Abstract: Systems, methods, and devices for predicting driver intent and future movements of a human driven vehicles are disclosed herein. A computer implemented method includes receiving an image of a proximal vehicle in a region near a vehicle. The method includes determining a region of the image that contains a driver of the proximal vehicle, wherein determining the region comprises determining based on a location of one or more windows of the proximal vehicle. The method includes processing image data only in the region of the image that contains the driver of the proximal vehicle to detect a driver's body language.

Abstract: Example metal bridge detection systems and methods are described. In one implementation, a method receives LIDAR data from a LIDAR system mounted to a vehicle and receives camera data from a camera system mounted to the vehicle. The method analyzes the received LIDAR data and the camera data to identify a metal bridge proximate the vehicle. If a metal bridge is identified, the method adjusts vehicle operations to improve vehicle control as it drives across the metal bridge.

Abstract: A vehicle includes one or more laterally mounted microphones and a controller programmed to detect a signature of an unoccupied position adjacent the vehicle in outputs of the microphones. The signature may be identified using a machine learning algorithm. In response to detecting an unoccupied position, the controller may invoke autonomous parking, store the location of the unoccupied position for later use, and/or report the unoccupied position to a server, which then informs other vehicles of the available parking. The unoccupied position may be verified by evaluating whether map data indicates legal parking at that location. The unoccupied position may also be confirmed with one or more other sensors, such as a camera, LIDAR, RADAR, SONAR, or other type of sensor.

Abstract: Example sinkhole detection systems and methods are described. In one implementation, a method receives data from multiple sensors mounted to a vehicle and analyzes the received data to identify a sinkhole in a roadway ahead of the vehicle. If a sinkhole is identified, the method adjusts vehicle operations and reports the sinkhole to a shared database and/or another vehicle.

Abstract: A vehicle controller receives images from a camera upon arrival and upon departure. A location of the vehicle may be tracked and images captured by the camera may be tagged with a location. A departure image may be compared to an arrival image captured closest to the same location as the arrival image. A residual image based on a difference between the arrival and departure images is evaluated for anomalies. Attributes of the anomaly such as texture, color, and the like are determined and the anomaly is classified based on the attributes. If the classification indicates an automotive fluid, then an alert is generated.

Abstract: A method for detecting stubs or intersecting roadways includes receiving perception data from at least two sensors. The at least two sensors include a rear facing camera of a vehicle and another sensor. The perception data includes information for a current roadway on which the vehicle is located. The method includes detecting, based on the perception data, an intersecting roadway connecting with the current roadway. The method also includes storing an indication of a location and a direction of the intersecting roadway with respect to the current roadway.

Abstract: The disclosure relates generally to methods, systems, and apparatuses for automated or assisted driving and more particularly relates to identification, localization, and navigation with respect to bollard receivers. A method for detecting bollard receivers includes receiving perception data from one or more perception sensors of a vehicle. The method includes determining, based on the perception data, a location of one or more bollard receivers in relation to a body of the vehicle. The method also includes providing an indication of the location of the one or more bollard receivers to one or more of a driver and component or system that makes driving maneuver decisions.

Abstract: A vehicle controller receives images from a camera upon arrival and upon departure. A location of the vehicle may be tracked and images captured by the camera may be tagged with a location. A departure image may be compared to an arrival image captured closest to the same location as the arrival image. A residual image based on a difference between the arrival and departure images is evaluated for anomalies. Attributes of the anomaly such as texture, color, and the like are determined and the anomaly is classified based on the attributes. If the classification indicates an automotive fluid, then an alert is generated. A machine learning algorithm for generating classifications from image data may be trained using arrival and departure images obtained by rendering of a three-dimensional model or by adding simulated fluid leaks to two-dimensional images.

Abstract: The disclosure relates generally to methods, systems, and apparatuses for automated or assisted driving and more particularly relates to identification, localization, and navigation with respect to bollard receivers. A method for detecting bollard receivers includes receiving perception data from one or more perception sensors of a vehicle. The method includes determining, based on the perception data, a location of one or more bollard receivers in relation to a body of the vehicle. The method also includes providing an indication of the location of the one or more bollard receivers to one or more of a driver and component or system that makes driving maneuver decisions.

Abstract: A method and an apparatus pertaining to generating training data. The method may include executing a simulation process. The simulation process may include traversing one or more virtual sensors over a virtual driving environment defining a plurality of lane markings or virtual objects that are each sensible by the one or more virtual sensors. During the traversing, each of the one or more virtual sensors may be moved with respect to the virtual driving environment as dictated by a vehicle-dynamic model modeling motion of a vehicle driving on a virtual road surface of the virtual driving environment while carrying the one or more virtual sensors. Virtual sensor data characterizing the virtual driving environment may be recorded. The virtual sensor data may correspond to what an actual sensor would produce in a real-world environment that is similar or substantially matching the virtual driving environment.

