Motional Ad LLC

Aptiv Technologies Limited

Delphi Technologies, Inc.

Junqing Wei

Wenda Xu

Jarrod M Snider

Junsung Kim

Gaurav Bhatia

Ludong Sun

Zachary Thomas Batts

Jong Ho Lee

Yifan Yang

Guchan Ozbilgin

Serge Lambermont

Yimu Wang

Eric Wolff

Hsun-Hsien Chang

Philipp Robbel

Maurilio Di Cicco

Matthias Sapuan

Noam Weinstein

Alok Sharma

Akshay Bhagat

Mauro Aguiar

Bishwamoy Sinha Roy

David Butterworth

Andrew J Eick

Adam M Paul

Jungsung Kim

Among other things, we describe systems and method for validating sensor calibration. For validating calibration of a system of sensors having several types of sensors, an object may be configured to have a substantially reflective portion such that the sensors can isolate the substantially reflective portion, and their sensor data can be compared to determine, if the detected locations of the substantially reflective portion by each sensor are aligned. For calibrating a system of sensors, an object having known calibration features can be used and detected by each sensor, and the detected data can be compared to known calibration data associated with the object to determine if each sensor is correctly calibrated.

Systems and methods for validating sensor calibration

An operation-security system for an automated vehicle includes an object-detector and a controller. The object-detector includes at least three sensors. Each sensor is one of a camera used to determine an image-location of an object proximate to a host-vehicle, a lidar-unit used to determine a lidar-location of the object proximate to the host-vehicle, and a radar-unit used to determine a radar-location of the object proximate to the host-vehicle. The controller is in communication with the at least three sensors. The controller is configured to determine a composite-location based on a comparison of locations indicated by the at least three sensors. Information from one sensor is ignored when a respective location indicated by the one sensor differs from the composite-location by greater than an error-threshold. If a remote sensor not on the host-vehicle is used, V2V or V2I communications may be used to communicate a location to the host-vehicle.

Operation-security system for an automated vehicle

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 detection system includes a first-sensor, a second-sensor, and a controller. The first-sensor is mounted on a host-vehicle. The first-sensor detects objects in a first-field-of-view. The second-sensor is positioned at a second-location different than the first-location. The second-sensor detects objects in a second-field-of-view that at least partially overlaps the first-field of view. The controller is in communication with the first-sensor and the second-sensor. The controller selects the second-sensor to detect an object-of-interest in accordance with a determination that an obstruction blocks a first-line-of-sight between the first-sensor and the object-of-interest.

Detection system and method

A steering-system for an automated vehicle is provided. The system includes an object-detector and a controller. The object-detector indicates a height and/or a width of an object approached by a host-vehicle. The controller is configured to steer the host-vehicle and is in communication with the object-detector. The controller steers the host-vehicle to straddle the object when the height of the object is less than a ground-clearance of the host-vehicle, and/or the width of the object is less than a track-width of the host-vehicle.

Object height determination for automated vehicle steering control system

A radar system for an automated vehicle includes a digital-map, a radar, and a controller. The digital-map indicates a characteristic of a roadway traveled by a host-vehicle. The radar detects objects proximate to the host-vehicle. The radar is equipped with a range-setting that is selectively variable. The controller is in communication with the digital-map and the radar. The controller is configured to select the range-setting of the radar based on the characteristic of the roadway. The characteristic may be based on speed-limit, road-shape (e.g. curve-radius), a horizon-distance, and/or an obstruction (e.g. hill, sign, or building). The radar may be equipped with a frame-rate-setting (i.e. pulse repetition frequency or PRF) that is selectively variable, and the controller may be further configured to select the frame-rate-setting based on the characteristic of the roadway.

Variable range and frame-rate radar operation for automated vehicle

A system for operating an autonomous vehicle includes a passenger-detector and a controller-circuit. The passenger-detector is operable to determine a passenger-count of passengers present in a host-vehicle. The controller-circuit is in communication with the passenger-detector and vehicle-controls of the host-vehicle. The controller-circuit is configured to operate the host-vehicle in an autonomous-mode and in accordance with a parameter. The parameter is set to an empty-value when passenger-count is equal to zero, and the parameter is set to an occupied-value different from the empty-value when the passenger count is greater than zero.

Autonomous vehicle operation based on passenger-count

A system for operating an automated vehicle in a crowd of pedestrians includes an object-detector, optionally, a signal detector, and a controller. The object-detector detects pedestrians proximate to a host-vehicle. The signal-detector detects a signal-state displayed by a traffic-signal that displays a stop-state that indicates when the host-vehicle should stop so the pedestrians can cross in front of the host-vehicle, and displays a go-state that indicates when the pedestrians should stop passing in front of the host-vehicle so that the host-vehicle can go forward. The controller is in control of movement of the host-vehicle and in communication with the object-detector and the signal-detector. The controller operates the host-vehicle to stop the host-vehicle when the stop-state is displayed, and operates the host-vehicle to creep-forward after a wait-interval after the traffic-signal changes to the go-state when the pedestrians fail to stop passing in front of the host-vehicle.

Automated vehicle control strategy for pedestrian crowds

A safe-to-proceed system () for operating an automated vehicle proximate to an intersection () includes an intersection-detector (), a vehicle-detector (), and a controller (). The intersection-detector () is suitable for use on a host-vehicle (). The intersection-detector () is used to determine when a host-vehicle () is proximate to an intersection (). The vehicle-detector () is also suitable for use on the host-vehicle (). The vehicle-detector () is used to estimate a stopping-distance () of an other-vehicle () approaching the intersection (). The controller () is in communication with the intersection-detector () and the vehicle-detector (). The controller () is configured to prevent the host-vehicle () from entering the intersection () when the stopping-distance () indicates that the other-vehicle () will enter the intersection () before stopping.

Safe-to-proceed 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 ().

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Bridgeville, PA, United States of America

Junqing Wei