Automated driving will have a big impact on society, creating new possibilities for mobility and reducing road accidents. Current developments aim to provide driver assistance in the form of conditional and partial automation. Computer vision, either alone or combined with other technologies such as radar or lidar, is one of the key technologies of advanced driver assistance systems (ADAS). The presence of vision technologies inside the vehicles is expected to grow as the automation levels increase. However, embedding a vision‐based driver assistance system supposes a big challenge due to the special features of vision algorithms, the existing constrains and the strict requirements that need to be fulfilled. The aim of this study is to show the current progress and future directions in the field of vision‐based embedded ADAS, bridging the gap between theory and practice. The different hardware and software options are reviewed, and design, development and testing considerations are discussed. Additionally, some outstanding challenges are also identified.

Fifth-generation (5G) mobile networks aim to be qualified as the core connectivity infrastructures to address connected automated mobility (CAM), both from a technological and from a business perspective, for the higher automation levels defined by the automotive industry. Specifically, in some territories such as the European Union the cross-border corridors have relevance, as they are the cohesive paths for terrestrial transport. Therefore, 5G for CAM applications is planned to be deployed there first. However, cross-border contexts imply paramount communication challenges, such as seamless roaming, not addressed by current technology. This paper identifies relevant future 5G enhancements, specifically those specified by Third-Generation Partnership Project (3GPP) releases beyond Release 15, and outlines how they will support the ambitions of highly automated driving in cross-border corridors. In order to conduct this study, a set of representative use cases and the related communication requirements were identified. Then, for each use case, the most relevant 5G features were proposed. Some open issues are described at the end.

In this study, the authors analyse the exponential growth of advanced driver assistance systems based on video processing in the past decade. Specifically, they focus on how research and innovative ideas can finally reach the market as cost‐effective solutions. They explore well‐known computer vision methods for services like lane departure warning systems, collision avoidance systems and point out potential future trends according to a review of the state‐of‐the‐art. Along this study, the authors’ own contributions are described as examples of such systems from the perspective of real‐time by design, pursuing a trade‐off between the accuracy and reliability of the designed algorithms and the restrictive computational, economical and design requisites of embedded platforms.

The reliability and availability of network connectivity, which significantly varies with mobility, is crucial in Connected, Cooperative and Automated Mobility (CCAM). Handover and roaming are the most challenging situations in terms of connectivity of cellular networks, which require switching across cells of the same cellular network or between Public Land Mobile Networks (PLMNs). This paper proposes a set of solutions for vehicular applications to mitigate the impact of mobility in service continuity, including a dual modem solution that reduces the interruption time when switching PLMNs, an adaptive bitrate mechanism for media streaming that increases reliability, a WebRTC server acting as a gateway in media streaming sessions between vehicles, and a MEC discovery and handover method. The proposed solutions have been evaluated executing an Extended Sensors application in several commercial and experimental 5G Non-Standalone (NSA) and Stand Alone (SA) setups with different Multi-access Edge Computing (MEC), edge-cloud and cloud infrastructures to host services. It can be concluded from the results obtained that 5G networks have not yet achieved the required performance for CCAM, and that practitioners need to implement solutions and workarounds, such as the ones proposed in this work, to mitigate the issues. As lessons learnt from the deployment and experimentation, this paper also overviews a detailed set of problems and the proposed solutions that CCAM industry and cellular network stakeholders need to consider.