Augmented Reality (AR) and edge computing can be used to enhance Vulnerable Road User (VRU) safety, leveraging a greater degree of interaction with the user and presenting real-time warning notifications, a use case that demands low latency for critical warnings. However, a new Multi-Access Edge Computing (MEC) system that distributes computational needs across edge nodes within the infrastructure is needed to meet these low-latency requirements. This paper proposes a microservice architecture designed to process data from various sources, including awareness and perception messages from road users and smart city sensors, through multiple communications technologies such as ITS-GS and 5G. This work employs peer-to-peer decentralized communications to perform seamless inte-gration and real-time processing, ensuring timely and relevant warnings for VRUs. Real-world road tests conducted in the Aveiro Tech City Living Lab in Aveiro, Portugal, and in commercial V2X equipment in the Autonomous Driving Test Field Baden-Wurttemberg in Karlsruhe, Germany, showed a maximum end-to-end latency of 110 ms, showcasing the system's capabilities in real-life demanding use cases and the system's interoperability.

Augmented Reality (AR) holds great potential for enhancing pedestrian’s urban experiences; however, its use in road traffic poses safety concerns due to potential distractions from interacting with AR interfaces. This paper investigates the effectiveness of AR applications for assisting pedestrians in crossing scenarios, against traditional crossing methods, by incorporating a collision warning system that uses an arrow to indicate the direction of a potential danger, and a virtual traffic light showing whether it is safe to cross. By leveraging Vehicle-to-Everything (V2X) communications within the living lab of Aveiro, Portugal, we conducted a user study to evaluate involved workloads, perceived safety and system usability in a realistic scenario. The findings from our study involving 20 participants reveal significant improvements in pedestrians’ perceived safety and a decrease in the perceived workload when using AR for pedestrian crossings, with both collision warning systems and virtual traffic lights demonstrating excellent usability.

E-bikes are recognized for their sustainable transportation benefits. However, the higher speeds associated with e-bikes pose an increased risk of potential accidents and hinder fluid riding in swarms with conventional bicycles. In this paper, we analyze the accuracy of maintaining an unknown speed, assess the associated workload, and investigate the self-reported speeds of e-bike cyclists in order to adapt the electric assistance to dynamic speed limits based on the surrounding traffic conditions. Our results from a pilot study with 15 participants show that the accuracy of maintaining a speed limit through active motor control and the associated workload are influenced by factors such as the level of electrical assistance and the perception of motor disengagement. E-bike cyclists using higher levels of electrical assistance demonstrated more accurate target speed maintenance. On average, participants consistently underestimated adapted speed limits, which were also influenced by the level of electrical support.

As Connected and Automated Vehicles (CAVs) introduce new maneuvers like platooning on the roads, manual drivers face challenges in adapting to reduced headway distances and automated behaviors.These interactions can affect safety and traffic flow, making it essential to study how drivers respond to CAVs in such scenarios.To address this, we present our virtual reality (VR) driving simulator, which replicates highway environments with automated vehicle platoons.By measuring objective data such as steering inputs, lane changes, and headway distance, we aim to analyze manual drivers' performance in proximity to automated platoons.Our configurable mobile system allows for diverse testing conditions and provides valuable insights to advance Cooperative, Connected, and Automated Mobility (CCAM) technologies, contributing to improved road safety and design.Furthermore, we briefly review related works, discuss the design and functionality of the simulator, and provide an outlook on future research directions.

Public bookcases offer the opportunity to serendipitously discover books and to anonymously share books with others. The set of available books as well as the sharing patterns are highly dynamic, as anybody can freely take or donate books. This makes it difficult for users to see what is available or of interest to them. To support book sharing via public bookcases we developed a mobile AR application that highlights relevant books in the camera viewfinder and that facilitates searching for specific books. The application recognizes books via text and color features on the spine. In a lab study with 15 participants we evaluated our book recognition algorithm and found that it outperforms unaided visual search. We interviewed users of public bookcases and analyzed the bookcases’ setup and rate of change. A subsequent field evaluation of the AR application on nine public bookcases found a recognition accuracy of 80 % for 450 books under different conditions. The proposed approach provides the basis for effectively sharing books via public bookcases.

Winter sports like skiing are becoming increasingly popular for both competitive and recreational activities. To minimize the risk of injury, new innovations in skiing equipment have been developed in recent years. However, unexpected slope conditions can still increase risks during skiing. The static categorisation of ski slopes in winter sports resorts does not take into account dynamic changes of difficulty due to high traffic volumes or sudden weather changes. Up to now, efforts have been made to measure the current conditions via satellite imaging or installations on the slope. However, this requires intervention in nature and causes high maintenance costs. To solve these issues we present our preliminary design of a wearable system to let skiers implicitly measure current slope conditions during their skiing experience. Audio and motion data are recorded from a prototype mounted on a ski boot. We show that the data generated by the prototype can be successfully classified with a neural network. We collected data from a skiing activity to demonstrate our concept and discuss the identified challenges in fitting the proposed approach to winter sports equipment.

Digital pens emit ink on paper and digitize handwriting. The range of the pen is typically limited to a special writing surface on which the pen's tip is tracked. We present Pentelligence, a pen for handwritten digit recognition that operates on regular paper and does not require a separate tracking device. It senses the pen tip's motions and sound emissions when stroking. Pen motions and writing sounds exhibit complementary properties. Combining both types of sensor data substantially improves the recognition rate. Hilbert envelopes of the writing sounds and mean-filtered motion data are fed to neural networks for majority voting. The results on a dataset of 9408 handwritten digits taken from 26 individuals show that motion+sound outperforms single-sensor approaches at an accuracy of 78.4% for 10 test users. Retraining the networks for a single writer on a dataset of 2120 samples increased the precision to 100% for single handwritten digits at an overall accuracy of 98.3%.