Abstract: Upon determining that a vehicle is operating within an area, a stop time for the vehicle in the area is estimated based on operation data from an infrastructure element in the area. A vehicle sensor that is available to transition to a low power mode is identified based on the estimated stop time. Upon stopping the vehicle in the area, the available vehicle sensor is transitioned to the low power mode based on the estimated stop time being greater than a threshold.

Abstract: A system and method for operating a vehicle operating on a route that will pass within a predetermined distance of a smart infrastructure node (IX Node) includes: retrieving, prior to receiving a message from the IX Node, data about the IX Node including (a) an IX Node assist zone based on an effective broadcast range of the IX Node, (b) an IX Node field of view area that is within the IX Node assist zone and that is defined by a field of view of one or more IX Node sensors, and (c) available services from the IX Node; and selecting a vehicle application for operation on the route while in the IX Node assist zone based on the IX Node field of view area, and the available services from the IX Node, and/or a type and accuracy of one or more IX Node sensors.

Abstract: A three-dimensional grid model of a traffic scene can be determined based on grid elements. Weights can be determined for the grid elements of the three-dimensional grid model corresponding to priority regions and occluded grid elements. Grid coverage for respective stationary sensors can be determined based on the grid elements of the three-dimensional grid model. A matrix can be determined based on the grid coverage of the plurality of stationary sensors. An optimal subset of stationary sensors can be determined based on applying a greedy search algorithm to the matrix, the weights and costs corresponding to the plurality of stationary sensors to maximize the ratio of grid coverage to the cost based on poses of the plurality of stationary sensors.

Abstract: A set of first candidate topologies of first candidate roadside infrastructure nodes at respective mounting locations in a geographic area is randomly generated. For each of the first candidate topologies, first simulations, including detection of objects according to selected sensor parameters, installation parameters, and environment parameters for the candidate nodes at the respective mounting locations, are executed. First fitness scores are determined for each of the first candidate topologies by comparing results of the first simulations to ground truth data. Upon identifying one of the first fitness scores as exceeding a threshold, the candidate topology associated with the identified first fitness score is identified for deployment.

Abstract: A set of first candidate topologies of first candidate roadside infrastructure nodes at respective mounting locations in a geographic area is randomly generated. For each of the first candidate topologies, first simulations, including detection of objects according to selected sensor parameters, installation parameters, and environment parameters for the candidate nodes at the respective mounting locations, are executed. First fitness scores are determined for each of the first candidate topologies by comparing results of the first simulations to ground truth data. Upon identifying one of the first fitness scores as exceeding a threshold, the candidate topology associated with the identified first fitness score is identified for deployment.