MACROSS · Multimedia Communication and Processing over Vehicular Cloud Networks for Autonomous Driving

Multimedia Communication and Processing over Vehicular Cloud Networks for AutonomousDriving

Since the invention, vehicles have significantly improved human’s life quality, but also brought serious traffic problems. Integrating information and communication technologies (ICT) into the transportation infrastructure to establish intelligent transportation system (ITS) has been deemed as an inevitable trend in the evolution of modern transportation and traffic management. Several companies and research labs have successfully demonstrated prototypes of automated cars. However, a great number of theoretical and technical challenges still need to be addressed in order to reap the full potential of ITS. For instance, the decision-making process of most current driver assistance applications and autonomous driving functions completely rely on the processing of on-board sensor data. Due to the physically constrained sensing range and accuracy, as well as limited computation and storage capacity, a single vehicle’s capability of understanding the complex driving environment can be unsatisfactory. An effective solution to this issue is to allow the sensing/computing/storage resources of different elements in the ITS to be shared, so that the environment perception and decision-making ability of each individual can be greatly enhanced. This idea stems from the cloud computing technology and constitutes a recently emerged concept, vehicular cloud network (VCN). Supported by the mobile edge computing (MEC) framework, VCN enables ITS services and applications that individual vehicles cannot produce alone. High-quality wireless communication that guarantees reliable real-time sensing data (normally in the form of multimedia) to be shared among vehicles and road-side infrastructure is the key to realize VCN. The vehicle-to-everything (V2X) communication technology has attracted tremendous research attentions. This motivates another technical path towards fully autonomous driving by improving the environment perception capability of smart cars with reduced cost through V2X communications. However, supporting future autonomous driving applications with shared sensing data is challenging. On the one hand, transmission protocols are designed mainly targeting on small-size bursty emergency-triggered messages or periodic vehicle-status messages. On the other hand, investigations exploiting the full potential of sharing content-rich and resource-hungry sensing data normally focus on effective data processing based on the assumptions that the wireless data delivery process is either sufficiently good or completely unpredictable. These designing perspectives would lead to unsatisfactory outcomes. To this end, the project aims to develop the effective methods that leverage innovative multimedia communication and multimedia processing technologies over VCNs to support future autonomous driving. It targets to tackle two challenges: 1) how to transmit resource-hungry multimedia sensing data in unreliable vehicular communication environments and maximize the data sharing quality; and 2) how to process the sensing data when only a certain level of knowledge regarding the network communication quality is available.

Data: CORDIS, © European Union