Ford Global Technolgoies, LLC

Nitendra Nath

Aaron Lewis Mills

Michael Hafner

Benjamin Maus

Akshat Rajvanshi

Zhao Wang

Bahgat Aboushaban

Ali Hassani

Daniel A Makled

Hongtei Eric Tseng

Steven Yellin Schondorf

Michael Mcquillen

Amey Y Karnik

Jeremy Lerner

Patrick Lawrence Jackson Van Hoecke

Taylor Hawley

Danielle Rosenblatt

Scott Huggins

Xingping Chen

Andrew Arthur Brown

Andrea Bowes Chowanic

Timothy D Zwicky

Jason Joseph Woo

Alexandra Taylor

Vladimir Ivanovic

Pavithra Madhavan

Shadi Jammoul

Brian Tamm

Jens Kotte

Ryan Sorsby

Md Nahid Pervez

Andreas Giersiefer

Justin Teems

Kevin Wegienka

Nicolas Salciccioli

Natalie Arguello

Shalabh Rakesh Bhatnagar

Jonathan Mcneely

Jigarkumar Patel

Ryan Danko

At least one object is detected, via a sensor, in a direction of movement ahead of a host vehicle. A current sensor range is determined based on the detected at least one object. A maximum sensor range is determined based on the sensor. The current sensor range is compared with the maximum sensor range. Upon determining the current sensor range is less than the maximum sensor range by at least a predetermined threshold, driving parameters of the host vehicle are updated based on the current sensor range.

Enhanced vehicle operation

While operating a host vehicle in a first lane on a road, respective first statistical probabilities of a) an average speed of a plurality of vehicles, including the host vehicle, operating in the first lane, b) an average distance between the vehicles operating in the first lane, and c) an average number of braking events performed by the vehicles operating in the first lane are determined based on sensor data. A high traffic density probability for the road is determined based on the first statistical probabilities. A host vehicle assist feature is transitioned between an enabled state and a disabled state based on the high traffic density probability for the road being greater than a threshold probability.

Vehicle assist feature control

A current lane of vehicle operation is determined to be branched at a location into a through lane and a deceleration lane based on first sensor data indicating an increased width of the current lane exceeds a predetermined width at the location. Then the vehicle is determined to be operating in one of (a) the deceleration lane, or (b) the through lane, based on second sensor data. Then one of (a) an assist feature of the vehicle is activated to a disabled state based on determining the vehicle is in the deceleration lane, or (b) the assist feature is maintained in an enabled state based on determining the vehicle is in the through lane.

Vehicle lane-based control

A system includes a processor and a memory storing instructions executable by the processor to control at least one of a steering system or a propulsion system to operate a vehicle at a speed below a speed threshold. The instructions include instructions to determine whether one or more second vehicles a first distance from the vehicle are traveling below the speed threshold. The instructions include instructions to, upon determining the second vehicles are traveling below the speed threshold, continue to control the steering system or the propulsion system. The instructions include instructions to, upon determining the second vehicles are not traveling below the speed threshold, transition control of the steering system or the propulsion system to a human operator of the vehicle.

Vehicle speed and steering control

A vehicle control system includes a computer including a processor and a memory storing instruction executable by the processor to, in response to detecting hands being off a steering wheel for a threshold time while a lane-centering assist operation is active, steer a vehicle including the steering wheel from a center of a lane to a lateral position between the center of the lane and an edge of the lane; then steer the vehicle from the lateral position to the center of the lane; and then maintain the vehicle at the center of the lane.

Lane-centering assistance

A computer in a vehicle includes first and second electronic control units (ECUs). The first and second ECUs are programmed to monitor, respectively, a first operating condition and a second operating condition. Each operating condition includes one of path deviation, lane width, user awareness, or steering torque. The second ECU is programmed to monitor the second operating condition according to a protocol with a security measure. The first or second ECU is further programmed to control vehicle operation based on the first or second operating condition.

A computer includes a processor and a memory, the memory storing instructions executable by the processor to identify an error between a predicted steerable path of a vehicle based on data collected according to a first protocol and a predicted lane path based on data collected according to a second protocol and to identify a path fault when the error exceeds an error threshold for an elapsed time exceeding a time threshold.

Vehicle path processing

A system, comprising a computer that includes a processor and a memory, the memory storing instructions executable by the processor to estimate path coefficients based on a difference between a vehicle location and a vehicle path. Estimating the path coefficients can include minimizing the difference while controlling path coefficient gain to maintain occupant comfort and safety. A vehicle can be operated based on the estimated path coefficients.

A lane curvature, a vehicle position offset relative to a lane center line, and a vehicle heading are determined based on image data received from a vehicle camera sensor. An adjusted lane curvature and an adjusted vehicle heading are computed based on a vehicle speed, a vehicle yaw rate and the position offset.

Vehicle lateral motion control

A determination that a driver of a vehicle is directed to a non on-road zone based on at least one of a head pose or an eye gaze of a driver. Based on the non on-road zone, a probability is determined that the driver does not transition from the non on-road zone to an on-road zone based on the non on-road zone. The probability is adjusted when the driver is directed to the non on-road zone for a time period that exceeds a predetermined time threshold. A warning is generated within the vehicle based on the adjusted probability.

Driver awareness detection system

A system, comprising a processor and a memory, the memory including instructions to be executed by the processor to pilot a vehicle based on determining first and second lane markers, where lane markers are mathematical descriptions of roadway lane markers applied to a roadway to mark traffic lanes, determine a missing first or second lane marker, and pilot the vehicle for a determined period of time based on a remaining first or second lane marker.

In-vehicle traffic assist

A vehicle computer includes a memory and a processor programmed to execute instructions stored in the memory. The instructions include determining a first lane center, autonomously operating a host vehicle relative to the first lane center, detecting a change in the first lane center to a second lane center, selecting a filter, and applying the filter while transitioning autonomous operation of the host vehicle from the first lane center to the second lane center.

Lane centering disturbance mitigation

Method and apparatus are disclosed for monitoring and adjustment of gaps between vehicles. An example vehicle includes a rear sensing device and a controller. The controller is to determine a target lead gap for following a lead vehicle during adaptive cruise control and measure, via the rear sensing device, a trailing distance to a trailing vehicle. The controller also is to determine a target trailing gap for the trailing vehicle. The example vehicle also includes a cruise control unit to increase, responsive to the trailing distance being less than the target trailing gap, the target lead gap based on the trailing distance.

Monitoring and adjustment of gaps between vehicles

A computing system can determine a probability of precipitation based on Bayesian inference conditioned on probabilities associated with vehicle wiper status, vehicle jerk, vehicle sway, and vehicle lateral offset. The computing system can disable a vehicle lane assist system based on the probability of precipitation and operate a vehicle with the disabled vehicle lane assist system.

Driver assist

Methods and systems are disclosed for vehicle cruise control smoothness adaptation. An example vehicle includes a GPS receiver for receiving expected road incline data, a camera for determining a half lane width position, and a radar for determining two respective leading vehicle angles of arrival. The vehicle also includes a processor for determining an actual road incline by filtering the expected road incline, half lane width position, and leading vehicle angles of arrival. And the processor is further for modifying a cruise control system based on the actual road incline.

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Troy, MI, United States of America

Nitendra Nath