Abstract: In various examples, lane biasing for navigating around objects in autonomous systems and applications is described. Systems and methods are disclosed that generate lane (or other demarcated regions of an environment) geometries in environments—such as environments without clear lane or boundary demarcations—by using locations and poses of static objects and/or outputs of a drivable free-space analysis. The systems and methods then use the lane geometries, the locations of the static objects, and current paths (e.g., centerlines of the current paths) along the lanes to determine new paths for navigating around the static objects. For instance, the new paths may be determined by shifting the centerlines of the current paths in directions away from the static objects by some distance or safety margin. This way, the vehicles may navigate around the static objects with greater distances between the vehicles and the static objects for increased safety.

Abstract: A controller receives outputs from a plurality of sensors such as a camera, LIDAR sensor, RADAR sensor, and ultrasound sensor, which may be rearward facing. A probability is updated each time a feature in a sensor output indicates presence of an object. The probability may be updated as a function of a variance of the sensor providing the output and a distance to the feature. Where the variance of a sensor is directional, directional probabilities may be updated according to these variances and the distance to the feature. If the probability meets a threshold condition, actions may be taken such as a perceptible alert or automatic braking. The probability may be decayed in the absence of detection of objects. Increasing or decreasing trends in the probability may be amplified by further increasing or decreasing the probability.

Abstract: A hitch assist system and method are provided herein. A human machine interface is configured to receive user-input specifying a target travel direction of a vehicle. A controller is configured to generate commands for autonomously maneuvering the vehicle in the target travel direction, identify a hitch coupler of a trailer based on input received from an object detection system, and request additional user-input updating the target travel direction if the hitch coupler is unable to be identified.

Abstract: A system for detecting and identifying foliage includes a tracking component, a tracking parameters component, and a classification component. The tracking component is configured to detect and track one or more features within range data from one or more sensors. The tracking parameters component is configured to determine tracking parameters for each of the one or more features. The tracking parameters include a tracking age and one or more of a detection consistency and a position variability. The classification component is configured to classify a feature of the one or more features as corresponding to foliage based on the tracking parameters.

Abstract: A system and methods related to hitch assist are provided herein. An imager captures rear-vehicle images. A processor is configured to extract trailer and ground features from the captured images, compute vehicle motion displacement based on optical flow of the ground features, estimate a height of each trailer feature based on vehicle motion displacement and optical flow of the trailer features, and determine a trailer height based on the estimated heights of at least a portion of the trailer features.

Abstract: A system and methods related to hitch assist are provided herein. An imager captures rear-vehicle images. A processor is configured to extract trailer and ground features from the captured images, compute vehicle motion displacement based on optical flow of the ground features, estimate a height of each trailer feature based on vehicle motion displacement and optical flow of the trailer features, and determine a trailer height based on the estimated heights of at least a portion of the trailer features.

Abstract: A controller receives outputs from a plurality of sensors such as a camera, LIDAR sensor, RADAR sensor, and ultrasound sensor, which may be rearward facing. A probability is updated each time a feature in a sensor output indicates presence of an object. The probability may be updated as a function of a variance of the sensor providing the output and a distance to the feature. Where the variance of a sensor is directional, directional probabilities may be updated according to these variances and the distance to the feature. If the probability meets a threshold condition, actions may be taken such as a perceptible alert or automatic braking. The probability may be decayed in the absence of detection of objects. Increasing or decreasing trends in the probability may be amplified by further increasing or decreasing the probability.

Abstract: A controller receives outputs from a plurality of sensors such as a camera, LIDAR sensor, RADAR sensor, and ultrasound sensor, which may be rearward facing. A probability is updated each time a feature in a sensor output indicates presence of an object. The probability may be updated as a function of a variance of the sensor providing the output and a distance to the feature. Where the variance of a sensor is directional, directional probabilities may be updated according to these variances and the distance to the feature. If the probability meets a threshold condition, actions may be taken such as a perceptible alert or automatic braking. The probability may be decayed in the absence of detection of objects. Increasing or decreasing trends in the probability may be amplified by further increasing or decreasing the probability.

Abstract: A hitch assist system and method are provided herein. A human machine interface is configured to receive user-input specifying a target travel direction of a vehicle. A controller is configured to generate commands for autonomously maneuvering the vehicle in the target travel direction, identify a hitch coupler of a trailer based on input received from an object detection system, and request additional user-input updating the target travel direction if the hitch coupler is unable to be identified.

Abstract: A system for detecting and identifying foliage includes a tracking component, a tracking parameters component, and a classification component. The tracking component is configured to detect and track one or more features within range data from one or more sensors. The tracking parameters component is configured to determine tracking parameters for each of the one or more features. The tracking parameters include a tracking age and one or more of a detection consistency and a position variability. The classification component is configured to classify a feature of the one or more features as corresponding to foliage based on the tracking parameters.

