Techniques relating to monitoring map consistency are described. In an example, a monitoring component associated with a vehicle can receive sensor data associated with an environment in which the vehicle is positioned. The monitoring component can generate, based at least in part on the sensor data, an estimated map of the environment, wherein the estimated map is encoded with policy information for driving within the environment. The monitoring component can then compare first information associated with a stored map of the environment with second information associated with the estimated map to determine whether the estimated map and the stored map are consistent. Component(s) associated with the vehicle can then control the object based at least in part on results of the comparing.

This disclosure describes methods and techniques for estimating lanes for a vehicle. The methods and techniques include determining a first preliminary estimate of lanes based on a plurality of lane markings at a location of the vehicle, determining a second preliminary estimate of lanes based on a plurality of trails of objects at the location of the vehicle, comparing the first preliminary estimate of lanes and the second preliminary estimate of lanes, and determining a final estimate of lanes at the location of the vehicle based on the comparing.

A system for identifying redundant road lane detections in map data and subsequently updating the map data to remove the redundant road lane detections is provided. The system may be configured to determine, based on sensor data, a plurality of road lane detections associated with a road link represented by the map data. The system is further configured to determine a cluster for the road link based on a clustering criterion. The system is further configured to establish a plurality of road lane detection groups based on connectivity of the road lane detections in the cluster. The plurality of road lane detection groups is evaluated to identify one or more redundant road lane detections based on one or more of a parallel-detection criterion or a heading difference criterion. The identified redundant road lane detections are used to update the map data by a computer-implemented update process.

An evaluation computing system may implement techniques to validate a vehicle controller, such as based on a detection of a systematic fault. The evaluation computing system may access data (e.g., log data and/or map data) associated with an operation of the vehicle in an environment as controlled by the controller. The evaluation computing system may modify a portion of the map data representative of a simulated change associated with a portion of the environment. The evaluation computing system may run a simulation with a simulated environment, generated based on the modified map data, to determine whether the controller detects and/or mitigates the simulated change in a sufficient manner. Based on a determination of whether or not the controller detects and/or mitigates the simulated change in a sufficient manner, the evaluation computing system may determine an error associated with the controller or may validate the controller.

The present disclosure provides systems and methods for tracking and scoring robot or machine performance. The robot or machine performance may comprise a metric that can be computed based on operational data for the robot or machine.

Systems including one or more sensors, coupled to a vehicle, may detect sensor information and provide the sensor information to another computing device for processing. A system includes one or more sensors, coupled to a vehicle and configured to detect sensor information, and a computing device configured to communicate with one or more mobile sensors to receive the mobile sensor information, communicate with the one or more sensors to receive the sensor information, and analyze the sensor information and the mobile sensor information to identify one or more risk factors.

A system includes a first vehicle computer programmed to monitor first vehicle sensor data as the first vehicle traverses a route; identify a location of interest along the route based on a topographic feature of the location of interest determined from the first vehicle sensor data; and store a selected subset of the first vehicle sensor data for the location of interest in a set of collected data about the route. The system further includes a second computer programmed to receive the collected data about the route; and based on the collected data about the route, generate a set of guidance data including one or more recommended operating parameters for the location of interest, whereby the recommending operating parameters are available to a third computer for a second vehicle.

System, methods, and other embodiments described herein relate to predicting trajectories of multiple vehicles using graphs and multiple decoding models. In one embodiment, a method includes computing, using an encoding model, a graph having a geographic map and vehicle features associated with a plurality of vehicles in an area according to characteristics, prior trajectories, and spatiotemporal interactions. The method also includes processing, using the encoding model, updates for the geographic map and the vehicle features separately in association with encoded features of neighboring vehicles. The method also includes decoding, using a probability model and a regression model, the geographic map and the vehicle features to output estimated trajectories for the plurality of vehicles.

Provided are a method for constructing a three-dimensional map in a high-definition map, a device and a storage medium. The method includes: acquiring vehicle trajectory data, where the vehicle trajectory data is three-dimensional trajectory data; matching the vehicle trajectory data with a lane in a two-dimensional map and determining vehicle trajectory data matched with the lane; and assigning a height to the lane in the two-dimensional map according to the matched vehicle trajectory data to obtain a three-dimensional map.

Techniques are described for using computing devices to perform automated operations for determining the acquisition location of an image using an analysis of the image's visual contents. In at least some situations, images to be analyzed include panorama images acquired at acquisition locations in an interior of a multi-room building, and the determined acquisition location information includes a location on a floor plan of the building and in some cases orientation direction information—in at least some such situations, the acquisition location determination is performed without having or using information from any distance-measuring devices about distances from an image's acquisition location to objects in the surrounding building. The acquisition location information may be used in various automated manners, including for controlling navigation of devices (e.g., autonomous vehicles), for display on one or more client devices in corresponding graphical user interfaces, etc.