An autonomous vehicle (AV) computing device including at least one processor may be provided. The at least processor may be programmed to (i) receive a proposed trip including a destination location and a departure time, (ii) determine environmental conditions data based on the destination location and the departure time, (iii) retrieve current software ecosystem data for the AV, (iv) retrieve aggregated data for a plurality of AVs, the aggregated data including a plurality of correlations, each correlation including a) an interaction between at least one software application and at least one environmental condition and b) an adverse performance outcome associated with the interaction, (v) compare the environmental conditions data for the proposed trip and the current software ecosystem data for the AV to the plurality of correlations to identify an adverse performance outcome, and (vi) execute a remedial action to avoid the adverse performance outcome.

A system and method for implementing a neural network based vehicle dynamics model are disclosed. A particular embodiment includes: training a machine learning system with a training dataset corresponding to a desired autonomous vehicle simulation environment; receiving vehicle control command data and vehicle status data, the vehicle control command data not including vehicle component types or characteristics of a specific vehicle; by use of the trained machine learning system, the vehicle control command data, and vehicle status data, generating simulated vehicle dynamics data including predicted vehicle acceleration data; providing the simulated vehicle dynamics data to an autonomous vehicle simulation system implementing the autonomous vehicle simulation environment; and using data produced by the autonomous vehicle simulation system to modify the vehicle status data for a subsequent iteration.

Embodiments described herein include a method of receiving, by a moving assisting vehicle, a calibration assistance request related to a moving ego vehicle that requested assistance in collaborative calibration of a sensor deployed on the moving ego vehicle. The method further includes analyzing the calibration assistance request to extract at least one of a schedule or an assistance route associated with the requested assistance. The method includes communicating with the moving ego vehicle about a desired location relative to the position of the moving ego vehicle for the moving assisting vehicle to be in order to assist the sensor to acquire information of a target present on the moving assisting vehicle. The method includes facilitating to drive the moving assisting vehicle to reach the desired location to achieve the collaborative calibration of the sensor on the moving ego vehicle.

Methods and systems for autonomous and semi-autonomous vehicle control, routing, and automatic feature adjustment are disclosed. Sensors associated with autonomous operation features may be utilized to search an area for missing persons, stolen vehicles, or similar persons or items of interest. Sensor data associated with the features may be automatically collected and analyzed to passively search for missing persons or vehicles without vehicle operator involvement. Search criteria may be determined by a remote server and communicated to a plurality of vehicles within a search area. In response to which, sensor data may be collected and analyzed by the vehicles. When sensor data generated by a vehicle matches the search criteria, the vehicle may communicate the information to the remote server.

The present disclosure relates to systems and methods for aligning navigation information from a plurality of vehicles. In one implementation, at least one processing device may receive first navigational information from a first vehicle and second navigational information from a second vehicle. The first and second navigational information may be associated with a common road segment. The processor may divide the common road segment into a first road section and a second road section that join at a common point. The processor may then align the first and second navigational information by rotating at least a portion of the first navigational information or the second navigational information relative to the common point. The processor may store the aligned navigational information in association with the common road segment and send the aligned navigational information to vehicles for use in navigating along the common road segment.

A control apparatus that controls a plurality of vehicles performing platoon travelling is provided. Each of the vehicles is provided with an inter-vehicle communication apparatus that performs an inter-vehicle communication with an other vehicle included in a column of the platoon travelling. At least some of the vehicles is provided with an external communication apparatus that performs an external communication based on own identification information given by a telecommunication company. The control apparatus is configured to control respective vehicles such that the number of vehicles capable of simultaneously performing the external communication is less than the number of vehicles included in the column.

A method for estimating a position of a robot in a local area of a large space and a robot and a cloud server that implement such method are provided. The robot includes a local area classifier configured to identify a local area of a plurality of local areas of the space in which the robot moves and a plurality of position estimators configured to estimate the position of the robot in the local area.

A method, a communication device and a computer program product for navigating to a location with communications to a wireless network. The method includes detecting, via a processor of a communication device, a loss of a first communication signal from a wireless network and receiving signal loss location data corresponding to a first location where the first communication signal from the wireless network was lost. The method further includes retrieving, from a memory of the communication device, first navigation data corresponding to a first route between a starting location and an end location. The method further includes generating and presenting, on an output device of the communication device, second navigation data based on the first navigation data and the signal loss location data. The second navigation data corresponds to a second route between a current location and the first location where communications to the wireless network are available.

The present application provides a method for detecting map error information, an apparatus, a device, a vehicle and a storage medium, where the method includes: acquiring current environmental feature information around a vehicle; and detecting, according to the current environmental feature information and map data, whether the map data is erroneous. Whether the map data is erroneous is detected in real time based on the current environmental feature information during the process of the actual traveling of the vehicle, which improves timeliness and efficiency for the map error detection, thereby the map data can be corrected in time and the traveling safety of the vehicle may be improved.

Systems and methods for mutual discovery in autonomous rideshare between passengers and vehicles may receive a pick-up request to pick-up a user with an autonomous vehicle and interact with the user to perform an operation associated with the autonomous vehicle and/or update a user profile associated with the user.