Abstract: A method comprises receiving a first set of data associated with a plurality of retroreflective features from a vehicle; retrieving a digital map comprising vectorized data associated with the plurality of retroreflective features and a location associated with the plurality of retroreflective features, the map previously generated based on monitoring a second set of data retrieved from a sensor of a second vehicle where the second set of data is associated with the plurality of retroreflective features near a road being driven by the second vehicle; generating a score for each retroreflective feature indicating a match between each retroreflective feature as indicated within the digital map and a location of each retroreflective feature as identified within the first set of data; and localizing the vehicle.

Abstract: Systems and methods of automatic correction of map data for autonomous vehicle navigation are disclosed. One or more servers can receive an indication of a traffic condition at an autonomous vehicle hub; upon receiving the indication of the traffic condition at the autonomous vehicle hub, identify an autonomous vehicle traveling a route that includes the autonomous vehicle hub; generate a control command for the autonomous vehicle based on the route and the traffic condition; and transmit the control command to the autonomous vehicle to correct the traffic condition at the autonomous vehicle hub.

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 herein include an autonomy system of an automated vehicle performing operations for detecting and avoiding lazy or unpredictable vehicles on a roadway. The autonomy system generates bounding boxes for tracking traffic vehicles recognized in sensor data. For close vehicles, the autonomy system generates expanded bounding boxes. The autonomy system determines the close vehicle is an encroaching vehicle by detecting that the expanded bounding box of the close vehicle overlapped the middle lane line or enters into the automated vehicle's current lane of travel. The autonomy system may simulate the behavior of the close vehicle by forward-propagating predicted positions of the close vehicle and expanded bounding box over some future time. In response to detecting the encroaching vehicle, the autonomy system may determine whether to perform an avoidance action or select a particular avoidance action, such as biasing away from the encroaching vehicle.

Abstract: An autonomous vehicle can include a tractor; a trailer coupled with the tractor; a thermal sensor coupled with the trailer, the thermal sensor configured to measure a temperature of the trailer; and one or more processors. The processors can be configured to receive a plurality of temperature measurements of the trailer from the thermal sensor while the autonomous vehicle is driving; compare the plurality of temperature measurements to a condition; and automatically execute a trailer anomaly response protocol responsive to determining at least one of the plurality of temperature measurements satisfies the condition.

Abstract: Systems and methods for generating safe driving paths in autonomous driving adversity conditions can include receiving, by a computer system including one or more processors, sensor data of a vehicle that is traveling. The sensor data can be indicative of one or more autonomous driving adversity conditions, and can include image data depicting surroundings of the vehicle. The method can include executing, by the computer system, a trained machine learning model to predict, using the sensor data, a trajectory to be followed by the vehicle through the one or more autonomous driving adversity conditions, and providing, by the computer system, an indication of the predicted trajectory to an autonomous driving system of the vehicle.

Abstract: An autonomous vehicle can include a perception sensor and one or more processors. The processors can be configured to automatically control the autonomous vehicle to an energy supply station to resupply the autonomous vehicle; detect an arrival of the autonomous vehicle at the energy supply station; detect a calibration target; collect data from the perception sensor corresponding with the calibration target; and calibrate the perception sensor based on the collected data.

Abstract: An autonomous vehicle can include a horn; a sensor(s) configured to capture images; and one or more processors. The one or more processors can be configured to receive a sequence of images from the sensor(s), the sequence of images captured by the sensor(s) as the autonomous vehicle was moving; execute a machine learning model using the sequence of images as input to detect a human inside a second vehicle or in the surrounding environment of the autonomous vehicle depicted within the sequence of images; determine, based on the detection of the human inside the second vehicle or in the surrounding environment of the autonomous vehicle within the sequence of images, the human is depicted performing a defined arm gesture within the sequence of images; and activate the horn responsive to the determination that the human is depicted performing the defined arm gesture within the sequence of images.

Abstract: A method comprises monitoring, by a processor, using a sensor of a first vehicle, data associated with a retroreflective feature near a road being driven by the first vehicle; vectorizing, by the processor, the data associated with the retroreflective feature; generating, by the processor, a digital map including vectorized data associated with the retroreflective feature and a location associated with the retroreflective feature; receiving, by the processor, data associated with the retroreflective feature from a second vehicle; and executing, by the processor, a localization protocol to identify a location of the second vehicle using the digital map.

Abstract: Systems and methos for learning safe driving paths in autonomous driving adversity conditions can include acquiring, by a computer system, vehicle sensor data for a plurality of driving events associated with one or more autonomous driving adversity conditions. The vehicle sensor data can include, for each driving event, corresponding image data depicting surroundings of the vehicle and corresponding inertial measurement data. The computer system can acquire, for each driving event of the plurality of driving events, corresponding vehicle positioning data indicative of a corresponding trajectory followed by the vehicle, and train, using the vehicle sensor data and the vehicle positioning data, a machine learning model to predict navigation trajectories during the autonomous driving adversity conditions.

