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: Systems, methods and apparatuses are disclosed for assessing whether a vehicle will make contact with an obstacle. The systems, methods, and apparatuses may include an obstacle sensing component configured to determine a location and a dimension of an obstacle, a vehicle sensing component configured to determine a height of a point of the vehicle relative to the ground, and a notification component configured to provide an indication of a presence of the obstacle to assist a human driver or an automated driving system in parking the vehicle without making contact with the obstacle.

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: A method for testing the performance of one or more anomaly-detection algorithms. The method may include obtaining sensor data output by a virtual sensor modeling the behavior of an image sensor. The sensor data may correspond to a time when the virtual sensor was sensing a virtual anomaly defined within a virtual road surface. One or more algorithms may be applied to the sensor data to produce at least one perceived dimension of the virtual anomaly. Thereafter, the performance of the one or more algorithms may be quantified by comparing the at least one perceived dimension to at least one actual dimension of the virtual anomaly as defined in the virtual road surface.

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.

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 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 computing device comprising a processing circuit and a data storage medium. The computing device is programmed to receive virtual sensor data that represents data collected by a virtual sensor associated with autonomously operating a virtual vehicle in a virtual environment and process the virtual sensor data to identify a limitation of a real-world sensor.

Abstract: A computing device includes a processing circuit and a data storage medium. The computing device is programmed to receive a user input selecting at least one testing parameter associated with autonomously operating a virtual vehicle in a virtual environment, simulate the virtual environment incorporating the at least one testing parameter, virtually navigate the virtual vehicle through the virtual environment, collect virtual sensor data, and processing the collected virtual sensor data.

Abstract: A computing device includes a processing circuit and a data storage medium, and is programmed to receive a user input representing a vehicle control action associated with operating a virtual vehicle in a virtual environment, virtually navigate the virtual vehicle through the virtual environment according to the vehicle control action, collect virtual sensor data, and process the virtual sensor data collected.