Abstract: An automatic parking system is provided. The automatic parking system includes a camera processor that acquires images around a subject vehicle, converts the acquired images into external images and synthesizes the external images. A sensor processor measured spaced distances between the subject vehicle and surrounding vehicles. A parking space recognizing unit periodically receives the spaced distances and the external images and comparing the consecutive external images with the spaced distances using an image recognition technology to recognize parking areas. A controller calculates a moving path between a current position of the subject vehicle and an optimal parking area and operates the subject vehicle based on the moving path.

Abstract: A distributed safety lockout system for a vehicle, such as a recreational vehicle (RV), that includes a plurality of networked devices communicatively coupled via a communications network, and that is configured to implement a method for controlling an electromechanical operation of an electromechanical device based on a safety lockout condition determined through use of a consensus protocol in which a safety lockout status is agreed upon by the networked devices. The safety lockout status may be escalated by a safety lockout condition detection device that detects a safety lockout condition, such as movement of the RV. The safety lockout status may be de-escalated through a network contention mode of the consensus protocol in which each of the networked devices agree to de-escalate the safety lockout status.

Abstract: A method of optimizing rider satisfaction includes classifying, using a first neural network of a hybrid neural network, social media data sourced from a plurality of social media sources as indicative of an effect on a transportation system. The method further includes predicting, using a second neural network of the hybrid neural network, at least one aspect of rider satisfaction affected by an effect on the transportation system derived from the social media data classified as indicative of an effect on the transportation system. The method still further includes optimizing, using a third neural network of the hybrid neural network, the at least one aspect of rider satisfaction for at least one rider occupying a vehicle in the transportation system.

Abstract: A method of transmitting signals from a first node having multiple transceivers to a second node is disclosed. The method comprises receiving a message from the second node at the first node, wherein the message is received by each of a plurality of transceivers of the first node; and transmitting by each of the plurality of transceivers a respective data frame to the second node in response to the message; wherein each transceiver initiates the transmission of its respective data frame a predetermined time period after receipt of the message by the transceiver; and wherein the transmissions of the data frames from the plurality of transceivers overlap. The data frames may form part of a two-way ranging exchange.

Abstract: A spatial infotainment rendering system may include at least one infotainment device configured to present information to a vehicle occupant traveling along a vehicle route at one of a plurality of infotainment locations within the vehicle and at least one spatial rendering processor. The processor may be configured to receive vehicle location and direction information of the vehicle along the vehicle route, receive event data indicative of an event having a potential to affect the vehicle route, and determine an event location based on event data. The processor may also be configured to select one of the infotainment locations based on the event location relative to the vehicle location and direction to present event information to the vehicle occupant relating to the event at a location spatially related to the event location relative to the vehicle route.

Abstract: In some implementations, a computing device can manage conflicts using conflict islands. For example, when a computing device receives bulk map data updates, the computing device can determine conflicts between point updates (e.g., small, ad hoc feature edits) in the current map data and the bulk map data updates. When the bulk map data updates are merged with the current map data, a point update for a map feature may conflict with the bulk update for the feature. The computing device can determine a perimeter of stable map features around the conflicted feature. The map features within the perimeter can correspond to a conflict island. The bulk updates for features within a conflict island (e.g., within the perimeter) can be deferred until resolved while bulk updates for features outside of a conflict island can be applied to the current map data.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for generating estimated times of arrival for multiple stops along a route. A route management system provides for: accessing a route tracking report associated with a vehicle to identify a set of stops along a route; determining a current location of the vehicle; computing, based on a current location of the vehicle, a first estimated time of arrival (ETA) associated with a first stop of the set of stops and a second ETA associated with a second stop of the set of stops; and generating, for display, first and second identifiers of the first and second stops associated with the first and second ETAs of the first and second stops.

Abstract: Methods, computer-readable media, software, and apparatuses may determine, based upon edge-computing operations, that a vehicular trip has been initiated and cause one or more sensors to collect vehicle data. One or more trip segments for at least a portion of the vehicular trip may be determined. In some aspects, for each trip segment, a first plurality of time features and a second plurality of frequency features may be determined, and may be concatenated with a third plurality of GPS features to form a feature vector. An accuracy measure may be determined based on the feature vector, and a mode for the vehicle may be predicted.

Abstract: Methods, systems, and non-transitory computer-readable media are configured to perform operations comprising: determining whether a first type of condition associated with an input is satisfied; based at least in part on satisfaction of the first type of condition, generating an estimated value of a parameter associated with operation of a vehicle; and controlling the vehicle based at least in part on the estimated value of the parameter.

Abstract: Disclosed are a neural network training method, a neural network training device and an electronic device. The neural network training method includes: training a first neural network to be trained by using sample data; determining an indicator parameter of the first neural network in a current training process; determining an update manner corresponding to a preset condition if the indicator parameter meets the preset condition; and updating a parameter of a batch normalization layer in the first neural network based on the update manner. In this way, sparsing of a feature map output by a neural network is implemented, thereby reducing an amount of data to be transmitted and improving computation speed of a chip.

