AI-based mission-critical software exposes a blessing and a curse: its inherent statistical nature allows for flexibility in result quality, yet the mission-critical importance demands adherence to stringent constraints such as execution deadlines. This creates a space for trade-offs between the Quality of Result (QoR)-a metric that quantifies the quality of a computational outcome-and other application attributes like execution time and energy, particularly in real-time scenarios. Fluctuating resource constraints, such as data transfer to a remote server over unstable network connections, are prevalent in mobile and edge computing environments-encompassing use cases like Vehicle-to-Everything, drone swarms, or social-VR scenarios. We introduce a novel approach that enables software engineers to easily specify alternative AI service chains-sequences of AI services encapsulated in microservices aiming to achieve a predefined goal-with varying QoR and resource requirements. Our methodology facilitates dynamic optimization at runtime, which is automatically driven by the MARQ framework. Our evaluations show that MARQ can be used effectively for the dynamic selection of AI service chains in real-time while maintaining the required application constraints of mission-critical AI software. Notably, our approach achieves a 100x acceleration in service chain selection and an average 10% improvement in QoR compared to existing methods.

Recent advancements in deep learning have significantly enhanced the development of efficient models for multi-modal path prediction within urban environments, offering approaches to navigate complex environments accurately. Despite their performance, models grounded in deep learning techniques frequently encounter challenges related to interpretability. This limitation not only hampers their practical application but also complicates the process of diagnosing and rectifying errors within these systems, which is a critical factor for ensuring reliability and safety in realworld deployments. In this paper we propose NeSyMoF, a Neuro-Symbolic model for Motion Forecasting, to address this critical gap by combining the predictive power of deep neural networks with the interpretable logic inherent in symbolic reasoning. Data processing in NeSyMoF involves extracting pertinent features from the agent’s environment and channeling them into a neuro-symbolic reasoning module. The neurosymbolic reasoning module generates first-order logic rules that describe and condition the path prediction process, thereby providing clear explanations and intentions behind the forecasts of the model. We evaluate our model with the Argoverse benchmark for path forecasting, as it includes challenging driving situations, necessary to extensively evaluate our model. The results of our evaluation show that NeSyMoF outperforms state-of-the-art interpretable models for single-mode predictions while providing logic-based explanations for its forecasts, that articulate the reasoning behind predictions, making NeSyMoF more adapted for human-centric applications.

The annotation of 3D point cloud datasets is an expensive and tedious task. To optimize the annotation process, recent works have proposed the use of environments with higher levels of immersion in combination with different types of visual aids. However, two problems remain unresolved. First, the proposed environments limit the user to a unique level of immersion and a fixed hardware setup. Second, their design overlooks the interaction effects between the level of immersion and the visual aids on the quality of the annotation process. To address these issues, we propose PointCloudLab, an environment for 3D point cloud annotation that allows the use of different levels of immersion that work in combination with visual aids. Using PointCloudLab, we conducted a controlled experiment (N=20) to investigate the effects of levels of immersion and visual aids on the annotation process. Our findings reveal that higher levels of immersion combined with object-based visual aids lead to a faster and more accurate annotation. Furthermore, we found significant interaction effects between the levels of immersion and the visual aids on the accuracy of the annotation.

The recent advances in deep learning have paved the way for autonomous vehicles (AVs) to take charge of more complex tasks in the navigation process. However, predictions of deep learning models are subject to different types of uncertainty that may put the user and the surrounding environment in danger. In this paper, we investigate the effects that AR-based visualizations of 3 types of uncertainties in deep learning modules for path planning in AVs may have on drivers. The uncertainty types of the deep learning models that we consider are: the waypoint uncertainty, the situation uncertainty, and the path uncertainty. We propose 3 concepts to visualize the 3 uncertainty types on a Windshield display. We evaluate our AR-based concepts with a user study $(\mathrm{N}=20)$ using a VR-based immersive environment, to ensure the security of the participants. The results of our evaluation reveal that the absence of uncertainty visualization leads to lower driver engagement. More importantly, the combination of situation uncertainty and path uncertainty visualizations leads to higher driver engagement, and higher trust in the automated vehicle, while inducing an acceptable mental load for the drive.