Abstract: Systems, methods, and non-transitory computer readable media may be configured to determine user response to simulation of driving experience. Simulation information may be obtained. The simulation information may define a simulation of driving experience. A simulation of driving experience may include a visual portion, an audio portion, and a motion portion. The visual portion of the simulation may be outputted via a display. The audio portion of the simulation may be outputted via a speaker. The motion portion of the simulation may be outputted via a vibration motor configured to vibrate a seat and motion of the seat along one or more of six-degrees of freedom. A user's response to the simulation of driving experience may be determined via a set of sensors.

Abstract: A set of sensor information may include first sensor information generated based on a first sensor of a first vehicle and second sensor information generated based on a second sensor of a second vehicle. Individual sensor information may characterize positions of objects in an environment of individual sensors. Relevant sensor information for a vehicle may be determined based on the set of sensor information and a position of the vehicle. The relevant sensor information may characterize positions of objects in a maneuver environment of the vehicle. A desired navigation of the vehicle in the maneuver environment of the vehicle may be determined based on the relevant sensor information. An instruction may be provided to the vehicle based on the desired navigation of the vehicle. The instruction may characterize one or more maneuvers to be performed by the vehicle to execute the desired navigation.

Abstract: Systems, methods, and non-transitory computer readable media may be configured to facilitate prioritization of vehicle navigation. One or more vehicles in an environment of a priority vehicle may be identified. Priorities of the vehicle(s) and the priority vehicle may be determined. A desired navigation of the priority vehicle in the environment may be determined based on the priorities of the vehicle(s) and the priority vehicle. An instruction may be provided to at least one of the vehicle(s) based on the desired navigation of the priority vehicle in the environment. The instruction may characterize one or more maneuvers to be performed by the at least one of the vehicle(s) to facilitate the desired navigation of the priority vehicle.

Abstract: A sensor enclosure comprises a cover and a structure. The structure can be encased by the cover. The structure comprises a frame, a ring, and one or more anchoring posts. The frame can be configured to mount one or more sensors. The ring, disposed peripherally to the frame, can be operatively coupled to the cover. The ring can include a drainage ring plate that drains rainwater accumulated on the cover away from the sensor enclosure. The one or more anchoring posts, disposed underneath the frame and the ring, can be used to anchor the sensor enclosure to a vehicle.

Abstract: Obtaining one or more parameters of an autonomous vehicle, the parameters including any of a position, path, and/or speed of the autonomous vehicle. A region of interest from a plurality of regions surrounding the autonomous vehicle is identified based on the one or more parameters. One or more sensors mounted on a sensor guide rail are controlled, based on the region of interest, to move the sensor(s) along at least a portion of the autonomous vehicle, and to capture sensor data of the region of interest and not capture sensor data from the one or more other regions of the plurality of regions surrounding the autonomous vehicle, the sensor guide rail being mounted on a surface of the autonomous vehicle. The captured sensor data is provided to a processor capable of facilitating, based on the captured sensor data of the region of interest, one or more autonomous vehicle driving actions.

Abstract: A computer-implemented method and a system for training a computer-based autonomous driving model used for an autonomous driving operation by an autonomous vehicle are described. The method includes: creating time-dependent three-dimensional (3D) traffic environment data using at least one of real traffic element data and simulated traffic element data; creating simulated time-dependent 3D traffic environmental data by applying a time-dependent 3D generic adversarial network (GAN) model to the created time-dependent 3D traffic environment data; and training a computer-based autonomous driving model using the simulated time-dependent 3D traffic environmental data.

Abstract: A system included and a computer-implemented method performed in an autonomous-driving vehicle are described. The system performs: detecting one or more movable objects; determining a target movable object from the one or more detected objects; determining a manner of generating a directed alert notification selectively toward the target movable object; and causing a directed alert notification of the determined manner to be generated toward the target movable object.

Abstract: A system included and a computer-implemented method performed in one of a plurality of self-driving vehicles that are connected through a network are described. The system performs: processing image data of one or more scene images received by said one of the plurality of self-driving vehicles, to detect one or more objects included in the one or more scene images; determining a target object from the one or more detected objects at least based on the processed image data; predicting movement of the target object at least based on a current position and a current movement state of the target object; and performing a self-driving operation to drive said one of the plurality of self-driving vehicles based on the predicted movement of the target object.

Abstract: Systems, methods, and non-transitory computer-readable media are provided for implementing a preemptive control for an autonomous vehicle to improve ride quality. Data from one or more sensors onboard the autonomous vehicle can be acquired. A surface imperfection of a road can be identified from the data. A next action for the autonomous vehicle can be determined based on the surface imperfection. A signal can be outputted that causes the autonomous vehicle to act in accordance with the next action.

