Intelligent Transportation Systems (ITS) have significantly improved transportation quality by using applications capable of monitoring, managing, and improving the transportation system. However, the large number of devices required to provide data to ITS applications has become a challenge in recent years, particularly the high installation and maintenance costs made broad deployment impracticable. Despite several advances in smart city research and the internet of things (IoT), research on ITS is still in the early stages. In this sense, to improve data collection and maintenance strategies for ITS systems, this article proposes a virtual infrastructure model based on data reuse, mainly autonomous vehicle (AV) data, to support ITS applications. It presents design choices and challenges for deploying a virtual infrastructure based on Beyond 5G (B5G) communication and data reuse, followed by developing a proof of concept of an AV data acquisition system evaluated through simulation. The results show that the extra data collection module results in a 1.1% increase in total memory usage with direct sensor collection and a 2.6% increase with application performance management (APM) data collection on the reference hardware. This data reuse setup can significantly improve ITS data challenges with minimal impact on current technology stack on the Autonomous vehicles currently in circulation. © 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

Road traffic noise (RTN) is a substantial environmental pollutant, implicated in various detrimental effects on public health. Regulatory initiatives have emerged to address RTN, aiming to integrate noise mitigation measures into vehicle design and road infrastructure. A precise and robust noise estimation is significant, serving as basis for effective and reliable assessment towards different environmental scenarios. This paper reviews recent research findings on road traffic noise modeling (RTNM), encompassing common linear regression approaches, emerging machine learning (ML) applications, as well as basic concepts for RTN. It specifically highlights the statistical challenges and modeling considerations such as uncertainties and multivariate analysis. © 2024 IEEE.

Communication between road users is crucial, as miscommunication and misunderstandings can lead to accidents, often bearing serious consequences in case Vulnerable Road Users (VRU) are involved. In this article we focus on communication solutions directed towards one particular group of VRUs, namely pedestrians. Various approaches for communication from vehicles to pedestrians have been proposed, including external Human-Machine Interfaces and Vehicle-to-Everything (V2X) communication. Surprisingly, these works are rarely looked at together. Therefore we jointly survey these separate fields as a starting point for the development of effective methods for interactions between vehicles and pedestrians. We argue that merging the perspectives of different fields can be beneficial as their approaches often complement each other. © 2024 IEEE.

The success in the adoption of autonomous vehicles is dependent on their ability to solve rarely occurring safety-critical corner cases. Vehicular communications (V2X) aim at improving safety and efficiency of autonomous driving by adding the capability of explicit inter-vehicular information exchange. We argue that V2X enables another important function, namely the support of ethical driving decisions. In this article, we explain our envisioned methodology of an ethical cooperative driving interaction protocol design (including what to communicate and how to act based on the received information). Cooperative manoeuvring empowered by the ethical V2X is a new research direction we promote in the autonomous driving research agenda. © 2024 IEEE.

While rare, it is widely accepted that autonomous vehicles (AVs) will find themselves in dilemma scenarios involving vulnerable road users (VRUs). The ethics of these dilemma situations have been debated extensively in the context of trolley problem-like scenarios. What has not been noted is the inherent unfairness implicit in many of these discussions in which VRUs are seen as passive by-standers with no say as to what befalls them. Rather than simply remaining still in a collision scenario, VRUs can (and often do) take action that needs to be accounted for. If we are to increase fairness on public roads, it is important that AVs communicate with VRUs. This paper presents a highly theoretical discussion on the possibility of using communication tools (such as the V2X system) and techniques (derived from the science of human-machine interaction) to elicit positive responses from VRU during inevitable AV collisions. The paper begins with a brief ethical exploration of fairness in the context of current debates surrounding AV collisions. We progress to discuss possible technical solutions to AV-VRU communication, as well as the types of audio, visual, and tactile communication strategies necessary in critical scenarios. © 2024 IEEE.

Formal models for the safety validation of autonomous vehicles have become increasingly important. To this end, we present a safety framework for longitudinal automated driving. This framework enables the calculation of minimum safe inter-vehicular distances for arbitrary ego vehicle control policies in a computationally efficient manner. We use this framework to enhance and generalize the Responsibility-Sensitive Safety (RSS) [1] model and models based on it, which fail to cover situations where the ego vehicle has a higher decelerating capacity than its preceding vehicle. © 2024 IEEE.

With power comes responsibility: as robots become more advanced and prevalent, the role they will play in human society becomes increasingly important. Given that violence is an important problem, the question emerges if robots could defend people, even if doing so might cause harm to someone. The current study explores the broad context of how people perceive the acceptability of such robot self-defense (RSD) in terms of (1) theory, via a rapid scoping review, and (2) public opinion in two countries. As a result, we summarize and discuss: increasing usage of robots capable of wielding force by law enforcement and military, negativity toward robots, ethics and legal questions (including differences to the well-known trolley problem), control in the presence of potential failures, and practical capabilities that such robots might require. Furthermore, a survey was conducted, indicating that participants accepted the idea of RSD, with some cultural differences. We believe that, while substantial obstacles will need to be overcome to realize RSD, society stands to gain from exploring its possibilities over the longer term, toward supporting human well-being in difficult times. © 2023 by the authors.

Connected and automated vehicles (CAVs) will be a key component of future cooperative intelligent transportation systems (C-ITS). Since the adoption of C-ITS is not foreseen to happen instantly, not all of its elements are going to be connected at the early deployment stages. We consider a scenario where vehicles approaching a traffic light are connected to each other, but the traffic light itself is not cooperative. Information about indented trajectories such as decisions on how and when to accelerate, decelerate and stop, is communicated among the vehicles involved. We provide an optimization-based procedure for efficient and safe passing of traffic lights (or other temporary road blockage) using vehicle-to-vehicle communication (V2V). We locally optimize objectives that promote efficiency such as less deceleration and larger minimum velocity, while maintaining safety in terms of no collisions. The procedure is computationally efficient as it mainly involves a gradient decent algorithm for one single parameter. © 2023 by the authors.

We argue that an information exchange between vehicles via the vehicular communications is the foundation for ethical driving. In other words - autonomous vehicles must be cooperative to be able to resolve ethical dilemmas in a multi-vehicle scenario. We show this by exploring the minimal setting of a longitudinal driving in a formation of three vehicles. © 2023 IEEE.

Sensor data sharing in V2X communication enables vehicles to exchange locally perceived sensor data with each other to increase their environmental awareness. It relies on the periodic exchange of selected, safety-relevant objects. Object selection is used to reduce channel resource usage. Additionally, vehicles use congestion control mechanisms to avoid overloading the channel. Currently, both object selection and congestion control mechanisms operate independently. We study a congestion-aware object filtering approach combining both and improving the performance of sensor data sharing. © 2023 Walter de Gruyter GmbH, Berlin/Boston.