Abstract: A method comprises periodically monitoring, by a processor, lateral position and velocity of vehicle within a predetermined distance from an autonomous vehicle, the vehicle moving in a direction having at least one common attribute with the autonomous vehicle; executing, by the processor, a computer model using the monitored lateral position and velocity of the vehicle, to predict whether a trajectory for the vehicle; and when a current trajectory of the autonomous has a likelihood of collision with the predicted trajectory of the vehicle that satisfies a threshold, determining, by the processor, an alternative trajectory for the autonomous vehicle.

Abstract: Embodiments include systems and methods for determining states of traffic lights and managing behavior of an automated vehicle approaching an intersection. An autonomy system applies an object recognition engine trained to recognize a traffic light, and identify and confirm the state of the traffic light. A first neural network trained for object detection recognizes a traffic light and defines a bounding box around the recognized traffic light. A second neural network receives the region of the image bounded by the box as constituting the traffic light and “reads” the light. The automated vehicle uses other information, such as states of pedestrian traffic lights, detection of objects in and near the intersection, and glare on one or more cameras, to supplement its determination of the right of way through the intersection. The autonomy system generates a driving instruction based on the traffic light combined.

Abstract: Systems and methods of determining distance bounds for adaptive cruise control. An autonomous vehicle system can identify a second vehicle on the roadway that is traveling in front of the autonomous vehicle; determine a speed of the second vehicle while the second vehicle travels along the roadway; generate a distance bound between the autonomous vehicle and the second vehicle based on the speed of the second vehicle and a speed of the autonomous vehicle; and control the speed of the autonomous vehicle to implement adaptive cruise control according to the distance bound.

Abstract: A vehicle comprises one or more sensors, and a processor coupled with the one or more sensors and stored inside a housing of the vehicle. The processor can be configured to collect data regarding the environment surrounding the vehicle from the one or more sensors; detect a second vehicle and an observed trajectory of the second vehicle from the collected data, the observed trajectory indicating a position or speed of the second vehicle over a time period; compare the observed trajectory with one or more expected trajectories of the second vehicle; responsive to determining a deviation between the observed trajectory and at least one of the one or more expected trajectories satisfies a condition, generate a record indicating the deviation and including a video of the second vehicle that corresponds to the observed trajectory; and transmit the record to a remote processor.

Abstract: Aspects of this technical solution can include identifying, by a processor coupled to non-transitory memory, a plurality of bounding boxes for one or more objects depicted in each image of a sequence of images captured during operation of an autonomous vehicle, allocating, by the processor and based on corresponding positions of the bounding boxes in each image and corresponding time stamps, one or more of the bounding boxes to one or more tracking identifiers each indicating trajectories of corresponding objects, generating, by the processor and based on the time stamps and the bounding boxes allocated to each of the tracking identifiers, one or more tracking images for each of the tracking identifiers, each of the tracking images including visual indications of the time stamps, and training, by the processor and based on the tracking images, an artificial intelligence model to output an indication of a type of trajectory.

Abstract: Aspects of this technical solution can include detecting, by a sensor of a vehicle in a physical environment via visible light, a first object in the physical environment, detecting, by the sensor via the visible light, a first feature a having a digital encoding and located at a surface of the first object, decoding, by a processor of the vehicle and based on the digital encoding, the first feature into a first indication of location corresponding to the first object, generating, by the processor of the vehicle during movement of the vehicle through the physical environment and based on the first indication of location, a location metric corresponding to the vehicle, and modifying, by the processor of the vehicle based on the location metric, operation of the vehicle to navigate the vehicle through the physical environment according to the location metric.

Abstract: Disclosed herein are systems, methods, and apparatuses for calibrating sensors of automated vehicles using calibration targets. A bracket holds the calibration targets and attaches to the automated vehicle proximate to a sensor being calibrated. For some sensors, such as GNSS antennas or similar device for receiving location data from a GNSS, a two-target bracket is fixed at the top of the automated vehicle, nearby the GNSS antenna. For IMUs or similar sensors, a three-target bracket is fixed at location of the automated vehicle proximate to the particular IMU, such as a passenger cabin or on the chassis of the automated vehicle. The calibration targets include a retroreflective surface that reflect signals, such as infrared signals, back to a theodolite (or total station). The theodolite or computer includes preprogrammed offset values indicating the relative positions of the sensor being calibrated and each of the calibration targets of the bracket.

Abstract: Disclosed herein are methods and systems (sensors) that allow revision of HD maps for lane lines as the lane lines are created. In a non-limiting example, a method comprises receiving, by a processor from a sensor associated with a vehicle configured to paint a lane line, a color attribute, a patterns attribute, and a location of a lane line painted on a road by the vehicle; determining, by the processor, using the location received from the sensor, whether the road has an existing lane line; and revising, by the processor, a high definition map associated with at least one autonomous vehicle by removing the existing lane line from the high definition map and inserting the lane line in the high definition map.

Abstract: Aspects of this technical solution can include a first layer having a first region and a second region, the first region and the second region having a first transmissivity property corresponding to a first portion of a spectrum of light and a second portion of the spectrum of light, having the first transmissivity property, and a second layer disposed over the first layer and having a third region and a fourth region, the third region and the fourth region having a second transmissivity property corresponding to the first portion of the spectrum of light and the second portion of the spectrum of light.

Abstract: Embodiments disclosed herein include systems and methods for generating proposed driving paths for an automated vehicle performing a lane-change. An autonomy system continually generates reference trajectories. The autonomy system then iteratively and recursively generates clothoid points tracing the reference trajectory, by the computer, but constrained by clothoid thresholds. The clothoid points define a clothoid representing a revised trajectory, effectively constrained by the clothoid thresholds. The autonomy system generates and updates driving instructions for the automated vehicle to follow a drive path represented by the clothoid. If the autonomy system determines clothoid points cannot be generated according to the thresholds, then the autonomy system determines the automated vehicle cannot safely or practicably perform the lane change maneuver for the given portion of road.

