A vehicle behavior monitor installed in a host vehicle that monitors the behavior of other vehicles. When the behavior of the other vehicles appears to deviate from an expected trajectory of an in-vehicle map, a geo-fenced region is monitored by external sensors. The external sensor data stream is input to a pre-trained anomaly detector. The clusters from the feature space of the encoder are compared to a database of known behaviors. A confidence level is determined based on the number of vehicles which exhibit the behavior. If the confidence level is equal to or greater than a confidence level threshold, a persistence value is calculate based on the type of behavior. The behavior and the persistence value are used to update the in-vehicle map. Based on the persistence value, the update is transmitted to a map server when the host vehicle enters an area of high data connectivity or is dropped.

Disclosed herein is a technique for generating and providing an indication to an autonomous vehicle regarding the confidence level for the accuracy or quality of the map data in which the indication is determined from observation data received from other vehicles.

Embodiments of this application provide a map data collection method and apparatus, and a system, to report map data in a targeted manner. The method includes: receiving a first instruction from a network side device, where the first instruction instructs a map data reporting manner to a first vehicle, the first instruction includes confidence information, and the confidence information indicates confidence that map data reported by the first vehicle; and sending the map data to the network side device in the map data reporting manner instructed by the first instruction, where confidence of the map data is not lower than the confidence indicated by the confidence information.

Methods, apparatus and systems for map reconstruction based on wireless tracking are described. In one example, a described system comprises: a sensor configured to collect sensing data in a venue and obtain a plurality of trajectories, and a processor. Each trajectory is a time series of spatial coordinates (TSSC) representing a path traversed by a respective object in the venue. Each TSSC is accompanied by at least one respective time series of sensing data (TSSD) collected while the respective object traverses the path in the venue. The processor is configured for: segmenting each TSSC and its accompanying at least one TSSD into segments, bundling the plurality of trajectories based on similarity measures between pairs of the segments, fusing the bundled trajectories to generate fused trajectories, computing a shape of the fused trajectories, and generating a map of the venue based on the computed shape.

Aspects of the subject disclosure may include, for example, receiving, over a network from a plurality of mobile devices via a plurality of installed SDKs, sensor data captured by one or more sensors of the plurality of mobile devices, where the sensor data includes geomagnetic data captured within a particular building; providing, over the network, the sensor data to a geomagnetic mapping server to enable generation of a geomagnetic footprint for the particular building that is aggregated with indoor mapping data for the particular building and stored as a map in a mapping repository; and providing, over the network, the map of the particular building to a communication device for presentation at the communication device along with real-time locations of first responders in the particular building, where the real-time locations are determined according to real-time sensor data including real-time geomagnetic data captured by sensors of the first responders. Other embodiments are disclosed.

A navigational system for a host vehicle may comprise at least one processing device. The processing device may be programmed to receive a first output and a second output associated with the host vehicle and identify a representation of a target object in the first output. The processing device may determine whether a characteristic of the target object triggers a navigational constraint by verifying the identification of the target object based on the first output and, if the at least one navigational constraint is not verified based on the first output, then verifying the identification of the target object based on a combination of the first output and the second output. In response to the verification, the processing device may cause at least one navigational change to the host vehicle.

A server obtains a first model for a first area and first operational data associated with the first area. The server determines a second model, based on the first operational data and the first model, and obtains a first performance parameter indicative of a performance of the first model. The server obtains a second performance parameter indicative of a performance of the second model and determines a model performance parameter based on the first and second performance parameters. The server determines whether the model performance parameter satisfies a first criterion. The server, when the model performance parameter does not satisfy the first criterion, determines whether the second performance parameter satisfies a second criterion. The server, when the second performance parameter does not satisfy the second criterion, determines a second area that is smaller than the first area; and obtains a third model for the second area.

An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data—and ultimately a fused high definition (HD) map—that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.

An apparatus for updating map information for a vehicle includes a vehicle information detecting device that detects information of a surrounding vehicle which accompanies a vehicle, when the vehicle travels through an intersection, a line analyzing device that analyzes line information based on information of the surrounding vehicle which accompanies the vehicle, a reliability determining device that determines reliability of the line information, and a controller that extracts a change point on a map based on the reliability and update map information based on the change point.

The present invention relates to the efficient use of both local and remote computational resources and communication bandwidth to provide distributed environment mapping using a plurality of mobile sensor-equipped devices. According to a first aspect, there is provided a method of determining a global position of one or more landmarks on a global map, the method comprising the steps of determining one or more differences between sequential sensor data captured by one or more moving devices; determining one or more relative localisation landmark positions with respect to the one or more moving devices; determining relative device poses based one or more differences between sequential sensor data relative to the one or more relative localisation landmark positions; and determining a correlation between each device pose and the one or more relative localisation landmarks positions.