Abstract: A method for improving the accuracy with which a road profile ahead of a vehicle may be determined. The method may include receiving a plurality of inputs corresponding to a plurality of on-board sensors corresponding to a vehicle. An on-board computer system may estimate motion of the vehicle. The on-board computer system may correct data corresponding to a forward-looking sensor of the plurality of on-board sensors by accounting for the motion of the vehicle. Accordingly, the on-board computer system may use the corrected data to produce more accurate information characterizing the driving environment ahead of the vehicle. This more accurate information may be used to better estimate the motion of the vehicle in the future as the vehicle encounters that driving environment, which may improve the corrections that may be applied to the data corresponding to the forward-looking sensor at that time.

Abstract: A scenario is defined that including models of vehicles and a typical driving environment. A model of a subject vehicle is added to the scenario and sensor locations are defined on the subject vehicle. Perception of the scenario by sensors at the sensor locations is simulated to obtain simulated sensor outputs. The simulated sensor outputs are annotated to indicate the location of obstacles in the scenario. The annotated sensor outputs may then be used to validate a statistical model or to train a machine learning model. The simulates sensor outputs may be modeled with sufficient detail to include sensor noise or may include artificially added noise to simulate real world conditions.

Abstract: A method for generating training data. The method may include executing a simulation process. The simulation process may include traversing one or more virtual sensors over a virtual road surface defining a plurality of virtual anomalies that are each sensible by the one or more virtual sensors. During the traversing, each of the one or more virtual sensors may be moved with respect to the virtual road surface as dictated by a vehicle-motion model modeling motion of a vehicle driving on the virtual road surface while carrying the one or more virtual sensors. Virtual sensor data characterizing the virtual road surface may be recorded. The virtual sensor data may correspond to what a real sensor would have output had it sensed the road surface in the real world.

Abstract: A method for improving the safety and comfort of a vehicle driving over a railroad track, cattle guard, or the like. The method may include receiving, by a computer system, one or more inputs corresponding to one or more forward looking sensors. The computer system may also receive data characterizing a motion of the vehicle. The computer system may estimate, based on the one or more inputs and the data, a motion of a vehicle with respect to a railroad track, cattle guard, or the like extending across a road ahead of the vehicle. Accordingly, the computer system may change a suspension setting, steering setting, or the like of the vehicle to more safely or comfortably drive over the railroad track, cattle guard, or the like.

Abstract: Example ice and snow detection systems and methods are described. In one implementation, a method activates an ice and snow detection system in response to receiving weather data indicating a likelihood of ice or snow on a roadway near a vehicle. The method receives data from multiple vehicle sensors and analyzes the received data to identify ice or snow on the roadway. If ice or snow is identified on the roadway, the method adjusts vehicle operations and reports the ice or snow condition to a shared database.

Abstract: A method and an apparatus pertaining to generating training data. The method may include executing a simulation process. The simulation process may include traversing one or more virtual sensors over a virtual driving environment defining a plurality of lane markings or virtual objects that are each sensible by the one or more virtual sensors. During the traversing, each of the one or more virtual sensors may be moved with respect to the virtual driving environment as dictated by a vehicle-dynamic model modeling motion of a vehicle driving on a virtual road surface of the virtual driving environment while carrying the one or more virtual sensors. Virtual sensor data characterizing the virtual driving environment may be recorded. The virtual sensor data may correspond to what an actual sensor would produce in a real-world environment that is similar or substantially matching the virtual driving environment.

Abstract: Methods and systems for generating virtual sensor data for developing or testing computer vision detection algorithms are described. A system and a method may involve generating a virtual environment. The system and the method may also involve positioning a virtual sensor at a first location in the virtual environment. The system and the method may also involve recording data characterizing the virtual environment, the data corresponding to information generated by the virtual sensor sensing the virtual environment. The system and the method may further involves annotating the data with a depth map characterizing a spatial relationship between the virtual sensor and the virtual environment.

Abstract: A driving assistance system includes a drive detection component, a presence component, and a notification component. The drive detection component is configured to determine that a vehicle or driver is exiting or preparing to exit a parking location. The presence component is configured to determine, from a drive history database, whether a parking barrier is present in front of or behind the parking location. The notification component is configured to provide an indication that the parking barrier is present to a human driver or an automated driving system of the vehicle.

Abstract: Techniques and implementations pertaining to detection of moving objects, such as pedestrians, when a vehicle moves in a rearward direction are described. A method may involve identifying a region of interest when a vehicle moves in a rearward direction. The method may involve detecting a moving object in the region of interest. The method may also involve determining whether a collision with the moving object by the vehicle moving in the rearward direction is likely. The method may further involve providing a human-perceivable signal responsive to a determination that the collision is likely.

Abstract: The disclosure relates to methods, systems, and apparatuses for autonomous driving vehicles or driving assistance systems and more particularly relates to vehicle radar perception and location. The vehicle driving system disclosed may include a storage media, a radar system, a location component and a driver controller. The storage media stores a map of roadways. The radar system is configured to generate perception information from a region near the vehicle. The location component is configured to determine a location of the vehicle on the map based on the radar perception information and other navigation related data. The drive controller is configured to control driving of the vehicle based on the map and the determined location.