Ford Global Technolgoies, LLC

Siteco Gmbh

Dorian Jack Spero

Thomas Edward Pilutti

Peter Gyumyeong Joh

Matthew Y Rupp

Aaron Lewis Mills

Jeffrey Dan Rupp

Shane Elwart

Matt Y Rupp

Brian Wolski

Torsten Onasch

Systems, methods, and software to acquire light measurements in a targeted space using an autonomous vehicle, such as an aerial drone, are provided. Examples of targeted spaces include, but are not limited to, stadiums, arenas, racetracks, fields, parking lots, etc. Uses of such systems, software, and/or methods include, but are not limited to, verifying that required light intensity, distribution, camera image quality, and/or other performance metrics are met when commissioning, changing, checking, and approving lighting systems, among other things.

Light measurement using an autonomous vehicle

An autonomous control system for a vehicle that controls the speed and steering system of the vehicle to operate in a lane-keeping mode or a lane-changing mode. Position sensors sense the location of surrounding vehicles. A lane determining system identifies a current lane where the vehicle is located. A source of oncoming lane course data provides information as to the course of the current lane. A controller provides instructions to the steering system and speed control system to maneuver the vehicle in either the lane-keeping mode or the lane-changing mode. The driver may override the control system by providing a manual input to the steering system or the speed control system.

Autonomous lane control system

A vehicle includes a radar sensor for detecting objects in the vehicle path. The radar sensor may become blocked by contaminants or debris. A controller monitoring the radar system may detect a radar blockage when a return signal magnitude is less than a threshold. A typical response may be to set a radar blockage diagnostic under such a condition. The controller may inhibit setting a radar blockage diagnostic when other data indicates that the radar sensor is not blocked. Data such as vehicle position, traffic information, camera images, and historical radar returns may be used. A controller may confirm a radar blockage diagnostic when other data indicates that the radar sensor may be blocked. Data such as temperature, vehicle position, surface roughness, automated brake interventions, and heating system status may be used. Return signal dependent functions may be operated based on the radar blockage diagnostic.

Detecting radar blockage based on drive history

A vehicle may include a sensor configured to detect a rearward approaching object and at least one controller configured to cause the vehicle to accelerate in response to the sensor detecting a rearward approaching object while the vehicle is moving forward.

Vehicle and powertrain control based on rearward approaching object

A target (not-to-exceed) speed for a vehicle over a road segment ahead of the vehicle is established based on a desired relationship with a speed profile of the segment. The speed profile is generated by analyzing a statistical distribution of historical speed data over the segment collected by probe vehicles. A driver alert is activated if the vehicle is likely to exceed the target speed based on at least one measured vehicle dynamic property. The target speed may be established by identifying a baseline road segment over which the vehicle has previously travelled and which is similar to the approaching road segment, comparing a past speed of the vehicle over the baseline segment with a speed profile of the baseline segment to determine a speed differential, and applying the speed differential to the speed profile of the approaching segment.

System and method for vehicle speed monitoring using historical speed data

Apparatus for a vehicle operated on a roadway having lane markers includes an optical sensor providing optical data of the roadway. A first lane model is stored in an electronic memory in response to detected lane markers in the optical data. An electronic horizon system tracks a position of the vehicle and provides roadway data in response to the position. A second lane model is stored in the electronic memory in response to the roadway data. A confidence checker compares a discrepancy between the first and second lane models to a threshold in order to determine a confidence level. An output selector selects the first lane model when lane markers are detected in the optical data, and selects the second lane model if the lane markers are not detected in the optical data and the confidence level is greater than a predetermined level.

Lane monitoring with electronic horizon

A target (not-to-exceed) speed for a vehicle over a road segment ahead of the vehicle is established based on a desired relationship with a speed profile of the segment. The speed profile is generated by analyzing a statistical distribution of historical speed data over the segment collected by probe vehicles. A driver alert is activated if the vehicle is likely to exceed the target speed based on at least one measured vehicle dynamic property. The target speed may be established by identifying a baseline road segment over which the vehicle has previously traveled and which is similar to the approaching road segment, comparing a past speed of the vehicle over the baseline segment with a speed profile of the baseline segment to determine a speed differential, and applying the speed differential to the speed profile of the approaching segment.

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Reading, MA, United States of America

Dorian Jack Spero