Ford Global Technolgoies, LLC

Navid Tafaghodi Khajavi

Mohsen Bahrami

Amin Ariannezhad

Mohammad Nekoui

Hamed Asadi

Jeremy Lerner

Praveen Kumar Yalavarty

Scott Huggins

Xingping Chen

Mohammad Abouali

Taylor Hawley

Elnaz Tavakoli Yazdi

Danielle Rosenblatt

Ryan Jones

Jae Hyung Lim

Sajit Janardhanan

Mohammed-Javed Padinhakara

Nahid Pervez

A V2X event-message dictionary and a binning function are received from a cloud server. Sensor data is compared to events specified in the V2X event-message dictionary to identify a best-fit event for the sensor data. A number of bins and a bin number for the event are computed using the binning function. A V2X message is transmitted including the number of bins and the bin number, thereby avoiding including the sensor data in the V2X message.

Information aware V2X messaging

A system comprises a computer having a processor and a memory, the memory storing instructions executable by the processor to monitor a position of a vehicle while an adaptive cruise control feature of the vehicle is set to operate in a standard operation mode, identify, based on the position of the vehicle and on visibility zone information, a limited visibility zone in a route of travel of the vehicle, and cause the adaptive cruise control feature to operate in a modified operation mode while the vehicle is in the limited visibility zone, wherein during operation in the modified operation mode, the adaptive cruise control feature operates in accordance with one or more modified operating parameters.

Adaptive cruise control based on information about low visibility zones

Routes may be presented to a vehicle operator with information about predicted autonomous driving levels. A road network route to a destination is received. The road network route is partitioned into segments. Indicia of the segments are provided to an inference engine. The inference engine predicts levels of autonomous driving for the respective segments generated by the inference engine based on the indicia of the segments. Based on the predicted levels of autonomous driving for the respective segments, a ratio of a level of autonomous driving for the road network route is computed. The ratio corresponds to a proportion of time and/or distance for the road network route during which a driver-system is predicted to be engaged at the level of autonomous driving. A user interface is displayed, which includes a graphic representation of the road network route and a graphic indication of the ratio.

AI-based map for drive-assist system availability

Systems and methods for predicting an optimal use level of a driver assist system are provided. The system may collect historical driver behavior and road condition data, and train an optimization prediction model using the historical data, e.g., via machine learning or artificial intelligence. Moreover, the system may collect real-time driver behavior and road condition data, and predict an optimal use level of the driver assist system based on the real-time data using the trained optimization prediction model. The system may then send feedback to the driver assist system when the optimal use level falls outside a predetermined threshold, such that the driver assist system may be unavailable or have reduced functionality until the optimal use level improves.

Drive assist optimization systems and methods using artificial intelligence

A method includes identifying a broadcast set of access points from among the plurality of access points based on access point interference data associated with a plurality of access points disposed in an environment, where the access point interference data includes current radio frequency (RF) signal interference data associated with each of the plurality of access points, predicted RF signal interference data associated with each of the plurality of access points, or a combination thereof. The method includes partitioning a data packet into a plurality of data subpackets based on the broadcast set of access points and broadcasting the plurality of data subpackets to a vehicle via the broadcast set of access points.

Systems and methods for providing vehicle software updates

A system includes a server computer programmed upon determining that a first portion of software data for updating an operational feature of a first computer is stored in the first computer and a second portion of the software data is stored in a second computer, to encode the first portion and the second portion to generate encoded data, and to send the encoded data via wireless data transfer to the first and second computers. The first computer is programmed to decode the second portion from the received encoded data, to update the operational feature of the first computer based on the stored first portion and the decoded second portion, and to operate the first computer based on the updated operational feature.

Enhanced data provision in a digital network

System and methods for a decentralized hybrid air-ground autonomous last-mile goods delivery are disclosed herein. A drone can include a controller configured to cause the drone to depart from a docking station of a vehicle, transmit a discovery message to available vehicles in an operating area, the discovery message having drone metadata, select at least one of one of the available vehicles based on response codes received from the available vehicle, or a nearest fixed docking station, and dock with a docking station of the one of the available vehicles or the nearest fixed docking station.

