Abstract: A camera and production technique for an ADAS. The camera lens and image sensor are positioned along multiple axes using a target located at a first distance from the lens to establish a first relative position between the lens and the image sensor. The first relative position between the lens and the image sensor is modified by a predetermined amount for an object located a second distance from the lens. The second distance is larger than the first distance.

Abstract: A detection system for a vehicle is provided. The detection system may include at least one image capture device configured to acquire a plurality of images of an area forward of the vehicle, the area including a curb separating a road surface from an off-road surface and a data interface. The detection system may also include at least one processing device programmed to receive the plurality of images via the data interface, and determine a plurality of curb edge line candidates in the plurality of images. The at least one processing device may be further programmed to identify at least one edge line candidate as an edge line of the curb.

Abstract: Systems and methods are provided for navigating an autonomous vehicle using reinforcement learning techniques.

Abstract: Systems and methods are provided for navigating an autonomous vehicle using reinforcement learning techniques.

Abstract: Detection of three dimensional obstacles using a system mountable in a host vehicle including a camera connectible to a processor. Multiple image frames are captured in the field of view of the camera. In the image frames, an imaged feature is detected of an object in the environment of the vehicle. The image frames are portioned locally around the imaged feature to produce imaged portions of the image frames including the imaged feature. The image frames are processed to compute a depth map locally around the detected imaged feature in the image portions. Responsive to the depth map, it is determined if the object is an obstacle to the motion of the vehicle.

Abstract: A system mountable in a vehicle to provide object detection in the vicinity of the vehicle. The system includes a camera operatively attached to a processor. The camera is mounted externally at the rear of the vehicle. The field of view of the camera is substantially in the forward direction of travel of the vehicle along the side of the vehicle. Multiple image frames are captured from the camera. Yaw of the vehicle may be input or the yaw may be computed from the image frames. Respective portions of the image frames are selected responsive to the yaw of the vehicle. The image frames are processed to detect thereby an object in the selected portions of the image frames.

Abstract: An imaging system for a vehicle may include a first image capture device having a first field of view and configured to acquire a first image relative to a scene associated with the vehicle, the first image being acquired as a first series of image scan lines captured using a rolling shutter. The imaging system may also include a second image capture device having a second field of view different from the first field of view and that at least partially overlaps the first field of view, the second image capture device being configured to acquire a second image relative to the scene associated with the vehicle, the second image being acquired as a second series of image scan lines captured using a rolling shutter. As a result of overlap between the first field of view and the second field of view, a first overlap portion of the first image corresponds with a second overlap portion of the second image.

Abstract: Systems and methods use cameras to provide autonomous navigation features. In one implementation, top-down refinement in lane marking navigation is provided. The system may include one or more memories storing instructions and one or more processors configured to execute the instructions to cause the system to receive from one or more cameras one or more images of a roadway in a vicinity of a vehicle, the roadway comprising a lane marking comprising a dashed line, update a model of the lane marking based on odometry of the one or more cameras relative to the roadway, refine the updated model of the lane marking based on an appearance of dashes derived from the received one or more images and a spacing between dashes derived from the received one or more images, and cause one or more navigational responses in the vehicle based on the refinement of the updated model.

Abstract: Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system includes a processing device programmed to receive a plurality of images representative of an environment of the host vehicle. The environment includes a road on which the host vehicle is traveling. The at least one processing device is further programmed to analyze the images to identify a target vehicle traveling in a lane of the road different from a lane in which the host vehicle is traveling; analyze the images to identify a lane mark associated with the lane in which the target vehicle is traveling; detect lane mark characteristics of the identified lane mark; use the detected lane mark characteristics to determine a type of the identified lane mark; determine a characteristic of the target vehicle; and determine a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.

Abstract: A control system for a vehicle may include a forward-facing camera to capture a plurality of images of a road ahead of the vehicle and a processing device. The processing device may be configured to: provide feedback to a vehicle operator of the vehicle to change lanes to a new lane, in which the vehicle is not already traveling, based on the ending of a current lane, the ending of the current lane indicated by a first traffic cone identified in the plurality of images; and update a distance from the vehicle to a second traffic cone, based on a position of the vehicle, the second traffic cone used to constrain vehicle operation to the new lane.

Abstract: Systems and methods are provided for determining a road surface characteristic. In one implementation, a system includes at least one processing device programmed to receive, from at least one camera, at least two images representative of an environment of a vehicle; align at least a portion of the at least two images using estimated motion of the vehicle; provide, to a trained system configured to determine a characteristic of the road surface, at least the aligned portions of the at least two images; receive, from the trained system, the determined characteristic of the road surface; and provide, to a vehicle control system, based on at least the determined characteristic of the road surface, control information for changing at least one setting of the vehicle control system.

