As complex autonomous systems become increasingly ubiquitous, their deployment and integration into our daily lives will become a significant endeavor. Human–machine trust relationship is now acknowledged as one of the primary aspects that characterize a successful integration. In the context of human–machine interaction (HMI), proper use of machines and autonomous systems depends both on the human and machine counterparts. On one hand, it depends on how well the human relies on the machine regarding the situation or task at hand based on willingness and experience. On the other hand, it depends on how well the machine carries out the task and how well it conveys important information on how the job is done. Furthermore, proper calibration of trust for effective HMI requires the factors affecting trust to be properly accounted for and their relative importance to be rightly quantified. In this article, the functional understanding of human–machine trust is viewed from two perspectives—human-centric and machine- centric. The human aspect of the discussion outlines factors, scales, and approaches, which are available to measure and calibrate human trust. The discussion on the machine aspect spans trustworthy artificial intelligence, built-in machine assurances, and ethical frameworks of trustworthy machines.

Drought is a stochastic natural feature that arises due to intense and persistent shortage of precipitation. Its impact is mostly manifested as agricultural and hydrological droughts following an initial meteorological phenomenon. Drought prediction is essential because it can aid in the preparedness and impact-related management of its effects. This study considers the drought forecasting problem by developing a hybrid predictive model using a denoised empirical mode decomposition (EMD) and a deep belief network (DBN). The proposed method first decomposes the data into several intrinsic mode functions (IMFs) using EMD, and a reconstruction of the original data is obtained by considering only relevant IMFs. Detrended fluctuation analysis (DFA) was applied to each IMF to determine the threshold for robust denoising performance. Based on their scaling exponents, irrelevant intrinsic mode functions are identified and suppressed. The proposed method was applied to predict different time scale drought indices across the Colorado River basin using a standardized streamflow index (SSI) as the drought index. The results obtained using the proposed method was compared with standard methods such as multilayer perceptron (MLP) and support vector regression (SVR). The proposed hybrid model showed improvement in prediction accuracy, especially for multi-step ahead predictions.

As unmanned aerial vehicles (UAVs) become increasingly integrated across various domains, both military and civilian, safeguarding the security of their navigation systems becomes paramount. In the contemporary age, the prominence of cybersecurity for UAVs has grown due to a rising number of cyberattacks on these systems. Notably, over the past decade, several significant cybersecurity breaches have impacted UAVs due to inadequate vulnerability assessments and security measures. Deep learning (DL)-based algorithms show immense potential for enabling autonomous UAV navigation. However, these algorithms are susceptible to malicious attacks, such as DL-based Trojan attacks, which can compromise the integrity and reliability of UAV navigation systems. This paper addresses potential vulnerabilities in DL-based UAV navigation systems and emphasizes the importance of securing these systems against DL-based Trojan attacks. We design various trigger patterns for collision and steering angle of the DroNet model incorporating adversarial inputs to test the robustness of the deep learning algorithm used for UAV navigation. By simulating potential attacks and studying their effects, we aim to highlight the weaknesses and potential entry points for malicious interference. We assess the effectiveness of Trojan attacks on the DroNet model using poisoned collision and steering angle datasets. Subsequently, we regulate the intensity of the designed triggers and evaluate the performance of the DroNet architecture. Additionally, we propose mitigation strategies to enhance the robustness and security of navigation systems against these attacks. To identify the likelihood of the trained model being trojaned or not, we have developed a Trojan detector and created distinct DroNet Trojan Model Datasets for this purpose. that the DroNet model is vulnerable to DL-based Trojan attacks, as evidenced by the successful manipulation of collision and steering angle predictions. The Trojan detector effectively identifies potential compromises within the model, highlighting the necessity for enhanced security measures.

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.