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From the automatic driving vehicle to the intelligent vehicle, and then to intelligent connected vehicle, the intelligent driving vehicle technology and industry has developed rapidly, surveying and remote sensing technology have played an important supporting role. Firstly, this paper introduces the development of intelligent vehicles at home and abroad and their differences from conventional vehicles. Based on the perspective of three important waves of scientific and technological development, this paper comparatively analyzes and summarizes the development process and core technology driving forces of autonomous driving and surveying remote sensing. The intelligent driving vehicle's key technologies are then introduced from four aspects: top-level planning and policy environment, environment perception and computing decision-making vehicle key technology, high definition map and navigation positioning basic support key technology, and vehicle road collaborative information interaction key technology. Several advanced navigation and positioning technology achievements, such as satellite-based navigation enhancement, high precision position and attitude measurement, multi-source fusion perception, are taken as examples to demonstrate that surveying and mapping can enable intelligent driving of a single car. By introducing mobile measurement and crowdsourcing high precision map-making technology, the key supporting role of surveying and remote sensing for intelligent driving is expounded. This paper takes the construction of space-based information real-time service system as an example to describe the development trend of how surveying, mapping, and remote sensing will serve the "person vehicle road cloud" of intelligent connected vehicles in the future.Finally, the paper summarizes and analyzes the problems that need to be solved in developing intelligent (connected) vehicles and the challenges that need to be addressed in the near future.

The floating-car GNSS trajectory data have been widely used to obtain and predict the urban traffic status in real time, with wide coverage and low deployment cost, and the result has an important supporting role for route decision-making of automatic driving and traffic management. However, the traffic information predicted by the floating-car GNSS data only contains the traffic information on each road segment, ignoring the difference of the traffic flow in different driving directions at the intersection; besides, the accuracy of the traffic information is limited by the GNSS sampling frequency. This paper proposes a turning-level traffic prediction method based on graph convolutional network and low-frequency GNSS trajectory data: first, a queuing-starting-point estimation model is proposed considering vehicle movement pattern; second, a Graph-structure of the turning connection relationship is constructed based on the dual graph theory; finally, to consider spatio-temporal pattern, a traffic prediction model is constructed based on the graph convolutional network. The experimental results show that our method can accurately obtain and predict the traffic speed and queue length at turning-level, and effectively improve the accuracy of traffic prediction by learning the spatio-temporal pattern within the Graph.

The scale and impacts of city events, including cultural, entertainment and sporting events, reflect the economics and culture of a city to a certain extent.The occurrence of events affects urban city significantly both online in cyberspace and offline in real space. The evolutionary perception, dynamic modeling and spatio-temporal analysis of city events from cyberspace and real space, are of important theoretical research and application value. This article proposes a novel approach of city events spatio-temporal modeling and analysis with trajectories and social media datasets in real space and cyberspace respectively. The approach firstly identifies statistically significant anomalous city regions and traffic flows with trajectories during the events, analyzing spatio-temporal change of events in real space, then analyzes spatio-temporal change during the whole process of city events in cyberspace.Finally, this article presents a modeling approach for characterizing the development and evolution of urban geospatial and behavioral space throughout events, with datasets in cyberspace and real space combined. Taking the Opus Ⅱ Jay World Tour in 2015 as an example, employing taxi GPS trace data and Weibo data in Wuhan, the proposed method realizes the whole process modeling and evolution analysis of urban geospatial and behavioral space before, during and after the event. Then the method is compared with two other approaches that use either real space dataset or cyberspace dataset alone. The experimental results show that the proposed approach measures the impact of an event both in cyberspace and real space with reason, and describes city events effectively.

Concrete defect detection of diversion shafts in large hydropower station has a direct impact on the safe operation of the station,endangering the lives of cities and millions of people downstream.However,the research on the detection of the 100-meter-level shafts is still a blank in the world.It is a "blind spots" in detection field.In this paper,a method of integrating multiple mapping sensors based on unmanned airship is proposed to resolve the international problem of data acquisition and disease detection in vertical profile the diversion shafts in hydropower stations; We have designed a brand new device to acquire data and detect the disease of the shafts:a cylindrical unmanned airship adapted to the shafts environment is the floating loading platform,on which multi-sensors such as panoramic CCD camera,3D laser scanner,inertial measurement unit,barometric altimeter,illumination module and control module etc is integrated.Trial implemented inside the No.2,No.3 and No.4 shafts of Nuozhadu hydropower station from May 2017 to July,indicates that the detection device can be well applied for the overhaul of similar vertical profile.The detecting device fills the gap of 100-meter-level shaft detection field and presents a wide application prospect.