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: Systems, methods, and devices for predicting driver intent and future movements of a human driven vehicles are disclosed herein. A system for predicting future movements of a vehicle includes a camera system, a boundary component, a body language component, and a prediction component. The camera system is configured to capture an image of a vehicle. The boundary component is configured to identify a sub-portion of the image corresponding to an area where a driver of a vehicle is located. The body language component configured to detect a driver's body language. The prediction component configured to predict future motion of the vehicle based on the driver's body language detected by the body language component.

Abstract: The present invention extends to methods, systems, and computer program products for distinguishing lane markings for a vehicle to follow. Automated driving or driving assist vehicles utilize sensors to help the vehicle navigate on roadways or in parking areas. The sensors can utilize, for example, the painted surface markings to help guide the vehicle on its path. Aspects of the invention use a first type of sensor and at least a second different type of sensor to identify road surface markings. When ambiguity is detected between road surface markings, decision making algorithms identify the correct set of markings for a vehicle to abide by. The sensors also identify the location and trajectory of neighboring vehicles to increase confidence with respect to the identified road-surface markings.

Abstract: Systems, methods, and devices for detecting a vehicle's turn signal status for collision avoidance during lane-switching maneuvers or otherwise. A method includes detecting, at a first vehicle, a presence of a second vehicle in an adjacent lane. The method includes identifying, in an image of the second vehicle, a sub-portion containing a turn signal indicator of the second vehicle. The method includes processing the sub-portion of the image to determine a state of the turn signal indicator. The method also includes notifying a driver or performing a driving maneuver, at the first vehicle, based on the state of the turn signal indicator.

Abstract: The present invention extends to methods, systems, and computer program products for distinguishing lane markings for a vehicle to follow. Automated driving or driving assist vehicles utilize sensors to help the vehicle navigate on roadways or in parking areas. The sensors can utilize, for example, the painted surface markings to help guide the vehicle on its path. Aspects of the invention use a first type of sensor and at least a second different type of sensor to identify road surface markings. When ambiguity is detected between road surface markings, decision making algorithms identify the correct set of markings for a vehicle to abide by. The sensors also identify the location and trajectory of neighboring vehicles to increase confidence with respect to the identified road-surface markings.

Abstract: Systems, methods, and devices for predicting a driver's intention and future movements of a proximal vehicle, whether an automated vehicle or a human driven vehicle, are disclosed herein. A system for predicting future movements of a vehicle includes an intersection component, a camera system, a boundary component, and a prediction component. The intersection component is configured to determine that a parent vehicle is near an intersection. The camera system is configured to capture an image of the proximal vehicle. The boundary component is configured to identify a sub-portion of the image containing a turn signal indicator on the proximal vehicle. The prediction component is configured to predict future movement of the proximal vehicle through the intersection based on a state of the turn signal indicator.

Abstract: Systems, methods, and devices for predicting driver intent and future movements of a human driven vehicles are disclosed herein. A system for predicting future movements of a vehicle includes a camera system, a boundary component, a body language component, and a prediction component. The camera system is configured to capture an image of a vehicle. The boundary component is configured to identify a sub-portion of the image corresponding to an area where a driver of a vehicle is located. The body language component configured to detect a driver's body language. The prediction component configured to predict future motion of the vehicle based on the driver's body language detected by the body language component.

Abstract: Methods and apparatus pertaining to a testbed for lane boundary detection in a virtual driving environment are provided. A method may involve generating, by a processor, a virtual driving environment comprising one or more driving lanes, a virtual vehicle, and one or more virtual sensors mounted on the virtual vehicle configured to generate simulated data as the virtual vehicle traverses within the virtual environment. The method may also involve executing an algorithm to process the simulated data to detect the one or more driving lanes. The method may further involve recording an output of the algorithm. The method may additionally involve annotating the simulated data with the output of the algorithm.

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 controller for an autonomous vehicle receives an image stream from one or more imaging devices. The controller identifies vehicle images in the image stream. Vehicle images are compared to the color, shape, badging, markings, license plate, and driver of the autonomous vehicle. If the vehicle image is determined to match the autonomous vehicle, then the vehicle image is ignored as a potential obstacle. The location of a reflective surface that generated the vehicle image may be determined and added to a set of potential obstacles. The color and shape of a vehicle in a vehicle image may be evaluated first. Only if the color and shape in the vehicle image match the autonomous vehicle are other factors such as badging, markings, license plate, and driver considered. Vehicle images not matching the autonomous vehicle are included in a set of potential obstacles.

Abstract: A controller for an autonomous vehicle receives audio signals from one or more microphones and identifies sounds. The controller further identifies an estimated location of the sound origin and the type of sound, i.e. whether the sound is a vehicle and/or the type of vehicle. The controller analyzes map data and attempts to identify a landmark within a tolerance from the estimated location. If a landmark is found corresponding to the estimated location and type of the sound origin, then the certainty is increased that the source of the sound is at that location and is that type of sound source. Collision avoidance is then performed with respect to the location of the sound origin and its type with the certainty as augmented using the map data. Collision avoidance may include automatically actuating brake, steering, and accelerator actuators in order to avoid the location of the sound origin.