Abstract: Aspects of the disclosure provide methods of modeling wrong-way driving of road users. For instance, log data including an observed trajectory of a first road user may be accessed. A first set of candidate lane segments for wrong-way driving may be identified from map information. A second set of candidate lane segments for not wrong-way driving may be identified from the map information. For each candidate lane segment in the first set and in the second set, a distance cost between the candidate lane segment and the observed trajectory may be determined. A candidate lane segment may be selected from at least one of the first set or the second set based on the determined distance costs. The selected candidate lane segment may be used to train a model to provide a likelihood of a second road user being engaged in wrong-way driving in a lane.

Abstract: A network system managing a network service can receive queries and service requests from multiple user devices. The network system can manage the network service such that multiple service requests can be fulfilled by a single service provider. In addition, the network system can dynamically compute parameters associated with the network service. The computation of the parameters can be based on pending service requests or anticipated demand for the network service.

Abstract: There is provided a method and apparatus of training a model, a device, and a medium, which relate to artificial intelligence, and in particular to a deep learning and image processing technology. The method may include: determining a plurality of augmented sample sets associated with a plurality of original samples; determining a first constraint according to a first model based on the plurality of augmented sample sets; determining a second constraint according to the first model and a second model based on the plurality of augmented sample sets, wherein the second constraint is associated with a difference between outputs of the first model and the second model for one augmented sample, and the first model has a complexity lower than that of the second model; training the first model based on at least the first constraint and the second constraint, so as to obtain a trained first model.

Abstract: A network system managing a network service can receive queries and service requests from multiple user devices. The network system can manage the network service such that multiple service requests can be fulfilled by a single service provider. In addition, the network system can dynamically compute parameters associated with the network service. The computation of the parameters can be based on pending service requests or anticipated demand for the network service.