Abstract: An arithmetic logic unit (ALU) including a first routing grid connected to multiple data lanes to drive first data to the data lanes. A second routing grid is connected to the data lanes to drive second data to the data lanes. Each of the data lanes include multiple, e.g. N, functional units with first inputs from the first routing grid and second inputs from the second routing grid. The functional units compute pairwise a function of the respective first data on the respective first inputs and the respective second data on the respective second inputs. Each of the data lanes include a reduction unit with inputs adapted to receive K? bits per word from the functional units. The reduction unit is configured to perform a reduction operation configured to output an output result having a reduced number J? bits per word, wherein J? is less than N multiplied by K?.

Abstract: A system mountable in a vehicle to provide object detection in the vicinity of the vehicle. The system includes a camera operatively attached to a processor. The camera is mounted externally at the rear of the vehicle. The field of view of the camera is substantially in the forward direction of travel of the vehicle along the side of the vehicle. Multiple image frames are captured from the camera. Yaw of the vehicle may be input or the yaw may be computed from the image frames. Respective portions of the image frames are selected responsive to the yaw of the vehicle. The image frames are processed to detect thereby an object in the selected portions of the image frames.

Abstract: Systems and methods are provided for navigating an autonomous vehicle using reinforcement learning techniques.

Abstract: A navigation system for a vehicle may include at least one image capture device configured to acquire a plurality of images of an environment of a vehicle and a radar sensor to detect an object in the environment of the vehicle and to provide and output including range information indicative of at least one of a range or range rate between the vehicle and the object.

Abstract: Determining three-dimensional structure in a road environment using a system mountable in a host vehicle including a camera connectable to a processor. Multiple image frames are captured in the field of view of the camera. In the image frames, a line is selected below which the road is imaged. The line separates between upper images essentially excluding images of the road and lower images essentially including images of the road. One or more of the lower images is warped, according to a road homography to produce at least one warped lower image. The three-dimensional structure may be provided from motion of a matching feature within the upper images or from motion of a matching feature within at least one of the lower images and at least one warped lower image.

Abstract: A detection system for a vehicle is provided. The detection system may include at least one image capture device configured to acquire a plurality of images of an area forward of the vehicle, the area including a curb separating a road surface from an off-road surface and a data interface. The detection system may also include at least one processing device programmed to receive the plurality of images via the data interface, and determine a plurality of curb edge line candidates in the plurality of images. The at least one processing device may be further programmed to identify at least one edge line candidate as an edge line of the curb.

Abstract: Systems and methods are disclosed for navigating a host vehicle. In one implementation, at least one processor may be programmed to receive images representative of an environment of the host vehicle; identify an oncoming target vehicle associated with a projected trajectory indicated by an aspect of the oncoming target vehicle in the images; determine that a planned trajectory for the host vehicle crosses the projected trajectory of the oncoming target vehicle and indicates a potential turn-across-path event; determine a remedial action for the host vehicle in response to the oncoming target vehicle and the potential turn-across-path event; implement the remedial action if the oncoming target vehicle is determined to be not approaching a road feature that negates the potential turn-across-path event; and forego the remedial action if the oncoming target vehicle is determined to be approaching a road feature that negates the potential turn-across-path event.

Abstract: Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system for navigating a vehicle includes at least one processing device programmed to receive, from an image capture device, a plurality of images associated with an environment of the vehicle, analyze at least one of the plurality of images to identify a navigable region in the environment of the vehicle, identify, based on the at least one of the plurality of images, at least one barrier associated with an edge of the navigable region, and determine a type of the at least one barrier. The at least one processing device is also programmed to determine a navigational path of the vehicle based on the determined type of the at least one barrier, and cause the vehicle to travel on at least a portion of the determined navigational path.

Abstract: A driver assistance systems mountable in a host vehicle while the host vehicle is moving forward with headlights on for detection of obstacles based on shadows. The driver assistance system includes a camera operatively connectible to a processor. A first image frame and a second image frame are captured of a road. A first dark image patch and a second dark image patch include intensity values less than a threshold value. The first and the second dark image patches are tracked from the first image frame to the second image frame as corresponding images of the same portion of the road. Respective thicknesses in vertical image coordinates are measured of the first and second dark image patches responsive to the tracking.