With the explosive growth of online service platforms, an increasing number of people and enterprises are undertaking personal and professional tasks online. In real applications such as trip planning and online marketing, planning sessions for a sequence of activities or services will enable social users to receive the optimal services, improving their experience and reducing the cost of their activities. These online platforms are heterogeneous, including different types of services with different attributes. However, the problem of session planning over heterogeneous platforms has not been studied so far. In this paper, we propose a Motivation-Aware Session Planning (MASP) framework for session planning over heterogeneous social platforms. Specifically, we first propose a novel HeterBERT model to handle the heterogeneity of items at both type and attribute levels. Then, we propose to predict user preference using the motivations behind user activities. Finally, we propose an algorithm together with its optimisations for efficient session generation. The extensive tests prove the high effectiveness and efficiency of MASP.

Social video anomaly is an observation in video streams that does not conform to a common pattern of dataset's behaviour. Social video anomaly detection plays a critical role in applications from e-commerce to e-Iearning. Traditionally, anomaly detection techniques are applied to find anomalies in video broadcasting. However, they neglect the live social video streams which contain interactive talk, speech, or lecture with audience. In this paper, we propose a generic framework for effectively online detecting Anomalies Over social Video LI ve Streaming (AOVLIS). Specifically, we propose a novel deep neural network model called Coupling Long Short-Term Memory (CLSTM) that adaptively captures the history behaviours of the presenters and audience, and their mutual interactions to predict their behaviour at next time point over streams. Then we well integrate the CLSTM with a decoder layer, and propose a new reconstruction error-based scoring function REI A to calculate the anomaly score of each video segment for anomaly detection. After that, we propose a novel model update scheme that incrementally maintains CLSTM and decoder. Moreover, we design a novel upper bound and ADaptive Optimisation Strategy (ADOS) for improving the efficiency of our solution. Extensive experiments are conducted to prove the superiority of AOVLIS.

Group recommendation over social media streams has attracted attention due to its wide applications such as e-commerce, entertainment and online news broadcasting. However, existing stream group recommendation techniques ignore the influence of user groups, which are not effective for item propagation over social networks. To address this problem, we propose a framework for Influence-aware Group Recommendation (IGR) over high-speed social streams. Specifically, we first propose a novel GroupGCN model to capture the dynamics of user attributes and interactions which maps groups and items to their embeddings. A Temporal GroupGCN-RNN-Autoencoder (TGGCN-RA) model is designed to extend GroupGCN for sequence-based tasks, enabling the prediction of group interests over time. Then, we adopt an Independent Cascade (IC) model to predict the influence propagation of social items over user groups. Extensive experiments prove the high effectiveness and efficiency of IGR.

The key to achieving path-following and emergency obstacle avoidance in autonomous vehicles is the Steer-By-Wire (SBW) system. While enhancing control accuracy and response speed, the SBW system introduces an increased risk of electronic and electrical failures. To address this problem, the commonly adopted approach is dual steering motor redundancy, ensuring reliability and safety while considering structural and cost constraints. This paper focuses on the Dual-Motor Steer-By-Wire (DMSBW) system, specifically the master-master redundancy mode. Existing studies have paid less attention to the control after the failure of one steering motor. Therefore, this paper proposed an adaptive nonsingular fast terminal sliding mode redundant control algorithm to achieve accurate and swift steering angle tracking. This algorithm accounts for system uncertainty and parameter perturbation, enhancing system robustness while ensuring precise control accuracy. Additionally, considering cost implications in engineering applications, we propose an unknown input observer for accurate estimation of the front wheel steering angle. Finally, the effectiveness of the proposed strategy is validated through Carsim and Simulink, which demonstrate that the strategy exhibits outstanding control performance and robustness, enabling accurate and swift steering tracking at diverse working conditions.

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.

Situation-awareness (SA) has been important for natural disaster management and smart decision making. Traditionally, security officers recognize disaster situations through emergency reporting with phone calls. However, due to the busy phone lines or power outages caused by disasters, traditional SA has been limited in terms of time and mitigation response, which may cause high loss in life and properties in disaster areas. Social media-based SA has been studied recently. However, existing systems are designed for events without nonconsecutive migrations over location and time. In this demo, we design HSAP, the first human-in-the-loop social media-based SA platform for effective and efficient management of disasters with complex event migrations. HSAP is designed with a number of novel techniques, including complex social event detection, summarization, and human-in-the-loop result filtering. We demonstrate the usage of HSAP via Nepal earthquake 2015.

Precise path tracking and agilely avoiding obstacles are essential for the stability and safety of autonomous driving. In this paper, we introduce a uniform safe path tracking control strategy that combines obstacle avoidance with path tracking via a barrier function. Unlike the conventional hierarchical collision avoidance methods, our approach employs an integral heuristic barrier function that addresses obstacle avoidance planning and reference trajectory tracking problems simultaneously. Via this, the complex safe trajectory following problem is simplified into a tractable yaw angle tracking problem. We then present a novel finite-time adaptive barrier function-based sliding mode controller that handles input saturation and enhances robustness. This ensures precise and robust yaw angle tracking within specified performance constraints. Moreover, the proposed approach achieves accelerated finite-time convergence compared to the exponential convergence rate. Finally, the Carsim-Simulink co-simulations and real-vehicle experiments validate the effectiveness and superiority of our method in addressing the path-tracking challenge, while upholding driving safety.

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.

Human parsing emphasizes on human-oriented semantic segmentation for understanding the fine-grained body parts. In this paper, we make the first attempt to utilize saliency guided information to address the single human parsing task. Existing methods that explore the fusion of information from other related tasks mainly consider refining pose structure or edge features in a human parsing model. However, the intrinsic incompatibility of multiple tasks due to discrepant optimization targets brings the absence of global semantic information. To overcome this problem, considering the tight inter-task correlation against incompatibility, we propose an Attention-guided Progressive Partition Network (APPNet) to exploit the benefits of combining saliency detection and human parsing into a unified network. In APPNet, a Global Attention Module (GAM) is designed to capture weighted features in the spatial dimension from a global view and fuse multi-level complementary attention weights. Simultaneously, to capture appropriate receptive-field multi-scale features, we present a simple yet effective Feature Extraction Module (FEM) to adapt to this attention model. A series of experiments on four datasets demonstrate that APPNet outperforms several remarkable methods including the best model guided by other domain information and several attention-based approaches for human parsing. Code is available at http://github.com/huangxi6/APPNet.

Recently, a new type of video understanding task called Movie-Fill-in-the-Blank (MovieFIB) has attracted many research attentions. Given a pair of movie clip and description with one blank word as input, MovieFIB aims to automatically predict the blank word. Because of the advantage in processing sequence data, Long-Short Term Memory (LSTM) has been used as a key component in existing MovieFIB methods to generate representations of videos and descriptions. However, most of these methods fail to emphasize the salient parts of videos. To address this problem, in this paper we propose to use a novel LSTM network called LSTM with Linguistic gate (LSTMwL), which exploits adaptive temporal attention for MovieFIB. Specifically, we first use LSTM to produce video features, which are then used to update the text representation. Finally, we put the updated text into two opposite directional LSTMwL layers to infer the blank word. Experimental results demonstrate that our approach outperforms state-of-the-art models for MovieFIB.