Abstract: Systems, methods, and non-transitory computer readable media may be configured to characterize optical characteristics of optical elements. An optical element mount may be configured to carry an optical element. A calibration display may be configured to display a calibration object. The calibration object may include a known visual pattern. Multiple images of the calibration object may be obtained. The multiple images may be acquired using the optical element carried by the optical element mount. The multiple images may include different perspectives of the calibration object. Optical characteristics of the optical element may be characterized based on the known visual pattern and the different perspectives of the calibration object.

Abstract: Systems, methods, and non-transitory computer-readable media are provided for implementing a tracking camera system onboard an autonomous vehicle. Coordinate data of an object can be received. The tracking camera system actuates, based on the coordinate data, to a position such that the object is in view of the tracking camera system. Vehicle operation data of the autonomous vehicle can be received. The position of the tracking camera system can be adjusted, based on the vehicle operation data, such that the object remains in view of the tracking camera system while the autonomous vehicle is in motion. A focus of the tracking camera system can be adjusted to bring the object in focus. The tracking camera system captures image data corresponding to the object.

Abstract: Systems, methods, and non-transitory computer-readable media are provided for acquiring driving records from an autonomous vehicle. One or more patterns can be determined from the driving records. One or more criteria can be generated based on the one or more patterns. One or more suspicious points can be identified in the driving records by applying the one or more criteria to the driving records.

Abstract: A system included and a computer-implemented method performed in one of a plurality of self-driving vehicles that are connected through a network are described. The system performs: processing image data of one or more scene images received by said one of the plurality of self-driving vehicles, to detect one or more objects included in the one or more scene images; determining a target object from the one or more detected objects at least based on the processed image data; predicting movement of the target object at least based on a current position and a current movement state of the target object; and performing a self-driving operation to drive said one of the plurality of self-driving vehicles based on the predicted movement of the target object.

Abstract: A system included and a computer-implemented method performed in an autonomous-driving vehicle are described. The system performs: detecting one or more movable objects; determining a target movable object from the one or more detected objects; determining a manner of generating a directed alert notification selectively toward the target movable object; and causing a directed alert notification of the determined manner to be generated toward the target movable object.

Abstract: Systems, methods, and non-transitory computer readable media configured to generate enhanced training information. Training information may be obtained. The training information may characterize behaviors of moving objects. The training information may be determined based on observations of the behaviors of the moving objects. Behavior information may be obtained. The behavior information may characterize a behavior of a given object. Enhanced training information may be generated by inserting the behavior information into the training information.

Abstract: Systems, methods, and non-transitory computer readable media configured to generate enhanced three-dimensional information. Three-dimensional information of a scene may be obtained. The three-dimensional information may define a three-dimensional point cloud model of the scene. The three-dimensional information may be determined based on distances of the scene from a location. Image information may be obtained. The image information may define one or more images of an object. The object may be identified based on the image information. A three-dimensional point cloud model of the object may be obtained. Enhanced three-dimensional information of the scene may be generated by inserting the three-dimensional point cloud model of the object into the three-dimensional point cloud model of the scene.

Abstract: Systems and methods are provided for segmenting a data frame to be acquired into a number of incremental data of equal data length. A first incremental data of the data frame can be acquired from one or more sensors. The first incremental data of the data frame can be processed while a next incremental data of the data frame is being acquired from the one or more sensors. The acquiring and processing of incremental data of the data frame can continue until a last incremental data of the data frame is acquired and processed. Processed incremental data can be outputted as a processed data frame.

Abstract: An adaptive filter system and a method for controlling the adaptive filter system are described herein. The system can includes one or more filters to attenuate incoming light. The one or more filters can be moved by one or more actuators. The method can capture image data from an imaging device through the one or more filters. Information can be determined from the captured image data. The one or more filters can be moved to a position for capturing image data based on the information.

Abstract: A system included and a computer-implemented method performed in an autonomous-driving vehicle are described. The system performs: detecting a wireless push signal transmitted from a signal transmitter accompanied by an off-vehicle passer and received by a signal receiver of the autonomous-driving vehicle, the wireless push signal including information about a motion capability level of the off-vehicle passer, determining a position and a motion capability level of the off-vehicle passer at least based on the wireless push signal, and controlling a locomotive mechanism of the autonomous-driving vehicle based on the determined position and motion capability level of the off-vehicle passer.

Abstract: A system included and a computer-implemented method performed in an autonomous-driving vehicle are described. The system performs: detecting one or more movable objects; determining a target movable object from the one or more detected objects; determining a manner of generating a directed alert notification selectively toward the target movable object; and causing a directed alert notification of the determined manner to be generated toward the target movable object.