Related papers: Resource-efficient Transmission of Vehicular Senso…
Mobile crowdsensing harnesses the sensing power of modern smartphones to collect and analyze data beyond the scale of what was previously possible. In a mobile crowdsensing system, it is paramount to incentivize smartphone users to provide…
Task-oriented semantic communications (TSC) enhance radio resource efficiency by transmitting task-relevant semantic information. However, current research often overlooks the inherent semantic distinctions among encoded features. Due to…
The research project HDV-Mess aims at a currently missing, but very crucial component for addressing important challenges in the field of connected and automated driving on public roads. The goal is to record traffic events at various…
The way of analyzing, designing and building of real-time projects has been changed due to the rapid growth of internet, mobile technologies and intelligent applications. Most of these applications are intelligent, tiny and distributed…
While privacy concerns entice connected and automated vehicles to incorporate on-board federated learning (FL) solutions, an integrated vehicle-to-everything communication with heterogeneous computation power aware learning platform is…
Novel vehicular communication methods are mostly analyzed simulatively or analytically as real world performance tests are highly time-consuming and cost-intense. Moreover, the high number of uncontrollable effects makes it practically…
Task-Oriented Semantic Communication (TOSC) has been regarded as a promising communication framework, serving for various Artificial Intelligence (AI) task driven applications. The existing TOSC frameworks focus on extracting the full…
Through connecting intelligent vehicles as well as the roadside infrastructure, the perception range of vehicles can be significantly extended, and hidden objects at blind spots can be efficiently detected and avoided. To realize this,…
Connected vehicles (CVs) can capture and transmit detailed data like vehicle position, speed and so on through vehicle-to-vehicle and vehicle-to-infrastructure communications. The wealth of CV data provides new opportunities to improve the…
Traffic signal control systems (TSCSs) are integral to intelligent traffic management, fostering efficient vehicle flow. Traditional approaches often simplify road networks into standard graphs, which results in a failure to consider the…
Semantic communications are expected to improve the transmission efficiency in Internet of Things (IoT) networks. However, the distributed nature of networks and heterogeneity of devices challenge the secure utilization of semantic…
Traffic scene recognition, which requires various visual classification tasks, is a critical ingredient in autonomous vehicles. However, most existing approaches treat each relevant task independently from one another, never considering the…
Rich semantic information extraction plays a vital role on next-generation intelligent vehicles. Currently there is great amount of research focusing on fundamental applications such as 6D pose detection, road scene semantic segmentation,…
Advancement of mobile technologies has enabled economical collection, storage, processing, and sharing of traffic data. These data are made accessible to intended users through various application program interfaces (API) and can be used to…
The current development trend of wireless communications aims at coping with the very stringent reliability and latency requirements posed by several emerging Internet of Things (IoT) application scenarios. Since the problem of realizing…
In forthcoming years, the Internet of Things (IoT) will connect billions of smart devices generating and uploading a deluge of data to the cloud. If successfully extracted, the knowledge buried in the data can significantly improve the…
Autonomous mobility systems increasingly operate in dense and dynamic environments where perception occlusions, limited sensing coverage, and multi-agent interactions pose major challenges. While onboard sensors provide essential local…
This work introduces an integrated approach to optimizing urban traffic by combining predictive modeling of vehicle flow, adaptive traffic signal control, and a modular integration architecture through distributed messaging. Using real-time…
In smart cities, context-aware spatio-temporal crowd flow prediction (STCFP) models leverage contextual features (e.g., weather) to identify unusual crowd mobility patterns and enhance prediction accuracy. However, the best practice for…
In this paper, we address the problem of distributing a large amount of bulk data to a sparse vehicular network from roadside infostations, using efficient vehicle-to-vehicle collaboration. Due to the highly dynamic nature of the underlying…