Abstract: A supervisory control system is disclosed that provides an operator situational awareness interface use with monitoring a plurality of automated vehicles (AVs). The system is configured to: generate a map of a geographical area of interest; obtain location data and perceived risk data for a plurality of AVs in the geographical area; generate a vehicle icon corresponding to each AV; position the vehicle icon for each AV on the map based on the location data for a corresponding AV; apply a color coding to each vehicle icon based on a perceived risk level for a corresponding AV; and signal a display device to display an AV fleet map graphic that includes the color coded vehicle icons positioned on the map. The controller may be further configured to: generate an AV servicing queue graphic that displays vehicle icons in an order based on a determined servicing priority.

Abstract: Systems and methods of detecting a traffic object outside of a vehicle and controlling the vehicle. The systems and methods receive perception data from a sensor system included in the vehicle, determine a focused Region Of Interest (ROI) in the perception data, scale the perception data of the at least one focused ROI, process the scaled perception data of the focused ROI using a neural network (NN)-based traffic object detection algorithm to provide traffic object detection data, and control at least one vehicle feature based, in part, on the traffic object detection data.

Abstract: Systems and method are provided for coordinating remote control of an autonomous vehicle by a remote transportation system. In one embodiment, a method includes: receiving, by a processor of the remote transportation system, request data requesting intervention of control of the autonomous vehicle; selecting, by the processor, an operator from a plurality of available operators; selectively assigning, by the processor, the selected operator to a task associated with the intervention based on at least one of a skill level of the selected operator, a workload of the selected operator, and an economic element of the ride associated with the selected operator; and coordinating, by the processor, control of the autonomous vehicle based on the assigned operator.

Abstract: Systems and method are provided for requesting remote control of an autonomous vehicle by a remote transportation system. In one embodiment, a method includes: receiving, by a processor of the autonomous vehicle, experience data associated with the autonomous vehicle, wherein the experience data includes a location of the autonomous vehicle, a time of day, a pose of the autonomous vehicle, detected freespace in an environment of the autonomous vehicle, detected congestion in the environment, a planned maneuver type, and an associated maneuver graph; determining, by the processor, one or more features of a planned maneuver based on the experience data; determining, by the processor, a risk value associated with the planned mission by processing the one or more features with a machine learning model; and selectively generating, by the processor, request data to the remote transportation system based on the risk value, wherein the request data includes the risk value.

Abstract: Systems and methods of detecting a traffic object outside of a vehicle and controlling the vehicle. The systems and methods receive perception data from a sensor system included in the vehicle, determine a focused Region Of Interest (ROI) in the perception data, scale the perception data of the at least one focused ROI, process the scaled perception data of the focused ROI using a neural network (NN)-based traffic object detection algorithm to provide traffic object detection data, and control at least one vehicle feature based, in part, on the traffic object detection data.

Abstract: A method for providing a situational awareness interface for an occupant of an automated vehicle is provided. The method includes: generating a vehicle route graphic that displays a street level map of an area that includes a current location of the vehicle and an intended destination of the vehicle along with a visual depiction of a planned route and one or more alternate routes for the vehicle; determining a probability of requiring assistance or interaction and an estimated time of arrival for each of the planned route and the one or more alternate routes; providing a visual indication of the probability of requiring assistance or interaction and the estimated time of arrival for each of the planned route and the one or more alternate routes on the vehicle route graphic; and signaling a display device to display the vehicle route graphic.

Abstract: A supervisory control system is disclosed that provides an operator situational awareness interface use with monitoring a plurality of automated vehicles (AVs). The system is configured to: generate a map of a geographical area of interest; obtain location data and perceived risk data for a plurality of AVs in the geographical area; generate a vehicle icon corresponding to each AV; position the vehicle icon for each AV on the map based on the location data for a corresponding AV; apply a color coding to each vehicle icon based on a perceived risk level for a corresponding AV; and signal a display device to display an AV fleet map graphic that includes the color coded vehicle icons positioned on the map. The controller may be further configured to: generate an AV servicing queue graphic that displays vehicle icons in an order based on a determined servicing priority.

