Abstract: A method for assessing possible trajectories of road users in a traffic environment includes capturing the traffic environment with static and dynamic features, identifying at least one traffic user, determining at least one possible trajectory for at least one road user in the traffic environment, and assessing the at least one determined possible trajectory for the at least one road user with an adapted/trained recommendation service and the captured traffic environment.

Abstract: A system and method for localizing an entity includes a processing system with at least one processing device. The processing system is configured to obtain sensor data from a sensor system that includes at least a first set of sensors and a second set of sensors. The processing system is configured to produce a map image with a map region that is selected and aligned based on a localization estimate. The localization estimate is based on sensor data from the first set of sensors. The processing system is configured to extract sets of localization features from the sensor data of the second set of sensors. The processing system is configured to generate visualization images in which each visualization image includes a respective set of localization features. The processing system is configured to generate localization output data for the vehicle in real-time by optimizing an image registration of the map image relative to the visualization images.

Abstract: Systems and methods of detecting and tracking one or more vehicles in a field of view of an imaging system using neural network processing. An electronic controller receives an input image from a camera mounted on the host vehicle. The electronic controller applies a neural network configured to output a definition of a three-dimensional bounding box based at least in part on the input image. The three-dimensional bounding box indicates a size and a position of a detected vehicle in a field of view of the input image. The three-dimensional bounding box includes a first quadrilateral shape outlining a rear or front of the detected vehicle and a second quadrilateral shape outline a side of the detected vehicle.

Abstract: A method for assessing possible trajectories of road users in a traffic environment includes capturing the traffic environment with static and dynamic features, identifying at least one traffic user, determining at least one possible trajectory for at least one road user in the traffic environment, and assessing the at least one determined possible trajectory for the at least one road user with an adapted/trained recommendation service and the captured traffic environment.

Abstract: A method is implemented by a processing system with at least one computer processor. The method includes obtaining a visualization of a scene that includes a template of a simulation object within a region. The method includes generating a sensor-fusion representation of the template upon receiving the visualization as input. The method includes generating a simulation of the scene with a sensor-fusion detection estimate of the simulation object instead of the template within the region. The sensor-fusion detection estimate includes object contour data indicating bounds of the sensor-fusion representation. The sensor-fusion detection estimate represents the bounds or shape of an object as would be detected by a sensor-fusion system.

Abstract: Systems and methods of detecting and tracking one or more vehicles in a field of view of an imaging system using neural network processing. An electronic controller receives an input image from a camera mounted on the host vehicle. The electronic controller applies a neural network configured to output a definition of a three-dimensional bounding box based at least in part on the input image. The three-dimensional bounding box indicates a size and a position of a detected vehicle in a field of view of the input image. The three-dimensional bounding box includes a first quadrilateral shape outlining a rear or front of the detected vehicle and a second quadrilateral shape outline a side of the detected vehicle.

Abstract: A system and method for localizing an entity includes a processing system with at least one processing device. The processing system is configured to obtain sensor data from a sensor system that includes at least a first set of sensors and a second set of sensors. The processing system is configured to produce a map image with a map region that is selected and aligned based on a localization estimate. The localization estimate is based on sensor data from the first set of sensors. The processing system is configured to extract sets of localization features from the sensor data of the second set of sensors. The processing system is configured to generate visualization images in which each visualization image includes a respective set of localization features. The processing system is configured to generate localization output data for the vehicle in real-time by optimizing an image registration of the map image relative to the visualization images.

Abstract: A method of operating an autonomous vehicle on a roadway includes generating stereo vision data with a stereo vision camera of a vehicle guidance system of the autonomous vehicle, the stereo vision data representative of a traffic light on the roadway, generating disparity map data with a controller of the vehicle guidance system based on the stereo vision data, and generating odometry data of the vehicle at a first time and at a second time after the first time with an odometry system of the autonomous vehicle. The method further includes determining a position of the traffic light based on the disparity map data at the first time, determining a predicted position of the traffic light in the disparity map data at the second time based on the odometry data, and determining a state of the traffic light at the predicted position.

Abstract: A method and system for navigating a vehicle using automatically labeled images. The system includes a camera configured to capture an image of a roadway and an electronic processor communicatively connected to the camera. The electronic processor is configured to load a map that includes a first plurality of features and receive the image. The image includes a second plurality of features. The electronic processor is further configured to project the map onto the image; detect the second plurality of features within the image; and align the map with the image by aligning the first plurality of features with the second plurality of features. The electronic processor is further configured to copy a label describing one of the first plurality of features onto a corresponding one of the second plurality of features to create a labelled image and to use the labelled image to assist in navigation of the vehicle.

Abstract: A method and system for navigating a vehicle using automatically labeled images. The system includes a camera configured to capture an image of a roadway and an electronic processor communicatively connected to the camera. The electronic processor is configured to load a map that includes a first plurality of features and receive the image. The image includes a second plurality of features. The electronic processor is further configured to project the map onto the image; detect the second plurality of features within the image; and align the map with the image by aligning the first plurality of features with the second plurality of features. The electronic processor is further configured to copy a label describing one of the first plurality of features onto a corresponding one of the second plurality of features to create a labelled image and to use the labelled image to assist in navigation of the vehicle.