Abstract: Embodiments described herein systems and methods for detecting fluid leaks for an automated vehicle using image data. The automated vehicle includes a camera at or near the front of the chassis and a camera at or near the rear of the chassis. Each camera captures images of a road surface beneath vehicle as the vehicle passes a road surface. The cameras store images of the road surface into a log with timestamp or location stamp, by mapping the image to a location in the world. An algorithm de-warps the images to correct for lensing. Using the locations data, computer vision, and/or object recognition, a processor extracts features, overlays the images, and compares the features to identify differences between the images indicating dripping fluid. Multi-image comparisons avoid false positives. The processor may recognize that image features indicate fluid leaks based on characteristics of spots (e.g., color, location, size) or other features.

Abstract: Disclosed herein are methods and systems to provide intelligent display for vehicles including a method that comprises determining, by a processor, that a current trajectory of an autonomous vehicle includes a revised road condition having at least one attribute; retrieving, by the processor using the at least one attribute, a graphical overlay corresponding to the revised road condition; and displaying, by the processor on an electronic device associated with the autonomous vehicle, the graphical overlay while the electronic device is presenting an image of a surrounding of the autonomous vehicle.

Abstract: A method and vehicle for external actor acknowledgment for automated vehicles is disclosed. In an example, a method comprises determining, by a processor, to control a vehicle to switch into a lane adjacent to the vehicle; monitoring, by the processor, a speed or a location of a second vehicle, the second vehicle in the lane adjacent to the vehicle; determining, by the processor, the speed or the location of the second vehicle satisfies a condition; controlling, by the processor, the vehicle to switch into the lane adjacent to the vehicle; and activating, by the processor, an acknowledgment sequence responsive to determining the vehicle has switched into the lane.

Abstract: Disclosed herein are methods and systems to provide intelligent display for vehicles including a method that comprises determining data associated a road sign; retrieving driving data associated with a vehicle; generating, using the driving data and data associated with the road sign, a graphical overlay presenting a virtual object corresponding to the trajectory of the vehicle or data associated with the vehicle; and presenting on an electronic device associated with the vehicle, the graphical overlay while the electronic device is presenting an image of a surrounding of the vehicle, wherein the graphical overlay is superimposed upon an image of the road sign.

Abstract: A vehicle can include a first camera on a first side of the vehicle and a second camera on a second side of the vehicle opposite the first side of the vehicle. The vehicle can include a processor communicatively coupled with the first camera and the second camera. The processor can be configured to receive a first image of a first lane line on a road in which the vehicle is driving from the first camera and a second image of a second lane line on the road from the second camera; detect the first lane line from the first image and the second lane line from the second image; determine a first location of the first lane line relative to a defined location of the vehicle and a second location of the second lane line relative to the defined location of the vehicle; and execute a correction sequence.

Abstract: Disclosed herein are methods and systems to avoid collisions with animals. In an example, a method comprises receiving, by a processor, an indication that a current trajectory of an autonomous vehicle is associated with a likelihood of a collision that satisfies a collision threshold indicating a potential collision with an animal having an attribute that satisfies a threshold; disabling, by the processor, at least one lighting apparatus associated with the autonomous vehicle; and enabling, by the processor, a sound-generating device associated with the autonomous vehicle.

Abstract: A driving simulator can include one or more input devices; a display; and one or more processors communicatively coupled with the one or more input devices and the display. The one or more processors can be configured to store, in memory, a predetermined path for a simulated vehicle to travel in a simulated environment; execute an application to cause the simulated environment to appear on the display in a autonomous mode in which the application is configured to simulate the simulated vehicle driving by updating the display over time according to the predetermined path; receive an input from at least one of the one or more input devices; and responsive to receiving the input, change the mode of the application from the autonomous mode to a manual mode in which the application is configured to update the display according to inputs from the one or more input devices.

Abstract: A system can include a driving simulator. The driving simulator can include one or more input devices corresponding to controls of a vehicle; a display; and one or more processors communicatively coupled with the one or more input devices and the display. The one or more processors can be configured to simulate, on the display, an autonomous vehicle driving through a simulated environment in a manual mode based on inputs from the one or more input devices, automatically in an autonomous mode, and transition between manual mode and autonomous mode. The system can include a remote computing device. The remote computing device can be configured to receive an input from a user interface displayed at the remote computing device during a simulation of the autonomous vehicle in the autonomous mode, the input causing a fault in the operation of the autonomous vehicle in the autonomous mode.

Abstract: Embodiments described herein include a system of fluid-delivery hardware, including tubes, valves, and pumps that deliver samples of fluid gathered from various exposed locations of a vehicle to commonly located gas analysis sensors. A durable sensor housing contains the gas sensors and is situated in a confined location of the vehicle, which is environmentally isolated and physically partitioned from the sample locations exposed to potentially deleterious environments. For a given sample location, a tube and valves gather and channel fluid samples from the sample location towards the sensor housing at the confined location. A pump forces air outwards to purge debris in the tube or tube inlet, and draws air inward through the tube towards the sensor housing for delivery to the sensors.

Abstract: Embodiments described herein include an automated vehicle having a pitot tube mounted to a side-view mirror of the automated vehicle. The pitot gathers samples of wind speed information for calculating three-dimensional wind measurements using the information gathered by the pitot tube. The pitot tube is installed at an area that offers the pitot tube access to outside “clean air,” undisturbed by the vehicle's movement. A controller and/or driving software ingest the three-dimensional wind measurements and generate driving tasks of the automated vehicle based upon the three-dimensional wind measurements.