Abstract: In one or more embodiments, a method comprises receiving, at a processor, an input signal from an input device in response to a first actuation of the input device by a driver of an autonomous vehicle. The input device is a device disposed with the autonomous vehicle and has a second actuation of the input device associated with a standard operation of the input device. The second actuation has an actuation pattern different from an actuation pattern of the first actuation. In response to the input signal, a determination is made by the processor to determine whether the autonomous vehicle can perform a maneuver safely. In response to determining that the autonomous vehicle can perform the maneuver safely, a signal is sent by the processor to cause the autonomous vehicle to perform the maneuver.

Abstract: In some embodiments, a method comprises receiving, at a processor of an autonomous vehicle and from at least one sensor, sensor data distributed within a time window. A first event being a first event type occurring at a first time in the time window is identified by the processor using a software model based on the sensor data. At least one first attribute associated with the first event is extracted by the processor. A second event being the first event type occurring at a second time in the time window is identified by the processor based on the at least one first attribute. In response to determining that the second event is not yet recognized as being the first event type, a first label for the second event is generated by the processor.

Abstract: Methods, systems, and non-transitory computer readable media are configured to perform operations comprising determining an occurrence of a sign in an environment of a vehicle; determining that at least a portion of the sign is unrecognized by a local machine learning model of the vehicle; and providing sensor data associated with the at least one portion of the sign to an operations center remote from the vehicle.

Abstract: Methods, systems, and non-transitory computer-readable media are configured to perform operations comprising determining map data and detection data for an area in an environment; determining a change in the area based on the map data and the detection data; and generating control data based on the change.

Abstract: In one or more embodiments, a method comprises receiving, at a processor, an input signal from an input device in response to a first actuation of the input device by a driver of an autonomous vehicle. The input device is a device disposed with the autonomous vehicle and has a second actuation of the input device associated with a standard operation of the input device. The second actuation has an actuation pattern different from an actuation pattern of the first actuation. In response to the input signal, a determination is made by the processor to determine whether the autonomous vehicle can perform a maneuver safely. In response to determining that the autonomous vehicle can perform the maneuver safely, a signal is sent by the processor to cause the autonomous vehicle to perform the maneuver.

Abstract: Techniques are described for determining whether to process a job request. An example, method can include a device emitting a first pulse using a light detection and ranging (LIDAR) system coupled to an autonomous vehicle. The device can receive a first signal reflected off of an object. The device can emit a second pulse using the system, a threshold time interval being configured for the second laser pulse to hit the object in motion. The device can receive a second signal reflected off of the object. The device can determine a first time of flight information of the first signal and a second time of flight information of the second signal. The device can determine a velocity of the object based at least in part on the first time of flight information and the second time of flight information.

Abstract: In some embodiments, a method includes receiving a first image and a second image from a stereo camera pair. The method includes selecting a first row of pixels from the rectified image and a set of rows of pixels from the second image and comparing the first row of pixels with each row of pixels from the set of rows of pixels to determine disparity values. The method includes determining a pair of rows of pixels having the first row of pixels and a second row of pixels from the set of rows of pixels. The pair of rows of pixels has an offset no greater than an offset between the first row of pixels and each row of pixels from remaining rows of pixels. The method includes adjusting, based on the offset, the relative rotational position between the first stereo camera and the second stereo camera.

Abstract: In some embodiments, a method can include executing a first machine learning model to detect at least one lane in each image from a first set of images. The method can further include determining an estimate location of a vehicle for each image, based on localization data captured using at least one localization sensor disposed at the vehicle. The method can further include selecting lane geometry data for each image, from a map and based on the estimate location of the vehicle. The method can further include executing a localization model to generate a set of offset values for the first set of images based on the lane geometry data and the at least one lane in each image. The method can further include selecting a second set of images from the first set of images based on the set of offset values and a previously-determined offset threshold.

Abstract: In some embodiments, a method can include executing a first machine learning model to detect at least one lane in each image from a first set of images. The method can further include determining an estimate location of a vehicle for each image, based on localization data captured using at least one localization sensor disposed at the vehicle. The method can further include selecting lane geometry data for each image, from a map and based on the estimate location of the vehicle. The method can further include executing a localization model to generate a set of offset values for the first set of images based on the lane geometry data and the at least one lane in each image. The method can further include selecting a second set of images from the first set of images based on the set of offset values and a previously-determined offset threshold.

Abstract: In some embodiments, a method comprises receiving, at a processor of an autonomous vehicle and from at least one sensor, sensor data distributed within a time window. A first event being a first event type occurring at a first time in the time window is identified by the processor using a software model based on the sensor data. At least one first attribute associated with the first event is extracted by the processor. A second event being the first event type occurring at a second time in the time window is identified by the processor based on the at least one first attribute. In response to determining that the second event is not yet recognized as being the first event type, a first label for the second event is generated by the processor.