Abstract: A scenario is defined that including models of vehicles and a typical driving environment. A model of a subject vehicle is added to the scenario and sensor locations are defined on the subject vehicle. Perception of the scenario by sensors at the sensor locations is simulated to obtain simulated sensor outputs. The simulated sensor outputs are annotated to indicate the location of obstacles in the scenario. The annotated sensor outputs may then be used to validate a statistical model or to train a machine learning model. The simulates sensor outputs may be modeled with sufficient detail to include sensor noise or may include artificially added noise to simulate real world conditions.

Abstract: Example obstacle detection systems and methods are described. In one implementation, a method receives data from at least one sensor mounted to a vehicle and creates a probabilistic grid-based map associated with an area near the vehicle. The method also determines a confidence associated with each probability in the grid-based map and determines a likelihood that an obstacle exists in the area near the vehicle based on the probabilistic grid-based map.

Abstract: A system for detecting and identifying foliage includes a tracking component, a tracking parameters component, and a classification component. The tracking component is configured to detect and track one or more features within range data from one or more sensors. The tracking parameters component is configured to determine tracking parameters for each of the one or more features. The tracking parameters include a tracking age and one or more of a detection consistency and a position variability. The classification component is configured to classify a feature of the one or more features as corresponding to foliage based on the tracking parameters.

Abstract: A controller receives outputs form a plurality of sensors such as a camera, LIDAR sensor, RADAR sensor, and ultrasound sensor. Sensor outputs corresponding to an object are assigned to a tracklet. Subsequent outputs by any of the sensors corresponding to that object are also assigned to the tracklet. A trajectory of the object is calculated from the sensor outputs assigned to the tracklet, such as by means of Kalman filtering. For each sensor output assigned to the tracklet, a probability is updated, such as using a Bayesian probability update. When the probability meets a threshold condition, the object is determined to be present and an alert is generated or autonomous obstacle avoidance is performed with respect to an expected location of the object.

Abstract: Example foliage detection training systems and methods are described. In one implementation, a method receives data associated with a plurality of vehicle-mounted sensors and defines multiple regions of interest (ROIs) based on the received data. The method applies a label to each ROI, where the label classifies a type of foliage associated with the ROI. A foliage detection training system trains a machine learning algorithm based on the ROIs and associated labels.

Abstract: Example obstacle detection systems and methods are described. In one implementation, a method receives data from at least one sensor mounted to a vehicle and creates a probabilistic grid-based map associated with an area near the vehicle. The method also determines a confidence associated with each probability in the grid-based map and determines a likelihood that an obstacle exists in the area near the vehicle based on the probabilistic grid-based map.

Abstract: An autonomous vehicle includes both a LIDAR sensor and a camera. A point cloud from the LIDAR sensor is processed to remove points corresponding to a ground plane and points having a reflectivity below a reflectivity threshold. The remaining points are grouped into clusters. Clusters having points satisfying a flatness threshold are then converted into 2D pixel positions in the output of the camera. Regions of interest including these 2D pixel positions are then analyzed to detect and interpret any road signs present.

Abstract: A scenario is defined that including models of vehicles and a typical driving environment. A model of a subject vehicle is added to the scenario and sensor locations are defined on the subject vehicle. Perception of the scenario by sensors at the sensor locations is simulated to obtain simulated sensor outputs. The simulated sensor outputs are annotated to indicate the location of obstacles in the scenario. The annotated sensor outputs may then be used to validate a statistical model or to train a machine learning model. The simulates sensor outputs may be modeled with sufficient detail to include sensor noise or may include artificially added noise to simulate real world conditions.

Abstract: A controller receives outputs from a plurality of sensors such as a camera, LIDAR sensor, RADAR sensor, and ultrasound sensor, which may be rearward facing. A probability is updated each time a feature in a sensor output indicates presence of an object. The probability may be updated as a function of a variance of the sensor providing the output and a distance to the feature. Where the variance of a sensor is directional, directional probabilities may be updated according to these variances and the distance to the feature. If the probability meets a threshold condition, actions may be taken such as a perceptible alert or automatic braking. The probability may be decayed in the absence of detection of objects. Increasing or decreasing trends in the probability may be amplified by further increasing or decreasing the probability.

Abstract: A system for detecting and identifying foliage includes a tracking component, a tracking parameters component, and a classification component. The tracking component is configured to detect and track one or more features within range data from one or more sensors. The tracking parameters component is configured to determine tracking parameters for each of the one or more features. The tracking parameters include a tracking age and one or more of a detection consistency and a position variability. The classification component is configured to classify a feature of the one or more features as corresponding to foliage based on the tracking parameters.