The introduction of autonomous vehicles (AV) will represent a milestone in the evolution of transportation and personal mobility. AVs are expected to significantly reduce accidents and congestion, while being economically and environmentally beneficial. However, many challenges must be overcome before reaching this ideal scenario. This study, which results from on‐site visits to top research centres and a comprehensive literature review, provides an overall state‐of‐the‐practice on the subject and identifies critical issues to succeed. For example, although most of the required technology is already available, ensuring the robustness of AVs under all boundary conditions is still a challenge. Additionally, the implementation of AVs must contribute to the environmental sustainability by promoting the usage of alternative energies and sustainable mobility patterns. Electric vehicles and sharing systems are suitable options, although both require some refinement to incentivise a broader range of customers. Other aspects could be more difficult to resolve and might even postpone the generalisation of automated driving. For instance, there is a need for cooperation and management strategies geared towards traffic efficiency. Also, for transportation and land‐use planning to avoid negative territorial and economic impacts. Above all, safe and ethical behaviour rules must be agreed upon before AVs hit the road.

Platooning of connected automated vehicles (CAVs) on highways has attracted the interest of researchers, companies and administrations for years. This interest has yielded a vast literature on CAV platooning, which generally describes its benefits in terms of traffic efficiency, safety and environmental impacts, among others. However, the boundary conditions of these studies and the reported magnitude of these benefits vary greatly among the different contributions. In this context, this paper presents the result of a comprehensive literature review on CAV platooning on highways, organizing the existing research and establishing links between the different strategies proposed and their expected impacts. Starting points for future investigations are suggested, and the research gaps to be addressed are identified. The analysis performed shows that the current state-of-the-art has two major limitations. First, few CAV platooning strategies have been proven to be asymptotically stable, which should be a requirement for any feasible solution. Second, in most cases, the CAV platooning algorithms are simplistic adaptations from existing car-following models proposed for human driving that, therefore, unnecessarily transfer current traffic-related problems to future cooperative environments.

Short-term travel time prediction on freeways is the most valuable information for drivers when selecting their routes and departure times. Furthermore, this information is also essential at traffic management centers in order to monitor the network performance and anticipate the activation of traffic management strategies. The importance of reliable short-term travel time predictions will even increase with the advent of autonomous vehicles, when vehicle routing will strongly rely on this information. In this context, it is important to develop a real-time method to accurately predict travel times. The present paper uses vehicle accumulation, obtained from input-output diagrams constructed from loop detector data, to predict travel times on freeway sections. Loop detector count drift, which typically invalidates vehicle accumulation measurements, is corrected by means of a data fusion algorithm using GPS measurements. The goodness of the methodology has been proven under different boundary conditions using simulated data.