Function-Specific Uncertainty Communication in Automated Driving

Conveying the overall uncertainties of automated driving systems was shown to improve trust calibration and situation awareness, resulting in safer takeovers. However, the impact of presenting the uncertainties of multiple system functions has yet to be investigated. Further, existing research lacks recommendations for visualizing uncertainties in a driving context. The first study outlined in this publication investigated the implications of conveying function-specific uncertainties. The results of the driving simulator study indicate that the effects on takeover performance depends on driving experience, with less experienced drivers benefitting most. Interview responses revealed that workload increments are a major inhibitor of these benefits. Based on these findings, the second study explored the suitability of 11 visual variables for an augmented reality-based uncertainty display. The results show that particularly hue and animation-based variables are appropriate for conveying uncertainty changes. The findings inform the design of all displays that show content varying in urgency.

The FGLOCTweet Corpus: An English tweet-based corpus for fine-grained location-detection tasks

Location detection in social-media microtexts is an important natural language processing task for emergency-based contexts where locative references are identified in text data. Spatial information obtained from texts is essential to understand where an incident happened, where people are in need of help and/or which areas have been affected. This information contributes to raising emergency situation awareness, which is then passed on to emergency responders and competent authorities to act as quickly as possible. Annotated text data are necessary for building and evaluating location-detection systems. The problem is that available corpora of tweets for location-detection tasks are either lacking or, at best, annotated with coarse-grained location types (e.g. cities, towns, countries, some buildings, etc.). To bridge this gap, we present our semi-automatically annotated corpus, the Fine-Grained LOCation Tweet Corpus (FGLOCTweet Corpus), an English tweet-based corpus for fine-grained location-detection tasks, including fine-grained locative references (i.e. geopolitical entities, natural landforms, points of interest and traffic ways) together with their surrounding locative markers (i.e. direction, distance, movement or time). It includes annotated tweet data for training and evaluation purposes, which can be used to advance research in location detection, as well as in the study of the linguistic representation of place or of the microtext genre of social media.

Use of Humanoid Robots for Students With Intellectual Disabilities

In parallel with the significant developments in robotics, humanoid robots have become popular recently. It is known that when humanoid robots are used for educational goals, students become more interested in learning activities, develop better situation awareness through exercises and physical activities, and learn more effectively. Therefore, humanoid robots will possibly play a key role in education in the future. Since humanoid robots have enhanced social skills, are able to repeat a particular sequence many times, and provide real-time feedback, they can improve the engagement of students with intellectual disabilities and may find significant acceptance in specific target groups, such as students with autism spectrum disorder. In this chapter, the authors investigate the use of humanoid robots for students with intellectual disabilities and review existing approaches in this domain. In addition, limitations and challenges to the use of humanoid robots for educational goals are discussed. Finally, the authors investigate research challenges in this domain and state future research directions.

SDAE+Bi-LSTM-Based Situation Awareness Algorithm for the CAN Bus of Intelligent Connected Vehicles

With a deep connection to the internet, the controller area network (CAN) bus of intelligent connected vehicles (ICVs) has suffered many network attacks. A deep situation awareness method is urgently needed to judge whether network attacks will occur in the future. However, traditional shallow methods cannot extract deep features from CAN data with noise to accurately detect attacks. To solve these problems, we developed a SDAE+Bi-LSTM based situation awareness algorithm for the CAN bus of ICVs, simply called SDBL. Firstly, the stacked denoising auto-encoder (SDAE) model was used to compress the CAN data with noise and extract the deep spatial features at a certain time, to reduce the impact of noise. Secondly, a bidirectional long short-term memory (Bi-LSTM) model was further built to capture the periodic features from two directions to enhance the accuracy of the future situation prediction. Finally, a threat assessment model was constructed to evaluate the risk level of the CAN bus. Extensive experiments also verified the improved performance of our SDBL algorithm.

Design, simulation and optimisation of lattice structures for remote control aeroplane

PurposeThus, in this work the goal is to design, simulate and optimise a holder of a brushless motor in lattice structure to get the best performance in terms of mechanical strength, vibration absorption and lightness.Design/methodology/approachNowadays, most manufacturers and designers' goal are to sell efficient products in mass to keep up or outrun competition. Medical, aeronautical, automobile and civil engineering sectors produce complex parts and products that encompasses multiple properties such as lightweight, energy absorbance, vibration reduction and stress resistant. Studies found that lattice structures are more and more useful in these fields since their characteristics satisfy complex behaviour.FindingsThe study's outcome suggests that the use of lattice structure reduces 60% of the actual motor holder mass while keeping the strength of the material, meeting initial specifications.Research limitations/implicationsThe Ram capacity of the PC.Practical implicationsLight materials for aerospace engineering elongate the range of the unmanned aerial vehicle (UAV) to an extra range of flight.Social implicationsSituation awareness of the country border using surveillance drone and minimising the consumption of fuel.Originality/valueThe research allowed reducing 60% the actual holder mass.

The Identification of Non-Driving Activities with Associated Implication on the Take-Over Process

In conditionally automated driving, the engagement of non-driving activities (NDAs) can be regarded as the main factor that affects the driver’s take-over performance, the investigation of which is of great importance to the design of an intelligent human–machine interface for a safe and smooth control transition. This paper introduces a 3D convolutional neural network-based system to recognize six types of driver behaviour (four types of NDAs and two types of driving activities) through two video feeds based on head and hand movement. Based on the interaction of driver and object, the selected NDAs are divided into active mode and passive mode. The proposed recognition system achieves 85.87% accuracy for the classification of six activities. The impact of NDAs on the perspective of the driver’s situation awareness and take-over quality in terms of both activity type and interaction mode is further investigated. The results show that at a similar level of achieved maximum lateral error, the engagement of NDAs demands more time for drivers to accomplish the control transition, especially for the active mode NDAs engagement, which is more mentally demanding and reduces drivers’ sensitiveness to the driving situation change. Moreover, the haptic feedback torque from the steering wheel could help to reduce the time of the transition process, which can be regarded as a productive assistance system for the take-over process.