Abstract: A system for a host vehicle operating on a road surface includes a polarimetric camera, a global positioning system (“GPS”) receiver, a compass, and an electronic control unit (“ECU”). The camera collects polarimetric image data of a drive scene, including a potential driving path on the road surface. The ECU receives the polarimetric image data, estimates the Sun location using the GPS receiver and compass, and computes an ideal representation of the road surface using the Sun location. The ECU normalizes the polarimetric image data such that the road surface has a normalized representation in the drive scene, i.e., an angle of linear polarization (“AoLP”) and degree of linear polarization (“DoLP”) equal predetermined fixed values. The ECU executes a control action using the normalized representation.

Abstract: A system for vehicle trajectory planning. The system may include a polarized camera system configured to generate polarized images of a roadway to be driven over with a vehicle, a prior controller configured to generate a prior for the roadway based at least in part on the polarized images, and a driving assistance system configured to provide a driving assistance according to the prior.

Abstract: A detection system for a host vehicle includes a camera, global positioning system (“GPS”) receiver, compass, and electronic control unit (“ECU”). The camera collects polarimetric image data forming an imaged drive scene inclusive of a road surface illuminated by the Sun. The GPS receiver outputs a present location of the vehicle as a date-and-time-stamped coordinate set. The compass provides a directional heading of the vehicle. The ECU determines the Sun's location relative to the vehicle and camera using an input data set, including the present location and directional heading. The ECU also detects a specular reflecting area or areas on the road surface using the polarimetric image data and Sun's location, with the specular reflecting area(s) forming an output data set. The ECU then executes a control action aboard the host vehicle in response to the output data set.

Abstract: A system in a vehicle includes an image sensor to obtain images in an image sensor coordinate system and a depth sensor to obtain point clouds in a depth sensor coordinate system. Processing circuitry implements a neural network to determine a validation state of a transformation matrix that transforms the point clouds in the depth sensor coordinate system to transformed point clouds in the image sensor coordinate system. The transformation matrix includes rotation parameters and translation parameters.

Abstract: A disclosure is presented for estimating height of an object using ultrasonic sensors carried onboard a vehicle. The height may be estimated by generating ultrasonic distance measurements based on a time of flight associated with ultrasonic reflections detected with the ultrasonic sensors, generating a three-dimensional (3D) occupancy grid to volumetrically represent at least a portion of an environment within field of view of the ultrasonic sensors, the 3D occupancy grid including a plurality of volumetric pixels (voxels), assigning a count value to each of the voxels based on the distance measurements, and estimating the height according to a relative spatial relationship between the vehicle and the voxel having the count value with a greatest value.

Abstract: A system for a host vehicle operating on a road surface includes a polarimetric camera, a global positioning system (“GPS”) receiver, a compass, and an electronic control unit (“ECU”). The camera collects polarimetric image data of a drive scene, including a potential driving path on the road surface. The ECU receives the polarimetric image data, estimates the Sun location using the GPS receiver and compass, and computes an ideal representation of the road surface using the Sun location. The ECU normalizes the polarimetric image data such that the road surface has a normalized representation in the drive scene, i.e., an angle of linear polarization (“AoLP”) and degree of linear polarization (“DoLP”) equal predetermined fixed values. The ECU executes a control action using the normalized representation.

Abstract: A detection system for a host vehicle includes a camera, global positioning system (“GPS”) receiver, compass, and electronic control unit (“ECU”). The camera collects polarimetric image data forming an imaged drive scene inclusive of a road surface illuminated by the Sun. The GPS receiver outputs a present location of the vehicle as a date-and-time-stamped coordinate set. The compass provides a directional heading of the vehicle. The ECU determines the Sun's location relative to the vehicle and camera using an input data set, including the present location and directional heading. The ECU also detects a specular reflecting area or areas on the road surface using the polarimetric image data and Sun's location, with the specular reflecting area(s) forming an output data set. The ECU then executes a control action aboard the host vehicle in response to the output data set.

Abstract: A system for vehicle trajectory planning. The system may include a polarized camera system configured to generate polarized images of a roadway to be driven over with a vehicle, a prior controller configured to generate a prior for the roadway based at least in part on the polarized images, and a driving assistance system configured to provide a driving assistance according to the prior.

Abstract: A free space estimation and visualization system for a host vehicle includes a camera configured to collect red-green-blue (“RGB”)-polarimetric image data of drive environs of the host vehicle, including a potential driving path. An electronic control unit (“ECU”) receives the RGB-polarimetric image data and estimates free space in the driving path by processing the RGB-polarimetric image data via a run-time neural network. Control actions are taken in response to the estimated free space. A method for use with the visualization system includes collecting RGB and lidar data of target drive scenes and generating, via a first neural network, pseudo-labels of the scenes. The method includes collecting RGB-polarimetric data via a camera and thereafter training a second neural network using the RGB-polarimetric data and pseudo-labels. The second neural network is used in the ECU to estimate free space in the potential driving path.