Abstract: A method for reducing parking violations includes: searching for an empty parking spot in an area surrounding a vehicle; receiving, by a controller of the vehicle, parking restriction information in the area surrounding the vehicle, wherein the controller receives the parking restriction information from sensors of the vehicle; determining, by the controller of the vehicle, that the empty parking spot is invalid; and activating, by the controller of the vehicle, an alarm to alert a vehicle operator of the vehicle that the empty parking spot is invalid.

Abstract: Systems and method are provided for controlling a sensor of a vehicle. In one embodiment, a method includes: receiving depth image data from the sensor of the vehicle; computing, by a processor, an aleatoric variance value based on the depth image data; dividing, by the processor, the depth image data into grid cells; computing, by the processor, a confidence bound value for each grid cell based on the depth image data; computing, by the processor, an uncertainty value for each grid cell based on the confidence bound value of the grid cell and the aleatoric variance value; and controlling, by the processor, the sensor based on the uncertainty values.

Abstract: A system and method for monitoring a road segment includes determining a geographic position of a vehicle in context of a digitized roadway map. A perceived point cloud and a mapped point cloud associated with the road segment are determined. An error vector is determined based upon a transformation between the mapped point cloud and the perceived point cloud. A first confidence interval is derived from a Gaussian process that is composed from past observations. A second confidence interval associated with a longitudinal dimension and a third confidence interval associated with a lateral dimension are determined based upon the mapped point cloud and the perceived point cloud. A Kalman filter analysis is executed to dynamically determine a position of the vehicle relative to the roadway map based upon the error vector, the first confidence interval, the second confidence interval, and the third confidence interval.

Abstract: A system for generating a map includes a processing device configured to determine a provenance of each received map of a plurality of maps, parse each received map into objects of interest, and compare the objects of interest to identify one or more sets of objects representing a common feature. For each set of objects, the processing device is configured to select a subset of the set of objects based on the provenance associated with each object in the set of objects, and calculate a similarity metric for each object in the subset. The similarity metric is selected from an alignment between an object and a reference object in the subset, and/or a positional relationship between the object and the reference object. The processing device is configured to generate a common object representing the common feature based on the similarity metric, and generate a merged map including the common object.

Abstract: Methods and systems are provided for detecting objects within an environment of a vehicle. In one embodiment, a method includes: receiving, by a processor, image data sensed from the environment of the vehicle; determining, by a processor, an area within the image data that object identification is uncertain; controlling, by the processor, a position of a lighting device to illuminate a location in the environment of the vehicle, wherein the location is associated with the area; controlling, by the processor, a position of one or more sensors to obtain sensor data from the location of the environment of the vehicle while the lighting device is illuminating the location; identifying, by the processor, one or more objects from the sensor data; and controlling, by the processor, the vehicle based on the one or more objects.

Abstract: The vehicle mounted perception sensor gathers environment perception data from a scene using first and second heterogeneous (different modality) sensors, at least one of the heterogeneous sensors is directable to a predetermined region of interest. A perception processor receives the environment perception data and performs object recognition to identify objects each with a computed confidence score. The processor assesses the confidence score vis-à-vis a predetermined threshold, and based on that assessment, generates an attention signal to redirect the one of the heterogeneous sensors to a region of interest identified by the other heterogeneous sensor. In this way information from one sensor primes the other sensor to increase accuracy and provide deeper knowledge about the scene and thus do a better job of object tracking in vehicular applications.

Abstract: Systems and methods are provided for interpreting traffic information by a vehicle. In one embodiment, a method includes: receiving, by a processor, sensor data from one or more sensing devices of the vehicle, where the sensor data depicts a traffic device in an environment of the vehicle; receiving, by the processor, map data associated with the environment of the vehicle, where the map data includes traffic devices; matching, by the processor, the traffic device of the sensor data with a traffic device of the map data; determining, by the processor, a probability distribution of a traffic signal associated with the matched traffic devices based on a Hidden Markov model (HMM); and planning, by the processor, control of the vehicle based on the probability distribution.

