Metaverse is expected to emerge as a new paradigm for the next-generation Internet, providing fully immersive and personalized experiences to socialize, work, and play in self-sustaining and hyper-spatio-temporal virtual world(s). The advancements in different technologies such as augmented reality, virtual reality, extended reality (XR), artificial intelligence (AI), and 5G/6G communication will be the key enablers behind the realization of AI-XR metaverse applications. While AI itself has many potential applications in the aforementioned technologies (e.g., avatar generation, network optimization), ensuring the security of AI in critical applications like AI-XR metaverse applications is profoundly crucial to avoid undesirable actions that could undermine users’ privacy and safety, consequently putting their lives in danger. To this end, we attempt to analyze the security, privacy, and trustworthiness aspects associated with the use of various AI techniques in AI-XR metaverse applications. Specifically, we discuss numerous such challenges and present a taxonomy of potential solutions that could be leveraged to develop secure, private, robust, and trustworthy AI-XR applications. To highlight the real implications of AI-associated adversarial threats, we designed a metaverse-specific case study and analyzed it through the adversarial lens. Finally, we elaborate upon various open issues that require further research interest from the community.

With the recent emergence of deep learning, computer vision-based applications have demonstrated better applicability in accomplishing driving tasks including drivable road region detection, lane keeping and steering control in self-driving vehicles. Till recently, numerous lane-marking detection based steering control and lane keeping methods have been proposed to perform autonomous driving on urban well-structured roads. But the matter of fact is that these methods are not feasible on roads where lane markings are not available or faded over time which makes drivable road region detection a crucial task. Also, it is highly difficult task to estimate the steering angle on such highly deteriorated roads using existing road detection and steering angle estimation methods. To the best of our knowledge, there is no standard benchmark available for drivable road region detection and steering angle estimation on unstructured roads. To this end, we present a large-scale dataset for drivable road detection, comprising of 15,000-pixel level high-quality fine annotations. Also, we present a novel end-to-end drivable road region detection and steering angle estimation method to ensure the autonomous driving on unstructured roads. The proposed method performs pixel-level segmentation to extract drivable road region and calculates the lane interception to estimate the steering angle of self-driving vehicles. Lastly, a comprehensive qualitative and quantitative analysis has been carried out to demonstrate the effectiveness of our proposed dataset, road detection and steering angle estimation method. Our benchmark is available at https://carl-dataset.github.io/index/.

Rear-end collision detection and avoidance is one of the most crucial driving tasks of self-driving vehicles. Mathematical models and fuzzy logic-based methods have recently been proposed to improve the effectiveness of the rear-end collision detection and avoidance systems in autonomous vehicles (AVs). However, these methodologies do not tackle real-time object detection and response problems in dense/dynamic road traffic conditions due to their complex computation and decision-making structures. In our previous work, we presented an affective computing-inspired Enhanced Emotion Enabled Cognitive Agent (EEEC_Agent), which is capable of rear-end collision avoidance using artificial human driver emotions. However, the architecture of the EEEC_Agent is based on an ultrasonic sensory system which follows three-state driving strategies without considering the neighbor vehicles types. To address these issues, in this paper we propose an extended version of the EEEC_Agent which contains human driver-inspired dynamic driving mode controls for autonomous vehicles. In addition, a novel end-to-end learning-based motion planner has been devised to perceive the surrounding environment and regulate driving tasks accordingly. The real-time in-field experiments performed using a prototype AV demonstrate the effectiveness of this proposed rear-end collision avoidance system.