Abstract: Presented are intelligent vehicle systems for off-road driving incident prediction and assistance, methods for making/operating such systems, and vehicles networking with such systems. A method for operating a motor vehicle includes a system controller receiving geolocation data indicating the vehicle is in or entering off-road terrain. Responsive to the vehicle geolocation data, the controller receives, from vehicle-mounted cameras, camera-generated images each containing the vehicle's drive wheel(s) and/or the off-road terrain's surface. The controller receives, from a controller area network bus, vehicle operating characteristics data and vehicle dynamics data for the motor vehicle. The camera data, vehicle operating characteristics data, and vehicle dynamics data is processed via a convolutional neural network backbone to predict occurrence of a driving incident on the off-road terrain within a prediction time horizon.

Abstract: Methods, systems, and apparatuses to build an explainable user output to receive input feature data by a neural network of multiple layers of an original classifier; determine a semantic function to label data samples with semantic categories; determine a semantic accuracy for each layer of the original classifier within the neural network; compare each layer based on results from the comparison of the semantic accuracy; designate a layer based on an amount of computed semantic accuracy; extend the designated layer by a category branch to the neural network to extract semantic data samples from the semantic content to train a set of new connections of an explainable classifier to compute a set of output explanations with an accuracy measure associated each output explanation for each semantic category of the plurality of semantic categories, and compare the accuracy measure for each output explanation to generate the output explanation in a user understandable format.

Abstract: Vision-based airbag enablement may include capturing two-dimensional images of a passenger, segmenting the image, classifying the image, and determining seated height of the passenger from the image. Enabling or disabling deployment of the airbag may be controlled based at least in part upon the determined seated height.

Abstract: Presented are intelligent vehicle systems for off-road driving incident prediction and assistance, methods for making/operating such systems, and vehicles networking with such systems. A method for operating a motor vehicle includes a system controller receiving geolocation data indicating the vehicle is in or entering off-road terrain. Responsive to the vehicle geolocation data, the controller receives, from vehicle-mounted cameras, camera-generated images each containing the vehicle's drive wheel(s) and/or the off-road terrain's surface. The controller receives, from a controller area network bus, vehicle operating characteristics data and vehicle dynamics data for the motor vehicle. The camera data, vehicle operating characteristics data, and vehicle dynamics data is processed via a convolutional neural network backbone to predict occurrence of a driving incident on the off-road terrain within a prediction time horizon.

Abstract: A system and method for error estimation in an eye gaze tracking system in a vehicle may include an operator monitoring system providing measured eye gaze information corresponding to an object outside the vehicle and an external object monitoring system providing theoretical eye gaze information and an error in the measured eye gaze information based upon the measured eye gaze information and the theoretical eye gaze information.

Abstract: A system in a vehicle includes an image sensor to obtain images in an image sensor coordinate system and a depth sensor to obtain point clouds in a depth sensor coordinate system. Processing circuitry implements a neural network to determine a validation state of a transformation matrix that transforms the point clouds in the depth sensor coordinate system to transformed point clouds in the image sensor coordinate system. The transformation matrix includes rotation parameters and translation parameters.

Abstract: A system includes a seat belt routing module and a user interface device (UID) control module. The seat belt routing module is configured to: determine a routing of a seat belt relative to an occupant in a vehicle seat based on input from a webbing payout sensor; and determine the seat belt routing based on an input from an in-cabin sensor. The in-cabin sensor includes at least one of a camera, an infrared sensor, an ultrasonic sensor, a radar sensor, and a lidar sensor. The UID control module is configured to control a user interface device to indicate that the seat belt is being worn improperly when: the seat belt routing determined using at least one of the webbing payout sensor and the in-cabin sensor is improper; and the seat belt routing determined using the webbing payout sensor corresponds to the seat belt routing determined using the in-cabin sensor.

Abstract: A system and method for error estimation in an eye gaze tracking system in a vehicle may include an operator monitoring system providing measured eye gaze information corresponding to an object outside the vehicle and an external object monitoring system providing theoretical eye gaze information and an error in the measured eye gaze information based upon the measured eye gaze information and the theoretical eye gaze information.

Abstract: A vehicle, system and a computer-implemented method for setting a height of a seat belt in a vehicle. The system includes a computer vision module, a motor and a controller. The computer vision module determines a seatbelt-neck distance (SND) and a seatbelt-shoulder distance (SSD) for an occupant of the vehicle. The motor adjusts the height of the seat belt. The controller control the motor to set the height of the seat belt based on the SND and the SSD.

Abstract: Vision-based airbag enablement may include capturing two-dimensional images of a passenger, segmenting the image, classifying the image, and determining seated height of the passenger from the image. Enabling or disabling deployment of the airbag may be controlled based at least in part upon the determined seated height.

Abstract: Methods, systems, and apparatuses to build an explainable user output to receive input feature data by a neural network of multiple layers of an original classifier; determine a semantic function to label data samples with semantic categories; determine a semantic accuracy for each layer of the original classifier within the neural network; compare each layer based on results from the comparison of the semantic accuracy; designate a layer based on an amount of computed semantic accuracy; extend the designated layer by a category branch to the neural network to extract semantic data samples from the semantic content to train a set of new connections of an explainable classifier to compute a set of output explanations with an accuracy measure associated each output explanation for each semantic category of the plurality of semantic categories, and compare the accuracy measure for each output explanation to generate the output explanation in a user understandable format.