Abstract: A control system for a subject vehicle includes an autonomous braking system, a forward monitoring sensor and a rearward monitoring sensor. The controller monitors a first speed of a first vehicle travelling in front of the subject vehicle and a second speed of a second vehicle travelling to the rear of the subject vehicle. A first gap-closing time is determined based upon the speed of the subject vehicle and the first speed of the first vehicle. A second gap-closing time is determined based upon the speed of the subject vehicle and the second speed of the second vehicle. The controller controls the speed of the subject vehicle based upon the first gap-closing time and the second gap-closing time when one of the first gap-closing time or the second gap-closing time is less than a first threshold time.

Abstract: A risk maneuver assessment system and method to generate a perception of an environment of a vehicle and a behavior decision making model for the vehicle; a sensor system configured to provide the sensor input in the environment for filtering target objects; one or more modules configured to map and track target objects to make a candidate detection from multiple candidate detections of a true candidate detection as the tracked target object; apply a Markov Random Field (MRF) algorithm for recognizing a current situation of the vehicle and predict a risk of executing a planned vehicle maneuver at the true detection of the dynamically tracked target; apply mapping functions to sensed data of the environment for configuring a machine learning model of decision making behavior of the vehicle; and apply adaptive threshold to cells of an occupancy grid for representing an area of tracking of objects within the vehicle environment.

Abstract: A control system for a subject vehicle includes an autonomous braking system, a forward monitoring sensor and a rearward monitoring sensor. The controller monitors a first speed of a first vehicle travelling in front of the subject vehicle and a second speed of a second vehicle travelling to the rear of the subject vehicle. A first gap-closing time is determined based upon the speed of the subject vehicle and the first speed of the first vehicle. A second gap-closing time is determined based upon the speed of the subject vehicle and the second speed of the second vehicle. The controller controls the speed of the subject vehicle based upon the first gap-closing time and the second gap-closing time when one of the first gap-closing time or the second gap-closing time is less than a first threshold time.

Abstract: An exemplary automotive vehicle includes a first actuator configured to control acceleration and braking of the automotive vehicle, a second actuator configured to control steering of the automotive vehicle, a vehicle sensor configured to generate data regarding the presence, location, classification, and path of detected features in a vicinity of the automotive vehicle and a controller in communication with the vehicle sensor, and the first and second actuators. The controller is configured to selectively control the first and second actuators in an autonomous mode along a first trajectory according to an automated driving system. The controller is also configured to receive the data regarding the detected features from the vehicle sensor, determine a predicted vehicle maneuver from the data regarding the detected features, map the predicted vehicle maneuver with an indication symbol, and generate a control signal to display the indication symbol.

Abstract: A system and method for monitoring a road segment includes determining a geographic position of a vehicle in context of a digitized roadway map. A perceived point cloud and a mapped point cloud associated with the road segment are determined. An error vector is determined based upon a transformation between the mapped point cloud and the perceived point cloud. A first confidence interval is derived from a Gaussian process that is composed from past observations. A second confidence interval associated with a longitudinal dimension and a third confidence interval associated with a lateral dimension are determined based upon the mapped point cloud and the perceived point cloud. A Kalman filter analysis is executed to dynamically determine a position of the vehicle relative to the roadway map based upon the error vector, the first confidence interval, the second confidence interval, and the third confidence interval.

Abstract: A method for vehicle tracking and localization includes receiving, by a controller, odometry data from a sensor of the first vehicle; geospatial data from a Global Positioning System (GPS) device of the first vehicle; inertial data from an inertial measurement unit (IMU) of the first vehicle; estimating an estimated-current location of the first vehicle and an estimated-current trajectory of the first vehicle using the odometry data from the sensor, the geospatial data from the GPS device, and the inertial data from the IMU of the first vehicle; inputting the inertial data into a Bayesian Network to determine a predicted location of the first vehicle and a predicted trajectory of the first vehicle, and updating the Bayesian Network using the estimated-current location and the estimated-current trajectory of the first vehicle using the odometry data and the geospatial data.