Two emerging technologies in the automotive domain are autonomous vehicles and V2X communication. Even though these technologies are usually considered separately, their combination enables two key cooperative features: sensing and maneuvering. Cooperative sensing allows vehicles to exchange information gathered from local sensors. Cooperative maneuvering permits inter-vehicle coordination of maneuvers. These features enable the creation of cooperative autonomous vehicles, which may greatly improve traffic safety, efficiency, and driver comfort. The first generation V2X communication systems with the corresponding standards, such as Release 1 from ETSI, have been designed mainly for driver warning applications in the context of road safety and traffic efficiency, and do not target use cases for autonomous driving. This article presents the design of core functionalities for cooperative autonomous driving and addresses the required evolution of communication standards in order to support a selected number of autonomous driving use cases. The article describes the targeted use cases, identifies their communication requirements, and analyzes the current V2X communication standards from ETSI for missing features. The result is a set of specifications for the amendment and extension of the standards in support of cooperative autonomous driving.

Nanonetworks, the interconnection of nanosystems, are envisaged to greatly expand the applications of nanotechnology in the biomedical, environmental and industrial fields. However, it is still not clear how these nanosystems will communicate among them. This work considers a scenario of Diffusion-based Molecular Communication (DMC), a promising paradigm that has been recently proposed to implement nanonetworks. In a DMC network, transmitters encode information by the emission of molecules which diffuse throughout the medium, eventually reaching the receiver locations. In this scenario, a pulse-based modulation scheme is proposed and two techniques for the detection of the molecular pulses, namely, amplitude detection and energy detection, are compared. In order to evaluate the performance of DMC using both detection schemes, the most important communication metrics in each case are identified. Their analytical expressions are obtained and validated by simulation. Finally, the scalability of the obtained performance evaluation metrics in both detection techniques is compared in order to determine their suitability to particular DMC scenarios. Energy detection is found to be more suitable when the transmission distance constitutes a bottleneck in the performance of the network, whereas amplitude detection will allow achieving a higher transmission rate in the cases where the transmission distance is not a limitation. These results provide interesting insights which may serve designers as a guide to implement future DMC networks.

Vehicle-to-everything (V2X) communication is seen as one of the main enabling technologies for automated vehicles. Collective perception is especially promising, as it allows connected traffic participants to “see through the eyes of others” by sharing sensor-detected objects via V2X communication. Its benefit is typically assessed in terms of the increased object update rate, redundancy, and awareness. To determine the safety improvement thanks to collective perception, the authors introduce new metrics, which quantify the environmental risk awareness of the traffic participants. The performance of the V2X service is then analyzed with the help of the test platform TEPLITS, using real traffic traces from German highways, amounting to over 100 h of total driving time. The results in the considered scenarios clearly show that collective perception not only contributes to the accuracy and integrity of the vehicles’ environmental perception, but also that a V2X market penetration of at least 25% is necessary to increase traffic safety from a “risk of serious traffic accidents” to a “residual hypothetical risk of collisions without minor injuries” for traffic participants equipped with non-redundant 360° sensor systems. These results support the ongoing worldwide standardization efforts of the collective perception service.

Cooperative Automated Driving (CAD) brings together driving automation technology with vehicle-to-X (V2X) communication in order to enable vehicles to coordinate their driving maneuvers and achieve a common global knowledge of their surroundings, leading to safer and more efficient driving. The IMAGinE research project develops a CAD system based on collective perception and cooperative maneuver coordination and applies it to several use cases where CAD is expected to bring a significant benefit. Furthermore, a connected lane merge function, recently demonstrated as proof-of-concept, shows a first step towards a safe and efficient cooperation among automated vehicles. 5G direct vehicle-to-vehicle communication may enable the implementation of CAD due to its enhanced communication performance.