PCCO - Privacy-preserving Collaborative Control and Optimization in VANETs

• Applicant: Professor Dr.-Ing. Ansgar Trächtler, since 6/2020
• Subject Area: Automation, Mechatronics, Control Systems, Intelligent Technical Systems, Robotics
• Term: from 2018 to 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 392194080

With the rapid development of information technology and the extensive applications of automotive electronic systems, a new kind of network model, namely Vehicular Ad Hoc NETworks (VANETs), came into being. VANET is a spontaneous creation of ``human-vehicle-road-cloud'' interconnection network for data exchange between vehicles, RoadSide Units, and cloud. With this technology, vehicles have capabilities of sensing, computation, data storage and wireless communication, while the cloud contains multiple Service Providers. The forms of Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) and Vehicle-to-Everything (V2X) communications are widely used in VANETs. Meanwhile, thanks to cloud computing, big data processing, and other technical supports, one foresees real-time information extraction, sharing and efficient utilization of vehicles and road traffic, enabling the vehicles' intelligent decision according to their personalized demands. To head to a more efficient, green and safe intelligent transportation system, there is a need for using VANETs to extract, mine and apply real-time traffic flow information for collaborative control and optimization. Using VANETs, local information about individual vehicles' driving states and the surrounding traffic can be gathered to the cloud. Meanwhile, using only real-time traffic information, the cloud could respond to different vehicles' path planning requests and provide personalized services, yielding an improved traffic service. Furthermore, through V2V communication and collaborative control, vehicles could move in an organized way, which makes it possible to enhance road utility and traffic safety and reduce greenhouse gas emissions. The coordination between V2V and V2I relies on sharing sensitive information (e.g., real-time location, driving status, etc.), which could be used to infer users' private information, such as identity, location, and driving habits. This raises the issue of privacy protection and the necessity to study privacy-preserving collaborative control and optimization in VANETs. Here, it is worthwhile to investigate privacy through data desensitization mechanisms. Such methods allow one to trade privacy for system performance, by introducing a suitable amount of information deformation and distortion. Using such tools for real-time collaborative control and optimization of VANETs is challenging. Within this context, this proposal aims to: 1) design multi-source, high-dimensional data fusion and traffic forecasting mechanisms with the premise of incomplete and privacy-preserving information; 2) devise on-demand path planning algorithms in the global network that can respond to a mass of path planning requests; 3) explore efficient platoon scheduling and grouping methods of heterogeneous vehicles, as well as adaptive and low-latency collaborative control rules.

• DFG Programme: Research Grants
• International Connection: China
• Partner Organisation: National Natural Science Foundation of China
• Co-Investigator: Professor Peng Cheng, Ph.D.
• Ehemaliger Antragsteller: Professor Dr. Daniel Quevedo, until 6/2020