Systems and methods for a decentralized hybrid air-ground autonomous last-mile goods delivery

While operating a vehicle, a candidate marker is detected via first image data from a first image sensor. Upon failing to identify the candidate marker, vehicle exterior lighting is actuated to illuminate the candidate marker. Then the candidate marker is determined to be one of a real marker or a projected marker based on determining whether the candidate marker is detected via second image data from the first image sensor. Upon determining the candidate marker is the real marker, the vehicle is operated based on the real marker.

Enhanced vehicle operation

Methods and systems are provided for a start/stop feature. In one example, a method includes adjusting start/stop conditions in response to a vehicle operator customizing start/stop conditions. The vehicle operator customizes start/stop conditions for a plurality of different driving conditions.

Methods and systems for start/stop

A computer is programmed to allocate respective connectivity quality data of a geographic area to a first map or a second map. The computer is further programmed to assign one of a plurality of subsets of the first map and one of a plurality of subsets of the second map to a first vehicle, identify respective locations of the first and second vehicles and one of the first or second maps that includes the locations of the first and second vehicles. The computer is further programmed to send, to the first and second vehicles, a map dataset that is a result of applying an XOR function to (1) the subset of the identified map that includes the location of the first vehicle assigned to the first vehicle and (2) the subset of the identified map that includes the location of the second vehicle assigned to the second vehicle.

A graph of devices is constructed, each network device serving an amount of bandwidth over the network, each vertex of the graph corresponding to a respective one of the network devices, each edge of the graph connecting network devices by interference weight, such that edges between connected network devices using different channels have an interference weight of zero, and edges between connected network devices using the same channel have an interference weight denoting an amount of interference between the connected network devices. A cell utilization of each of the network devices is determined according to an amount of traffic served by the respective network device compared to a total of the amounts of bandwidth for all network devices. A vehicle requesting bandwidth is assigned to the one of the network devices having a smallest product of cell utilization and maximum interference weight edge.

Interference-aware autonomous vehicle routing among CBSDs

A computer is programmed to divide respective data of a first high-resolution map of a first geographic area and a second high-resolution map of a second geographic area into a plurality of respective subsets, assign one of the subsets of each of the first high-resolution map and the second high-resolution map to a first vehicle and a second vehicle, identify respective locations of the first vehicle and the second vehicle and the one of the first high-resolution map or the second high-resolution map that includes the locations of the first and second vehicles, and send, to the first and second vehicles, a map dataset that is a result of applying an XOR function to (1) the subset of the identified high-resolution map including the location of the first vehicle and (2) the subset of the identified high-resolution map including the location of the second vehicle.

A computer includes a processor and a memory, the memory storing instructions executable by the processor to receive, from each of a plurality of vehicles, a request to exchange data, each request including metadata about the request; sort the vehicles into a plurality of priority sets determined from the metadata; determine a first set of data transfer lines available for vehicles in a first one of the priority sets; select a first optimal data transfer line from the first set of data transfer lines for a first one of the vehicles in the first priority set; and assign the first optimal data transfer line to the first one of the vehicles.

Vehicle data transfer queueing

A graph of devices is constructed, each device requiring an amount of bandwidth over the network, each vertex of the graph corresponding to a respective one of the devices, each edge of the graph connecting devices according to a weight denoting interference between the connected devices. The vertices of the graph are labeled with labels such that no two vertices sharing the same edge have the same label, each label requiring bandwidth corresponding to the device of that label requiring the most bandwidth. If the sum of the bandwidth required for the labels exceeds the set bandwidth, the edge of the graph having the least interference is deleted, and the perform the construct and label operations are repeated. If the sum of the bandwidth required for the labels is within the set bandwidth, the bandwidth is assigned to the devices.

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Troy, MI, United States of America

Navid Tafaghodi Khajavi