Beijing Didi Infinity Technology and Development Co., Ltd.

Didi Research America, LLC

Ishan Jindal

Zhiwei Qin

Xuewen Chen

Systems and methods are provided for estimating travel time and distance. Such method may comprise obtaining a vehicle trip dataset comprising an origin, a destination, a time-of-day, a trip time, and a trip distance associated with each of a plurality of trips, and training a neural network model with the vehicle trip dataset to obtain a trained model. The neural network model may comprise a first module and a second module, the first module may comprise a first number of neuron layers, the first module may be configured to obtain the origin and the destination as first inputs to estimate a travel distance, the second module may comprise a second number of neuron layers, and the second module may be configured to obtain the information of a last layer of the first module and the time-of-day as second inputs to estimate a travel time.

System and method for estimating travel time and distance

A method may comprise recursively performing: (1) providing one or more states of a simulation environment to a simulated vehicle, and the states comprise a first current time and a first current location of the simulated vehicle; (2) obtaining an action by the simulated vehicle when the simulated vehicle has reached a milestone, wherein: the action is selected from: waiting at the first current location of the simulated vehicle, picking up a passenger group A at an origin of passenger group A's transportation, and dropping off a passenger group B at a destination of passenger group B's transportation, and the milestone is an origin or a destination of any passenger group's transportation; (3) determining a reward to the simulated vehicle for the action; and (4) updating the one or more states based on the action to obtain one or more updated states for providing to the simulated vehicle.

Method and device for providing vehicle navigation simulation environment

A method for providing vehicle navigation simulation environment may comprise recursively performing steps (1)-(4) for a time period: (1) providing one or more states of a simulation environment to a simulated agent, wherein: the simulated agent comprises a simulated vehicle, and the states comprise a first current time and a first current location of the simulated vehicle; (2) obtaining an action by the simulated vehicle when the simulated vehicle has no passenger, wherein the action is selected from: waiting at the first current location of the simulated vehicle, and transporting M passenger groups; (3) determining a reward to the simulated vehicle for the action; and (4) updating the one or more states based on the action to obtain one or more updated states for providing to the simulated vehicle, wherein: the updated states comprise a second current time and a second current location of the simulated vehicle.

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Detroit, MI, United States of America

Ishan Jindal