Abstract: A system and method for proximate vehicle intention prediction for autonomous vehicles are disclosed. A particular embodiment is configured to: receive perception data associated with a host vehicle; extract features from the perception data to detect a proximate vehicle in the vicinity of the host vehicle; generate a trajectory of the detected proximate vehicle based on the perception data; use a trained intention prediction model to generate a predicted intention of the detected proximate vehicle based on the perception data and the trajectory of the detected proximate vehicle; use the predicted intention of the detected proximate vehicle to generate a predicted trajectory of the detected proximate vehicle; and output the predicted intention and predicted trajectory for the detected proximate vehicle to another subsystem.

Abstract: The embodiments of the present disclosure provide a display module and a middle frame, the display module includes a backlight module and a display panel, the backlight module includes a back plate, a first light source, a middle frame. The back plate includes a bottom plate, the first light source is on the bottom plate, the middle frame is between the bottom plate and the display panel. A side of the middle frame close to the display panel includes a first bearing portion and a second bearing portion, the first bearing portion is at an edge of the middle frame, a second light source is on the first bearing portion, the second bearing portion is on a side of the first bearing portion close to a display region of the display panel, the second bearing portion is connected to a position of the display panel corresponding to the display region.

Abstract: A vehicle position and velocity estimation system based on camera and LIDAR data is disclosed. An embodiment includes: receiving input object data from a subsystem of a vehicle, the input object data including image data from an image generating device and distance data from a distance measuring device, the distance measuring device comprising one or more LIDAR sensors; determining a first position of a proximate object near the vehicle from the image data; determining a second position of the proximate object from the distance data; correlating the first position and the second position by matching the first position of the proximate object detected in the image data with the second position of the same proximate object detected in the distance data; determining a three-dimensional (3D) position of the proximate object using the correlated first and second positions; and using the 3D position of the proximate object to navigate the vehicle.

Abstract: Disclosed are devices, systems and methods for using a rotating camera for vehicular operation. One example of a method for improving driving includes determining, by a processor in the vehicle, that a trigger has activated, orienting, based on the determining, a single rotating camera towards a direction of interest, and activating a recording functionality of the single rotating camera, where the vehicle comprises the single rotating camera and one or more fixed cameras, and where the single rotating camera provides a redundant functionality for, and consumes less power than, the one or more fixed cameras.

Abstract: A method includes receiving a series of road images from a side-view camera sensor of the autonomous driving vehicle. For each object from objects captured in the series of road images, a series of bounding boxes in the series of road images is generated, and a direction of travel or stationarity of the object is determined. The methods and apparatus also include determining a speed of each object for which the direction of travel has been determined and determining, based on the directions of travel, speeds, or stationarity of the objects, whether the autonomous driving vehicle can safely move in a predetermined direction. Furthermore, one or more control signals is sent to the autonomous driving vehicle to cause the autonomous driving vehicle to move or to remain stationary based on determining whether the autonomous driving vehicle can safely move in the predetermined direction.

Abstract: Embodiments are disclosed for providing full and redundant sensor coverage for an environment surrounding a vehicle. An example vehicle includes a plurality of first cameras and a plurality of second cameras. The first cameras are associated with a first field-of-view (FOV) having a first horizontal aspect, and the second cameras are associated with a second FOV having a second horizontal aspect. The first cameras and the second cameras are located at different angular locations on the vehicle along a horizontal plane. Horizontal aspects of two FOVs of any two consecutive cameras located along the horizontal plane overlap in the horizontal plane by at least a predetermined degree. Another example vehicle includes a controller for controlling autonomous driving operation of the vehicle and a sensor network that includes at least six sensors. Directional beams corresponding to the sensors cover a surrounding region of the vehicle relevant to the autonomous driving operation.

Abstract: A method of lane detection for a non-transitory computer readable storage medium storing one or more programs is disclosed. The one or more programs include instructions, which when executed by a computing device, cause the computing device to perform the following steps comprising: generating a ground truth associated with lane markings expressed in god's view; receiving features from at least one of a hit-map image and a fitted lane marking, wherein the hit-map image includes a classification of pixels that hit a lane marking, and the fitted lane marking includes pixels optimized based on the hit-map image; and training a confidence module based on the features and the ground truth, the confidence module configured to determine on-line whether a fitted lane marking is reasonable, using parameters that express a lane marking in an arc.

Abstract: Techniques are described for estimating pose of a camera located on a vehicle. An exemplary method of estimating camera pose includes obtaining, from a camera located on a vehicle, an image including a lane marker on a road on which the vehicle is driven, and estimating a pose of the camera such that the pose of the camera provides a best match according to a criterion between a first position of the lane marker determined from the image and a second position of the lane marker determined from a stored map of the road.

Abstract: A method of lane detection for a non-transitory computer readable storage medium storing one or more programs is disclosed. The one or more programs include instructions, which when executed by a computing device, cause the computing device to perform the following steps comprising: generating a ground truth associated with lane markings expressed in god's view; receiving features from at least one of a hit-map image and a fitted lane marking, wherein the hit-map image includes a classification of pixels that hit a lane marking, and the fitted lane marking includes pixels optimized based on the hit-map image; and training a confidence module based on the features and the ground truth, the confidence module configured to determine on-line whether a fitted lane marking is reasonable, using parameters that express a lane marking in an arc.

