Accurate prediction of others' trajectories is essential for autonomous driving. Trajectory prediction is challenging because it requires reasoning about agents' past movements, social interactions among varying numbers and kinds of agents, constraints from the scene context, and the stochasticity of human behavior. Our approach models these interactions and constraints jointly within a novel Multi-Agent Tensor Fusion (MATF) network. Specifically, the model encodes multiple agents' past trajectories and the scene context into a Multi-Agent Tensor, then applies convolutional fusion to capture multiagent interactions while retaining the spatial structure of agents and the scene context. The model decodes recurrently to multiple agents' future trajectories, using adversarial loss to learn stochastic predictions. Experiments on both highway driving and pedestrian crowd datasets show that the model achieves state-ofthe-art prediction accuracy.

Transportable energy storage systems (TESSs) have great potential to enhance resilience of distribution systems (DSs) against large area blackouts. A joint post-disaster restoration scheme for TESS and generation scheduling in microgrids (MGs) and network reconfigurations is proposed to minimize the total system cost, including customer interruption cost, generation cost, and TESS related costs. A temporal-spatial TESS model which is related to both transportation networks and DSs is proposed to represent the difference between TESS and ESS in terms of flexibility and cost reduction of ESS sharing among MGs. The proposed restoration problem is formulated as a mixed-integer linear programming with considering various network and TESS constraints. The proposed model and scheme are tested in a modified 33-bus test system with three MGs and four TESSs. The results verify that a distribution system with TESS is more resilient compared with conventional ESS because of the benefit from total cost reduction.

Microgrid (MG) represents one of major drives of adopting Internet of Things for smart cities, as it effectively integrates various distributed energy resources. Indeed, MGs can be connected with each other and presented as a system of multimicrogrid (MMG). This paper proposes the optimal operation of MMGs by a cooperative energy and reserve scheduling model, in which energy and reserve can be cooperatively utilized among MMGs. In addition, values of Shapely are introduced to allocate economic benefits of the cooperative operation. Finally, a case study based on a system of MMGs is conducted, and simulation results verify the effectiveness of the proposed cooperative scheduling model.

in scenario s, otherwise set to 0. P t,s G,i , Q t,s G,i Real/reactive power generation at bus i in interval t in scenario s. P t,s r,i , Q t,s r,i Load restoration at bus i in interval t in scenario s. P t,s ij , Q t,s ij Real/reactive power of branch (i, j) in scenario s. v t,s i Voltage magnitude at bus i in interval t in scenario s. P t,s G,m , Q t,s G,m Aggregated active/reactive power in microgrid m in scenario s. P t,s DG,m , Q t,s DG,m Active/reactive power generation of equivalent dispatchable DG in microgrid m in scenario s. P t,s d,m and Q t,s d,m Active/reactive load in microgrid m in scenario s. E t,s DG,m Energy of equivalent dispatchable DG by the end of interval t in scenario s.

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