Robert Boschgmbh

Other

Synaptics Corporation

Krishna Mohan Chinni

Omar Oreifej

Rajani Janardhana

Adam L Schwartz

Gianni B Arcaini

Aydin Arpa

Karthikeyan Shanmuga Vadivel

Aghapi Mordovanakis

Kuntal Sengupta

Boyan Ivanov Bonev

Sameer J Parikh

Israel Umbehant

Larry Strach

Alireza Nemati

Miklos Halasz

Robert Umbehant

A system for representing objects in a surrounding environment of a vehicle using a Frenet coordinate system. The system includes a memory and an electronic processor. The electronic processor is in communication with the memory and configured to, receive, from one or more sensors, input regarding one or more objects in the surrounding environment. The input includes, for each of the one or more objects, a representation of a location of the object in Cartesian coordinates. The electronic processor is further configured to convert the location of each object from Cartesian coordinates to Frenet coordinates and store the location of each object in Frenet coordinates in the memory.

Representing objects in a surrounding environment of a vehicle using a frenet coordinate system

System, method, and device for controlling a vehicle. In one example, the system includes an electronic processor configured to capture, via a camera, a first image, determine, within the first image, a road traffic factor, and generate, based on sensor information from one or more sensors of the vehicle, a second image depicting an environment surrounding the vehicle. The second image includes the road traffic factor. The electronic processor is also configured to, determine, based on the detected road traffic factor and the second image, a predicted trajectory of a traffic participant proximate to the vehicle, and generate a steering command for the vehicle based on the predicted trajectory.

Vehicle trajectory prediction using road topology and traffic participant object states

A system, method, and computer-readable medium having instructions stored thereon to enable an ego vehicle having an autonomous driving function to estimate and traverse a curved segment of highway utilizing radar sensor data. The radar sensor data may comprise stationary reflections and moving reflections. The ego vehicle may utilize other data, such as global positioning system data, for the estimation and traversal. The estimation of the curvature may be refined based upon a lookup table or a deep neural network.

Lane boundary detection using radar signature trace data

A system for performing object and activity recognition based on data from a camera and a radar sensor. The system includes a camera, a radar sensor, and an electronic processor. The electronic processor is configured to receive an image from the camera and determine a portion of the image including an object. The electronic processor is also configured to receive radar data from the radar sensor and determine radar data from the radar sensor associated with the object in the image from the camera. The electronic processor is also configured to convert the radar data associated with the object to a time-frequency image and analyze the time-frequency image and the image of the object to classify the object and an activity being performed by the object.

Performing object and activity recognition based on data from a camera and a radar sensor

A method includes obtaining an occupancy grid. The method includes generating a reference, such as a ray, on the occupancy grid. The ray originates from a location of a vehicle and extends in a direction that corresponds to a driving direction of the vehicle. The method includes identifying markers along the ray. The markers include transition markers, which indicate a first type of transition or a second type of transition. The first type of transition is from an unoccupied zone to an occupied zone. The second type of transition is from the occupied zone to the unoccupied zone. The method includes generating a representation of drivable space in real-time based on computations involving sections that are associated with the first type of transition and the second type of transition. The method includes providing guidance to the vehicle based on the representation of drivable space.

System and method for generating a representation of an environment

Methods of creating trained semantic segmentation network models and operating vehicles using the model. One example method includes an outside view camera configured to capture images that represent an artificial representation of the driver's view, a driver-facing camera configured to capture a driver's eye movements, and an electronic controller. The electronic controller is configured to receive images from the cameras; calibrate the image of the driver's eye movement with the artificial driver view; create a pixel weighted heat map of the calibrated images; create a trained semantic segmentation neural network model and a trained attention neural network model using the pixel weighted heat map and the artificial driver view; and operate the vehicle using the trained semantic segmentation neural network model and the trained attention neural network model.

Semantic segmentation using driver attention information

Systems and methods for updating an enrollment template having a plurality of enrollment views of a biometric input object. A determination is made as to whether a new input biometric view is a candidate view for template update based on a match criterion, and a determination is made as to whether the new input biometric view increases coverage of the biometric input object by the enrollment template. The new input biometric view is added to the enrollment template as a new enrollment view in response to determining that the new biometric view i) is a candidate view for template update, and ii) increases coverage of the biometric input object by the enrollment template.

Biometric template updating systems and methods

Systems and methods for aligning images are disclosed. A method includes: receiving a first skeletonized biometric image; generating a first coarse representation of the first skeletonized biometric image; identifying a set of candidate transformations that align the first skeletonized biometric image to a second skeletonized biometric image based on comparing transformed versions of the first coarse representation to a second coarse representation of the second skeletonized biometric image; selecting a first candidate transformation as the candidate transformation that minimizes a difference metric between a transformed version of the first skeletonized biometric image and the second skeletonized biometric image; and determining whether the first skeletonized biometric image transformed by the first candidate transformation matches the second skeletonized biometric image, wherein the first skeletonized biometric image transformed by the first candidate transformation matches the second skeletonized biometric image if the difference metric satisfies a threshold.

Systems and methods for coarse-to-fine ridge-based biometric image alignment

This is a method and apparatus for bridge collision alert and monitoring impact analysis, which allows users of the method and apparatus to continuously monitor the integrity of a bridge as well as to detect insult or impact to the bridge structure or both as well as viewing purposes as for forensic purposes. The system is comprised of a series of monitors that gather different pieces of date and integrate that data into a system for view by the operator.

Growing community of inventors

Canton, MI, United States of America

Krishna Mohan Chinni