the optimization-based and learning-based baselines in qualitative and quantitative indexes.

This paper introduces an adaptive beyond-the-limit controller, aimed at striking a balance between high-performance maneuvers, such as transient drift, and ensuring safety in unpredictable environments. Our work is motivated by the necessity for autonomous beyond-the-limit control adaptable to real-world uncertainties, where reinforcement learning (RL) faces simulation-to-reality gap challenges in safety and performance. Our approach introduces a hybrid control mechanism that integrates data-driven performance optimization with a robust safety-centric control policy. By leveraging expert demonstrations and employing Jump-Start RL framework in Frenet coordinates, we greatly improve the learning efficiency of performance optimization. Further, an integrated safety control policy is designed to mitigate hazards through predictive trajectory planning, thus significantly reducing the risk of accidents in unforeseen situations. Meanwhile, the hybrid control mechanism employs adaptive weighting between performance and safety considerations, allowing for fusion control based on real-time environmental assessments. Through simulation experiments and initial real-vehicle testing, we validate the effectiveness of our adaptive hybrid controller. The findings confirm that our controller consistently ensures integrated safety in unpredictable environments, with an acceptable impact on performance.

The specific restrictions are breached in the case of vehicle platoon faults and result in unacceptable system performance degradation. This paper proposed a novel prescribed time performance recovery fault tolerant control method to ensure nominal platoon performance under multiple faults, including actuator faults with deferred backup actuator switching and leader-follower link faults in consideration. A novel barrier function based prescribed time sliding mode controller is devised to assure platoon consensus errors and convergence time within prescribed constraints under normal conditions at first. Under multiple faults conditions, to tackle with leader-follower link faults problem, a novel distributed recursive estimator is proposed to estimate the leader’s states and recover the previous leader-follower platooning control protocol in a prescribed time. Besides, in the presence of actuator failures, the nominal constraints violated problem under faults is put into consideration. Owing to the unavoidable deferred actuator replacement time, the previous platoon consensus error constraints are violated and cause platoon performance degradation. Under such circumstances, by exploiting one novel barrier function-based sliding mode controller with an error shifting function, the unfavorable exceeding platoon consensus errors can be recovered into the nominal constraints domains within a prescribed time. Numerical simulations and hardware-in-loop (HIL) experiments are demonstrated to validate the effectiveness and superiority of our performance recovery fault tolerant control algorithms.

Reliable path tracking control (PTC) method is essential for autonomous driving. However, existing PTC methods count on prior vehicle parameters to achieve good performance. This paper presents an optimal PTC method without requiring any prior vehicle parameters based on online modeling with strict parameter convergence ability. First, we build a virtual optimal control problem using adaptive dynamic programming (ADP) scheme to guide the data collection and solve two characteristic matrices containing parameter information. Then, the model construction method is derived using the solved matrices and the optimal PTC method is constructed using the constructed model. Finally, a fault-tolerant control scheme is further designed using the constructed model and the online modeling ability of the proposed method. The effectiveness of the proposed method is validated through co-simulation between Matlab/Simulink and high-fidelity vehicle dynamic simulation software CarSim® under both fault-free and fault-tolerant situations.

Learning-based approaches to graph matching have been developed and explored for more than a decade, have grown rapidly in scope and popularity in recent years. However, previous learning-based algorithms, with or without deep learning strategy, mainly focus on the learning of node and/or edge affinities generation, and pay less attention on the learning of the combinatorial solver. In this paper we propose a fully trainable framework for graph matching, in which learning of affinities and solving for combinatorial optimization are not explicitly separated as in many previous arts. We firstly convert the problem of building node correspondences between two input graphs to the problem of selecting reliable nodes from a constructed assignment graph. Subsequently, the graph network block module is adopted to perform computation on the graph to form structured representations for each node. It finally predicts a label for each node that is used for node classification, and the training is performed under the supervision of both permutation differences and the one-to-one matching constraints. The proposed method is evaluated on four public benchmarks in comparison with several state-of-the-art algorithms, and the experimental results illustrate its excellent performance.

Visual Augmented Reality (AR) typically overlays virtual computer graphics or other virtual contents on the real world videos, attracting much interest from both academic and industrial communities. Although AR techniques on planes are well studied, cylindrical objects are seldom used for augmented reality. In this paper, we propose a new method for 3D reconstruction and 6D pose computation for augmented reality on a cylindrical object. The 6D pose is the relative pose between the camera and the cylindrical object, which is very convenient to make augmented reality. First, we capture some images with a cylindrical object and then reconstruct its 3D model with textures offline by using projective invariance and image contours. Second, according to the 3D model, we track the 6D relative pose between the camera and the cylindrical object online, where we propose a linear P3P RANSAC to remove outliers. Finally, the virtual images are exactly aligned with the cylindrical object in the real world. Experimental results show that the proposed method outperforms the state of the arts in terms of 3D mapping and 6D pose computation on cylindrical objects.

This paper addresses the problem of deformable surface tracking from monocular images. Specifically, we propose a graph-based approach that effectively explores the structure information of the surface to enhance tracking performance. Our approach solves simultaneously for feature correspondence, outlier rejection and shape reconstruction by optimizing a single objective function, which is defined by means of pairwise projection errors between graph structures instead of unary projection errors between matched points. Furthermore, an efficient matching algorithm is developed based on soft matching relaxation. For evaluation, our approach is extensively compared to state-of-the-art algorithms on a standard dataset of occluded surfaces, as well as a newly compiled dataset of different surfaces with rich, weak or repetitive texture. Experimental results reveal that our approach achieves robust tracking results for surfaces with different types of texture, and outperforms other algorithms in both accuracy and efficiency.