Motional Ad LLC

Delphi Technologies, Inc.

Aptiv Technologies Limited

Jarrod M Snider

Junqing Wei

Wenda Xu

Ludong Sun

Zachary Thomas Batts

Yimu Wang

Guchan Ozbilgin

Yifan Yang

Junsung Kim

Jong Ho Lee

A sensor data fusion system for a vehicle with multiple sensors includes a first-sensor, a second-sensor, and a controller-circuit. The first-sensor is configured to output a first-frame of data and a subsequent-frame of data indicative of objects present in a first-field-of-view. The first-frame is characterized by a first-time-stamp, the subsequent-frame of data characterized by a subsequent-time-stamp different from the first-time-stamp. The second-sensor is configured to output a second-frame of data indicative of objects present in a second-field-of-view that overlaps the first-field-of-view. The second-frame is characterized by a second-time-stamp temporally located between the first-time-stamp and the subsequent-time-stamp. The controller-circuit is configured to synthesize an interpolated-frame from the first-frame and the subsequent-frame. The interpolated-frame is characterized by an interpolated-time-stamp that corresponds to the second-time-stamp. The controller-circuit fuses the interpolated-frame with the second-frame to provide a fused-frame of data characterized by the interpolated-time-stamp, and operates the host-vehicle in accordance with the fused-frame.

Data fusion system for a vehicle equipped with unsynchronized perception sensors

An operating system for an automated vehicle includes a failure-detector and a controller. The failure-detector detects a component-failure on a host-vehicle. Examples of the component-failure include a flat-tire and engine trouble that reduces engine-power. The controller operates the host-vehicle based on a dynamic-model. The dynamic-model is varied based on the component-failure detected by the failure-detector.

Adaptive dynamic model for automated vehicle

A scenario aware perception system () suitable for use on an automated vehicle includes a traffic-scenario detector (), an object-detection device (), and a controller (). The traffic-scenario detector () is used to detect a present-scenario () experienced by a host-vehicle (). The object-detection device () is used to detect an object () proximate to the host-vehicle (). The controller () is in communication with the traffic-scenario detector () and the object-detection device (). The controller () configured to determine a preferred-algorithm () used to identify the object (). The preferred-algorithm () is determined based on the present-scenario ().

Scenario aware perception system for an automated vehicle

A driving-rule system () suitable to operate an automated includes a vehicle-detector () and a controller (). The vehicle-detector () is suitable for use on a host-vehicle (). The vehicle-detector () is used to detect movement of an other-vehicle () proximate to the host-vehicle (). The controller () is in communication with the vehicle-detector (). The controller () is configured to operate the host-vehicle () in accordance with a driving-rule (), detect an observed-deviation () of the driving-rule () by the other-vehicle (), and modify the driving-rule () based on the observed-deviation ().

Automated vehicle operating system with driving-rule modification based on observation of another vehicle

A vehicle perception sensor adjustment system includes a perception-sensor, a digital-map, and controller-circuit. The perception-sensor is configured to detect an object proximate to a host-vehicle. The perception-sensor is characterized as having a field-of-view that is adjustable. The digital-map indicates a contour of a roadway traveled by the host-vehicle. The controller-circuit in communication with the perception-sensor and the digital-map. The controller-circuit determines the field-of-view of the perception-sensor in accordance with the contour of the roadway indicated by the digital-map, and outputs a control-signal to the perception-sensor that adjusts the field-of-view of the perception-sensor.

Vehicle sensor configuration based on map data

A tire-wear detection system for an automated vehicle includes a steering-angle-sensor, a vehicle-path-detector, and a controller. The steering-angle-sensor indicates a steering-angle of a host-vehicle. The vehicle-path-detector indicates a turning-radius of the host-vehicle. The controller is in communication with the steering-angle-sensor and the vehicle-path-detector. The controller determines a wear-status of a tire of the host-vehicle based on the turning-radius and the steering-angle.

Tire wear detection system for automated vehicle

A brake control system for operating brakes of an automated vehicle at slow speed includes a motion-detector and a controller. The motion-detector detects relative-movement of a host-vehicle relative to a stationary-feature located apart from the host-vehicle. The controller is configured to operate brakes of the host-vehicle. The controller determines a vehicle-speed of the host-vehicle based on the relative-movement when the vehicle-speed is less than a speed-threshold, and regulates brake-pressure of the brakes based on the vehicle-speed.

Slow speed automated vehicle brake pressure control system

A crosswalk navigation system for operating an automated vehicle in an intersection includes an intersection-detector, a pedestrian-detector, and a controller. The intersection-detector is suitable for use on a host-vehicle. The intersection-detector is used to determine when the host-vehicle is proximate to an intersection and determine when the intersection includes a cross-walk. The pedestrian-detector is suitable for use on the host-vehicle. The pedestrian-detector is used to determine a motion-vector of a pedestrian relative to the cross-walk. The controller is in communication with the intersection-detector and the pedestrian-detector. The controller is configured to determine a travel-path of the host-vehicle through the intersection, determine when the pedestrian will pass through an intersect-location where the travel-path intersects the cross-walk based on the motion-vector, and operate the host-vehicle to enter the intersection before the pedestrian passes through the intersect-location and to arrive at the intersect-location after the pedestrian passes through the intersect-location.

Intersection cross-walk navigation system for automated vehicles

An escape-path-planning system to operate an automated vehicle includes an object-detector and a controller. The object-detector is suitable for use on a host-vehicle. The object-detector is used to detect an other-vehicle in an adjacent-lane next to a present-lane traveled by the host-vehicle. The controller is in communication with the object-detector. The controller is configured to, in response to a lane-change-request, determine a first-route-plan that steers the host-vehicle from the present-lane to the adjacent-lane, determine a second-route-plan that steers the host-vehicle into the present-lane, initiate the first-route-plan when a forecasted-distance between the other-vehicle and the host-vehicle is greater than a distance-threshold, and cancel the first-route-plan and select the second-route-plan when the forecasted-distance between the other-vehicle and the host-vehicle becomes less than the distance-threshold after the first-route-plan is initiated. The second-route-plan is a pre-planned escape-path that is instantly available if needed that provides a smoother travel-experience for an occupant of the host-vehicle.

Escape-path-planning system for an automated vehicle

A scenario aware perception system suitable for use on an automated vehicle includes a traffic-scenario detector, an object-detection device, and a controller. The traffic-scenario detector is used to detect a present-scenario experienced by a host-vehicle. The object-detection device is used to detect an object proximate to the host-vehicle. The controller is in communication with the traffic-scenario detector and the object-detection device. The controller configured to determine a preferred-algorithm used to identify the object. The preferred-algorithm is determined based on the present-scenario.

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Pittsburgh, PA, United States of America

Jarrod M Snider