Autonomous vehicles (AVs) will soon become a reality with almost every major automotive original equipment manufacturer (OEM) such as Tesla, BMW, Daimler, and big technology giants such as Google's Waymo and Apple Car pushing toward the full self-driving objective. According to the SAE J3016 taxonomy [1] of autonomous driving, there are six levels of automated driving systems ranging from level 0 (completely manual) to level 5 (full selfdriving [FSD]) automated systems that are expected to function in all geographic locations, all weather conditions, and under all conditions. The proposed benefits of intelligent vehicles include fewer road accidents, enhanced safety, reduced traffic congestion, effective commute time usage, and importantly enjoyable and comfortable ride. With increased autonomy, the drivers also assume the role of mere passengers who are engaged in nondriving activities and are unavailable to participate in traffic interactions. This would increase complexity in a mixed-autonomy traffic environment as interactions with pedestrians and cyclists are based on visual cues with the driver. Therefore, intelligent vehicles also need to autonomously interact with other traffic participants such as pedestrians and cyclists and other vehicles.Human-vehicle interaction (HVI) is closely related to the field of human-robot interaction (HRI). It entails the problem of understanding and shaping the interaction dynamics between humans and vehicles. Particularly, the domain of interaction involves the study of sensation, perception, information exchange, inference, and decision-making. [2] With increasing levels of automation, the driver will have more time and choice to perform various tasks other than driving and this opens new avenues for interaction. As a result, a growing number of sensors are being incorporated into the vehicles to understand the driver's and/or passenger's actions, emotions, and personal choices to offer the precise functionalities and services for an enjoyable ride. [3] Figure 1 shows the schematic of in-vehicle interaction consisting of various interactive interfaces and sensing modalities present in the vehicle as well as some modes of interaction such as gestures, speech, and eye gaze.The advances in HVI have been presented in previous review articles, which have mainly focused on the detection of particular characteristics for driving assistance such as driver attention detection, [4] emotion recognition, [5] drowsiness detection, [6] mental workload, [7] and so on. A few other articles have reviewed the interior designs of AVs to better support and interact with drivers and passengers. [8,9] Likewise, the user interfaces (UIs) and user experience (UX) of vehicle interiors have been studied in context with manual as well as automated driving. [10] A number of previous works have also studied the interaction with the external

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The objective of this paper is to present a framework for quality software development in the Scientific Application area. In a scientific application area, the specification and configuration control phases of the development cycle generally do not pose a serious problem. In most cases, a compact and comprehensive specification can be arrived at solely from the field of application. However, support is expected in the development and maintenance phases of the software life cycle. With this assumption, an integrated Fortran Environment and Scientific CASE Tool (FE/SCT) was designed, developed, and implemented for the DOS operating system.

We propose for the first time, a novel deep active visuo-tactile cross-modal full-fledged framework for object recognition by autonomous robotic systems. Our proposed network xAVTNet is actively trained with labelled point clouds from a vision sensor with one robot and tested with an active tactile perception strategy to recognise objects never touched before using another robot. We propose a novel visuo-tactile loss (VTLoss) to minimise the discrepancy between the visual and tactile domains for unsupervised domain adaptation. Our framework leverages the strengths of deep neural networks for cross-modal recognition along with active perception and active learning strategies for increased efficiency by minimising redundant data collection. Our method is extensively evaluated on a real robotic system and compared against baselines and other state-of-art approaches. We demonstrate clear outperformance in recognition accuracy compared to the state-of-art visuo-tactile cross-modal recognition method.

Accurate object pose estimation using multi-modal perception such as visual and tactile sensing have been used for autonomous robotic manipulators in literature. Due to variation in density of visual and tactile data, we previously proposed a novel probabilistic Bayesian filter-based approach termed translation-invariant Quaternion filter (TIQF) for pose estimation. As tactile data collection is time consuming, active tactile data collection is preferred by reasoning over multiple potential actions for maximal expected information gain. In this paper, we empirically evaluate various information gain criteria for action selection in the context of object pose estimation. We demonstrate the adaptability and effectiveness of our proposed TIQF pose estimation approach with various information gain criteria. We find similar performance in terms of pose accuracy with sparse measurements across all the selected criteria.

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