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      Subject
      We present a novel traffic trajectory editing method which uses spatio-temporal keyframes to control vehicles during the simulation to generate desired traffic trajectories. By taking self-motivation, path following and collision avoidance into account, the proposed force-based traffic simulation framework updates vehicle's motions in both the Frenet coordinates and the Cartesian coordinates. With the way-points from users, lane-level navigation can be generated by reference path planning. With a given keyframe, the coarse-to-fine optimization is proposed to efficiently generate the plausible trajectory which can satisfy the spatio-temporal constraints. At first, a directed state-time graph constructed along the reference path is used to search for a coarse-grained trajectory by mapping the keyframe as the goal. Then, using the information extracted from the coarse trajectory as initialization, adjoint-based optimization is applied to generate a finer trajectory with smooth motions based on our force-based simulation. We validate our method with extensive experiments. [1]
      we present a prototype PVA tool called INSIGHTSFEED for exploring Twitter data at scale. As validation [1]
      Wearable [1]
      wearable [1]
      Wearable computing [1]
      wearable devices [1]
      Weather Forecast Analysis [1]
      Weather Visualization [1]
      Web [17]
      web [2]
      WEB 2.0 [1]
      Web 3D [1]
      Web applications [3]
      Web art and design [1]
      web based solution [1]
      Web interaction [1]
      web lessons [1]
      web logs [2]
      web map services [1]
      web platform [1]