Abstract: Disclosed are methods and devices related to autonomous driving. In one aspect, a method is disclosed. The method includes determining three-dimensional bounding indicators for one or more first objects in road target information captured by a light detection and ranging (LIDAR) sensor; determining camera bounding indicators for one or more second objects in road image information captured by a camera sensor; processing the road image information to generate a camera matrix; determining projected bounding indicators from the camera matrix and the three-dimensional bounding indicators; determining, from the projected bounding indicators and the camera bounding indicators, associations between the one or more first objects and the one or more second objects to generate combined target information; and applying, by the autonomous driving system, the combined target information to produce a vehicle control signal.

Abstract: Systems and methods for detecting restricted traffic zones for autonomous driving are disclosed. In one aspect, method, includes receiving output from a perception system of an autonomous vehicle and detecting, based on the output from the perception system, one or more objects indicating a presence of a restricted traffic zone. The method may further include determining that the autonomous vehicle is entering the restricted traffic zone based on at least one of a prior map, an obstruction map, and the detected one or more objects.

Abstract: A system and method for path planning of autonomous vehicles based on gradient are disclosed. A particular embodiment includes: executing a path planning module by use of a data processor; receiving, from a remote data source by use of the data processor, ancillary data to modify operation of the path planning module based on a context in which an autonomous vehicle is operating; generating, by use of the data processor, a trajectory comprising a plurality of waypoints; and causing the autonomous vehicle to follow a path conforming to the plurality of waypoints.

Abstract: A system comprises an autonomous vehicle and a control device. The control device detects an event trigger that impacts the autonomous vehicle. In response, to detecting the event trigger, the control device enters the autonomous vehicle into a first degraded autonomy mode, In the first degraded autonomy mode, the control device communicates sensor data to an oversight server. The control device receives high-level commands from the oversight server. The one or more high-level commands indicate minimal risk maneuvers for the autonomous vehicle. The control device receives a maximum traveling speed for the autonomous vehicle from the oversight server. The control device navigates the autonomous vehicle using an adaptive cruise control according to the one or more high-level commands and the maximum traveling speed.

Abstract: Techniques are described for estimating pose of a camera located on a vehicle. An exemplary method of estimating camera pose includes obtaining, from a camera located on a vehicle, an image including a lane marker on a road on which the vehicle is driven, and estimating a pose of the camera such that the pose of the camera provides a best match according to a criterion between a first position of the lane marker determined from the image and a second position of the lane marker determined from a stored map of the road.

Abstract: Disclosed are devices, systems and methods for processing an image. In one aspect a method includes receiving an image from a sensor array including an x-y array of pixels, each pixel in the x-y array of pixels having a value selected from one of three primary colors, based on a corresponding x-y value in a mask pattern. The method may further include generating a preprocessed image by performing preprocessing on the image. The method may further include performing perception on the preprocessed image to determine one or more outlines of physical objects.

Abstract: Techniques are described to estimate orientation of one or more cameras located on a vehicle. The orientation estimation technique can include obtaining an image from a camera located on a vehicle while the vehicle is being driven on a road, determining, from a terrain map, a location of a landmark located at a distance from a location of the vehicle on the road, determining, in the image, pixel locations of the landmark, selecting one pixel location from the determined pixel locations; and calculating values that describe an orientation of the camera using at least an intrinsic matrix and a previously known extrinsic matrix of the camera, where the intrinsic matrix is characterized based on at least the one pixel location and the location of the landmark.

Abstract: Disclosed are devices, systems and methods for an audio assistant in an autonomous or semi-autonomous vehicle. In one aspect the informational audio assistant receives a first set of data from a vehicle sensor and identifies an object or condition using the data from the vehicle sensor. Audio is generated representative of a perceived danger of an object or condition. A second set of data from the vehicle sensor subsystem is received and the informational audio assistant determines whether an increased danger exists based on a comparison of the first set of data to the second set of data. The informational audio assistant will apply a sound profile to the generated audio based on the increased danger.

Abstract: Image processing techniques are described to obtain an image from a camera located on a vehicle while the vehicle is being driven, cropping a portion of the obtained image corresponding to a region of interest, detecting an object in the cropped portion, adding a bounding box around the detected object, determining position(s) of reference point(s) on the bounding box, and determining a location of the detected object in a spatial region where the vehicle is being driven based on the determined one or more positions of the second set of one or more reference points on the bounding box.

Abstract: Disclosed are distributed computing systems and methods for controlling multiple autonomous control modules and subsystems in an autonomous vehicle. In some aspects of the disclosed technology, a computing architecture for an autonomous vehicle includes distributing the complexity of autonomous vehicle operation, thereby avoiding the use of a single high-performance computing system and enabling off-the-shelf components to be use more readily and reducing system failure rates.

Abstract: Systems and techniques disclosed in this patent document provide novel and efficient ground transportation of goods by self-driving trucks based on a system of infrastructures with shipping hub facilities serving as warehouses and services centers in accordance with unique characteristics of operating autonomous self-driving trucks without entirely relying on human drivers, including features to cover technological issues, fleet management issues, and customer service issues. In addition, the disclosed systems and techniques may be implemented to integrate other fleet services and modes of transportation of goods with transportation services by self-driving trucks.