Abstract: A system and method including receiving an indication of an occurrence of an event associated with the operation of an autonomous vehicle; determining at least one action to be performed, the at least one action including a data request for a specified subset of stored data associated with the operation of the autonomous vehicle from a memory; generating an output including the specified subset of data; and transmitting the specified subset of data to a remote monitoring system.

Abstract: A method, non-transitory computer readable medium, and system for receiving sensor data from one or more sensors disposed on an autonomous vehicle, determining whether a potentially dangerous event is detected in the environment around the autonomous vehicle, and providing automatically at least a portion of the sensor data to a user associated with the autonomous vehicle. The sensor data may comprise measurements associated with an environment around the autonomous vehicle. The determination of the potentially dangerous event may be based on the sensor data. The portion of the sensor data may be provided automatically in response to determining that the potentially dangerous event is detected.

Abstract: A vehicle includes an ADK attachable to and removable from a vehicle main body, the ADK issuing an instruction for autonomous driving, a VP including a plurality of functional units that perform a plurality of prescribed functions of the vehicle main body, and a VCIB that issues a control instruction to the functional units in accordance with an instruction from the ADK. One of the plurality of functional units is a steering system that steers the vehicle main body. The steering system specifies a limit value of a steering rate in accordance with a prescribed reference and transmits the specified limit value to the ADK through the VCIB. The ADK calculates a target steering angle to satisfy the limit value received from the steering system and transmits an instruction for the calculated steering angle to the steering system through the VCIB.

Abstract: Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.

Abstract: A method may include obtaining sensor data that include a plurality of sensor returns from an environment of an autonomous vehicle. A first set of features may be extracted from the sensor data. The first set of features may be processed with a machine learning model to generate, for at least a subset of the plurality of sensor returns, a first output that classifies a respective sensor return as corresponding to an object or non-object and a second output that indicates a property of the object. The sensor returns classified as corresponding to objects may be compared to a plurality of pre-classified objects to generate one or more generic object classifications. The autonomous vehicle may be controlled based at least in part on the one or more generic object classifications.

Abstract: A controller in a vehicle includes an obtaining unit that obtains facility information on a charging facility that can charge a power storage mounted on the vehicle, the facility information including position information and utility information in association with each other, the position information indicating a position of the charging facility, the utility information indicating an electric utility that manages the charging facility. The controller further includes at least one of a first notification unit and a second notification unit. The first notification unit causes a notification apparatus to give first information that indicates an electric utility, by using the facility information obtained by the obtaining unit. The second notification unit causes the notification apparatus to give second information that indicates a position of a charging facility, by using the facility information obtained by the obtaining unit.

Abstract: A machine learning method performs training of values of model parameters forming a machine learning model, and is performed in a machine learning system including a vehicle having the machine learning model and a server able to communicate with the vehicle. The machine learning method includes: detecting, by the server, a current processing load of the server; determining, by the server, processing amounts of training respectively performed by the server and the vehicle, based on the processing load; performing, by the server, training of the values of the model parameters in accordance with the determined processing amount in the server; and performing, by the vehicle, training of the values of the model parameters in accordance with the determined processing amount in the vehicle. The server decreases a processing amount at the server when the processing load is relatively high compared to when it is relatively low.

Abstract: Systems and methods for route management are disclosed. A system for use with a communication network includes an electronic logging device, a global positioning device, and a server. The server communicates via a communication network and to access vehicle data and global positioning data via the communication network. The server includes a processor(s) and a non-transitory computer-readable medium storing instructions thereon, that when executed by the one or more processors, cause the processor(s) to: analyze the vehicle data to identify start-and-stop events of the vehicle; analyze each of the start-and-stop events with respect to criteria to identify which of the start-and-stop events is associated with an actual visit event; identify the locations and the times from the global positioning data that correspond to the actual visit events; and correlate the locations and the times of the vehicle with the respective ones of the actual visit events.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for unmanned aerial vehicle authorization and geofence envelope determination. One of the methods includes determining, by an electronic system in an Unmanned Aerial Vehicle (UAV), an estimated fuel remaining in the UAV. An estimated fuel consumption of the UAV is determined. Estimated information associated with wind affecting the UAV is determined using information obtained from sensors included in the UAV. Estimated flights times remaining for a current path, and one or more alternative flight paths, are determined using the determined estimated fuel remaining, determined estimated fuel consumption, determined information associated wind, and information describing each flight path. In response to the electronic system determining that the estimated fuel remaining, after completion of the current flight path, would be below a first threshold, an alternative flight path is selected.

Abstract: Techniques are described for an autonomous vehicle to provide the autonomous vehicle's health-related information, identification information, and/or inspection information to a device that may be used by law enforcement or a government agency or a third-party so that the autonomous vehicle can comply with government regulations and can continue to operate on the road.