Abstract: Aspects of this technical solution can include determining, by one or more processors of a liquid collection system and based on respective amounts of liquid detected by a plurality of sensors and a velocity of a vehicle, a direction of movement of the liquid. The plurality of sensors can each be associated with respective ones of a plurality of receptacles. The plurality of sensors can be configured to detect respective amounts of liquid collected by respective openings of each of the plurality of receptacles associated with corresponding directions of movement of a liquid. The plurality of receptacles can be configured to collect at least a portion of the liquid via the respective openings.

Abstract: An autonomous vehicle comprises one or more processors. The processors can be configured to receive, from a sensor of the autonomous vehicle, an image of an environment outside of the autonomous vehicle. The processors can detect potential unknown objects based on the image. The processors can compare the detection based on the image to a set of data points of a LiDAR scan to determine if there are unknown objects on a roadway.

Abstract: An autonomous vehicle can include a horn; a sensor(s) configured to capture images; and one or more processors. The one or more processors can be configured to receive a sequence of images from the sensor(s), the sequence of images captured by the sensor(s) as the autonomous vehicle was moving; execute a machine learning model using the sequence of images as input to detect a human inside a second vehicle or in the surrounding environment of the autonomous vehicle depicted within the sequence of images; determine, based on the detection of the human inside the second vehicle or in the surrounding environment of the autonomous vehicle within the sequence of images, the human is depicted performing a defined arm gesture within the sequence of images; and activate the horn responsive to the determination that the human is depicted performing the defined arm gesture within the sequence of images.

Abstract: Systems and methods of predicting a grade of a road upon which a vehicle is traveling are disclosed. An autonomous vehicle system can receive sensor data from a sensor measuring a response from at least one mechanical component of the autonomous vehicle as the autonomous vehicle navigates a road; detect a speed of the autonomous vehicle; determine a predicted grade of the road based on the sensor data and the speed; and navigate the autonomous vehicle based on the predicted grade of the road.

Abstract: Disclosed herein include systems and methods for calibrating perception sensors of an automated vehicle using stationary calibration targets and preconfigured geographic information for the calibration targets. A controller of the automated vehicle references map data indicating locations of each calibration target, which is then recorded by the automated vehicle's perception sensors. The automated vehicle is configured to use a corrected geographical position system (GPS) process (e.g., RTK, PPK) to determine positions and orientations of each sensor and/or the automated vehicle. The controller uses these values to generate accurate calibrations of the automated vehicle and the sensors.

Abstract: An autonomous vehicle comprises a sensor, a plurality of paint dispensers, and one or more processors. The processors can be configured to collect, using the sensor, data regarding an environment surrounding the autonomous vehicle as the autonomous vehicle is moving on a surface; detect an event in the environment surrounding the autonomous vehicle from the collected data; determine an event type of the event based on the collected data; select a paint dispenser from the plurality of paint dispensers based on the determined event type; and automatically dispense paint from the selected paint dispenser onto the surface.

Abstract: An autonomous vehicle comprises a sensor and one or more processors. The one or more processors can be configured to receive a set of object location data from the sensor, the set of object location data indicating an object; generate a measured representation of the object based on the set of object location data; execute a tracking protocol using data of the measured representation of the object as input to generate a virtual representation of the object, the virtual representation of the object comprising a virtual point that is nearest to a defined location of the autonomous vehicle; and translate the virtual point of the virtual representation of the object to a measured point of the measured representation of the object.

Abstract: An autonomous vehicle comprises a sensor and one or more processors. The processors can be configured to monitor, using the sensor, a surface of a road while the autonomous vehicle is driving on the road; detect a numerical identification of a tracking tag embedded underneath a surface of the road based on data collected from the sensor during the monitoring of the surface; query a database using the numerical identification of the tracking tag, the database comprising navigational information corresponding to different numerical identifications of tracking tags; identify first navigational information from the database that corresponds to the detected numerical identification of the tracking tag embedded underneath the surface of the road; determine a navigational action based on the first navigational information; and operate the autonomous vehicle according to the navigational action.

Abstract: Systems and methods of automatic correction of map data for autonomous vehicle navigation are disclosed. One or more processors of an autonomous vehicle can determine a surrogate trajectory for navigating the autonomous vehicle towards a target trajectory; generate a curvature target for the autonomous vehicle based on the surrogate trajectory, a lateral error, and a velocity of the autonomous vehicle, the curvature target defined in part by at least three points; and navigate the autonomous vehicle according to the curvature target.

Abstract: A method of providing information around road based events through autonomous vehicles. The method describes how a system can maintain or upload information around a digital map indicating locations of events detected. The system can react to updated events to affect route planning, biasing and lane selection. The system can transmit updated map information to various road users including other autonomous vehicles.