Abstract: In one or more embodiments, a method comprises receiving, at a processor, an input signal from an input device in response to a first actuation of the input device by a driver of an autonomous vehicle. The input device is a device disposed with the autonomous vehicle and has a second actuation of the input device associated with a standard operation of the input device. The second actuation has an actuation pattern different from an actuation pattern of the first actuation. In response to the input signal, a determination is made by the processor to determine whether the autonomous vehicle can perform a maneuver safely. In response to determining that the autonomous vehicle can perform the maneuver safely, a signal is sent by the processor to cause the autonomous vehicle to perform the maneuver.

Abstract: In some embodiments, a method includes receiving a first image and a second image from a stereo camera pair. The method includes selecting a first row of pixels from the rectified image and a set of rows of pixels from the second image and comparing the first row of pixels with each row of pixels from the set of rows of pixels to determine disparity values. The method includes determining a pair of rows of pixels having the first row of pixels and a second row of pixels from the set of rows of pixels. The pair of rows of pixels has an offset no greater than an offset between the first row of pixels and each row of pixels from remaining rows of pixels. The method includes adjusting, based on the offset, the relative rotational position between the first stereo camera and the second stereo camera.

Abstract: In some embodiments, a method comprises receiving, at a processor of an autonomous vehicle and from at least one sensor, sensor data distributed within a time window. A first event being a first event type occurring at a first time in the time window is identified by the processor using a software model based on the sensor data. At least one first attribute associated with the first event is extracted by the processor. A second event being the first event type occurring at a second time in the time window is identified by the processor based on the at least one first attribute. In response to determining that the second event is not yet recognized as being the first event type, a first label for the second event is generated by the processor.

Abstract: In some embodiments, a method comprises receiving, at a processor of an autonomous vehicle and from at least one sensor, sensor data distributed within a time window. A first event being a first event type occurring at a first time in the time window is identified by the processor using a software model based on the sensor data. At least one first attribute associated with the first event is extracted by the processor. A second event being the first event type occurring at a second time in the time window is identified by the processor based on the at least one first attribute. In response to determining that the second event is not yet recognized as being the first event type, a first label for the second event is generated by the processor.

Abstract: In some embodiments, a method can include executing a first machine learning model to detect at least one lane in each image from a first set of images. The method can further include determining an estimate location of a vehicle for each image, based on localization data captured using at least one localization sensor disposed at the vehicle. The method can further include selecting lane geometry data for each image, from a map and based on the estimate location of the vehicle. The method can further include executing a localization model to generate a set of offset values for the first set of images based on the lane geometry data and the at least one lane in each image. The method can further include selecting a second set of images from the first set of images based on the set of offset values and a previously-determined offset threshold.

Abstract: In one or more embodiments, a method comprises receiving, at a processor, sensor data from a sensor at a vehicle that is moving while in an autonomous mode. A position and an orientation of the vehicle based on the sensor data is determined at the processor. A lateral deviation of the vehicle from a planned path based on the position and the orientation of the vehicle while the vehicle is moving in the autonomous mode is calculated at the processor. In response to the lateral deviation exceeding a predefined lateral deviation threshold, the autonomous mode is disengaged.

Abstract: A non-transitory processor-readable medium stores code representing instructions to be executed by the processor. The code comprises code to cause the processor to receive a first image and a second image from a stereo camera pair disposed with a vehicle. The code causes the processor to detect, using a machine learning model, an object based on the first image, the object located within a pre-defined area within a vicinity of the vehicle. The code causes the processor to determine a distance between the object and the vehicle based on disparity between the first image and the second image. The code causes the processor to determine a longitudinal value of the vehicle based on the distance and a height of the vehicle. The code causes the processor to send an instruction to facilitate driving of the vehicle based on a road profile associated with the longitudinal value.

Abstract: An apparatus includes a processor configured to be disposed with a vehicle and a memory coupled to the processor. The memory stores instructions to cause the processor to receive, at least two of: radar data, camera data, lidar data, or sonar data. The sensor data is associated with a predefined region of a vicinity of the vehicle while the vehicle is traveling during a first time period. At least a portion of the vehicle is positioned within the predefined region during the first time period. The method also includes detecting that no other vehicle is present within the predefined region. An environment of the vehicle during the first time period is classified as one state from a set of states that includes at least one of dry, light rain, heavy rain, light snow, or heavy snow, based on at least two of the sensor data to produce an environment classification. An operational parameter of the vehicle based on the environment classification is modified.

Abstract: The following relates generally to defense mechanisms and security systems. Broadly, systems and methods are disclosed that detect an anomaly in an Embedded Mission Specific Device (EMSD). Disclosed approaches include a meta-material antenna configured to receive a radio frequency signal from the EMSD, and a central reader configured to receive a signal from the meta-material antenna. The central reader may be configured to: build a finite state machine model of the EMSD based on the signal received from the meta-material antenna; and detect if an anomaly exists in the EMSD based on the built finite state machine model.

Abstract: An autonomous vehicle can obtain sensor data. Upon determining that the autonomous vehicle is in a lane adjacent a shoulder, and there is an object in the shoulder, the autonomous vehicle can determine if performing a lane change maneuver out of the lane prior to the autonomous vehicle being positioned adjacent to the object is feasible. If it is, the lane change maneuver can be performed. If it is not, a nudge maneuver and/or a deceleration can be performed.