Abstract: Systems and methods for publishing LIDAR cluster data are described. The vehicle can identify one or more clusters based on data from the LIDAR sensor. The identified one or more clusters can be representative of at least one object in an external environment of the vehicle. Additionally, the vehicle can publish the identified one or more clusters before the LIDAR sensor completes a full 360 degree rotation about the axis of rotation.

Abstract: A device and method for extending an object identification range in a vehicle environment are disclosed. In this regard, the embodiments may operate to solve to a remaining one of a set of pre-defined shape models to an object by successive iterations of point cloud data and associated closing distance to the object. Successive iterations of point cloud data may include receiving, via an object sensor device, the point cloud data, and detecting at least a portion of an object from the point cloud data, which portion is compared with each of a set of pre-defined shape models. The set is updated to include ones comparing favorably with the point cloud data. When the successive iterations solve to a singular remaining one of the set, an iterative match to the object may be based on the singular remaining one of the set of pre-defined shape models to identify the object.

Abstract: Human driving performance can be assessed using an autonomous vehicle. An autonomous vehicle can have a manual operational mode and one or more autonomous operational modes. While the vehicle is operating in the manual operational mode, driving data relating to a manual driving maneuver can be acquired. The acquired driving data can be evaluated relative to a driving scene model to determine whether the manual driving maneuver is acceptable or unacceptable based on the acquired driving data. Responsive to determining that the manual driving maneuver is unacceptable, feedback can be provided to a user. In some instances, the feedback can be active feedback or passive feedback. In some instance, the user can be the human driver of the vehicle, or some other person related to the driver in some manner.

Abstract: System, methods, and other embodiments described herein relate to identifying changes between models of a locality. In one embodiment, a method includes, in response to determining that a location model is available for a present environment of a vehicle, generating a current model of the present environment using at least one sensor of the vehicle. The method also includes isolating dynamic objects in the current model as a function of the location model. The method includes providing the dynamic objects to be identified and labeled.

Abstract: System, methods, and other embodiments described herein relate to associating disparate tracks from multiple sensor inputs for observed objects. In one embodiment, a method includes, in response to receiving a first input from a first sensor and a second input from a second sensor, generating the disparate tracks including first sensor tracks and second sensor tracks for the observed objects that correspond to the first input and the second input. The method includes identifying correlations between the first sensor tracks and the second sensor tracks by computing association likelihoods between the first tracks and the second tracks within a permutation matrix according to an objective cost function. The method includes controlling a vehicle according to the correlations.

Abstract: Arrangements relating to personal storage with shared vehicles are described. A shared vehicle, a user device, a storage compartment, a storage computing system, and/or a storage depot can be communicatively linked. The storage compartment can be configured to store one or more physical items as well as electronic data. The storage compartment can be configured to be selectively loaded within and unloaded from a shared vehicle during a use by a user. The storage compartment can be configured to be communicatively linked to the shared vehicle when in a loaded condition such that a component of the shared vehicle may access the electronic data during the use of the shared vehicle. Systems and methods described herein can be implemented with shared autonomous vehicles.

Abstract: System, methods, and other embodiments described herein relate to identifying surface properties of objects using a light detection and ranging (LIDAR) sensor. In one embodiment, a method includes, in response to scanning a surface of an object using the LIDAR sensor, receiving a reflected waveform as a function of attributes of the surface. The method includes analyzing the reflected waveform according to a surface property model to produce an estimate of the attributes. The surface property model characterizes relationships between reflected waveforms and different surface properties. The method includes providing the estimate as an indication of the surface of the scanned object.

Abstract: System, methods, and other embodiments described herein relate to controlling a vehicle to selectively collect event data. In one embodiment, a method includes, in response to receiving a collection request from a remote server, identifying defined parameters from the collection request about which data is to be harvested from the vehicle. The defined parameters include at least a content parameter that indicates criteria for determining which data associated with the vehicle is to be collected. The method includes collecting, from one or more vehicle systems of the vehicle, event data as a function of the content parameter and discarding extraneous data that does not match the content parameter. The method includes providing the event data to fulfill the collection request.

Abstract: This disclosure describes various embodiments for steering wheel recoupling for an autonomous vehicle. In an embodiment, a steering system is described. The steering system may comprise a steering wheel, wheels controlled by the steering wheel, and a wheel alignment control module. The wheel alignment control module may be configured to determine the steering wheel is decoupled from the steering system, determine a current angle of the wheels controlled by the steering wheel, activate an indicator, the indicator determined based, at least in part, upon the current angle, determine the steering wheel is positioned for recoupling, and recouple the steering wheel to the steering system.

Abstract: A device and method for extending an object identification range in a vehicle environment are disclosed. In this regard, the embodiments may operate to solve to a remaining one of a set of pre-defined shape models to an object by successive iterations of point cloud data and associated closing distance to the object. Successive iterations of point cloud data may include receiving, via an object sensor device, the point cloud data, and detecting at least a portion of an object from the point cloud data, which portion is compared with each of a set of pre-defined shape models. The set is updated to include ones comparing favorably with the point cloud data. When the successive iterations solve to a singular remaining one of the set, an iterative match to the object may based on the singular remaining one of the set of pre-defined shape models to identify the object.

Abstract: Systems and methods for publishing LIDAR cluster data are described. The vehicle can identify one or more clusters based on data from the LIDAR sensor. The identified one or more clusters can be representative of at least one object in an external environment of the vehicle. Additionally, the vehicle can publish the identified one or more clusters before the LIDAR sensor completes a full 360 degree rotation about the axis of rotation.

Abstract: System, methods, and other embodiments described herein relate to identifying changes between models of a locality. In one embodiment, a method includes, in response to determining that a location model is available for a present environment of a vehicle, generating a current model of the present environment using at least one sensor of the vehicle. The method also includes isolating dynamic objects in the current model as a function of the location model. The method includes providing the dynamic objects to be identified and labeled.

Abstract: System, methods, and other embodiments described herein relate to identifying surface properties of objects using a light detection and ranging (LIDAR) sensor. In one embodiment, a method includes, in response to scanning a surface of an object using the LIDAR sensor, receiving a reflected waveform as a function of attributes of the surface. The method includes analyzing the reflected waveform according to a surface property model to produce an estimate of the attributes. The surface property model characterizes relationships between reflected waveforms and different surface properties. The method includes providing the estimate as an indication of the surface of the scanned object.

Abstract: System, methods, and other embodiments described herein relate to controlling a vehicle to selectively collect event data. In one embodiment, a method includes, in response to receiving a collection request from a remote server, identifying defined parameters from the collection request about which data is to be harvested from the vehicle. The defined parameters include at least a content parameter that indicates criteria for determining which data associated with the vehicle is to be collected. The method includes collecting, from one or more vehicle systems of the vehicle, event data as a function of the content parameter and discarding extraneous data that does not match the content parameter. The method includes providing the event data to fulfill the collection request.

Abstract: System, methods, and other embodiments described herein relate to associating disparate tracks from multiple sensor inputs for observed objects. In one embodiment, a method includes, in response to receiving a first input from a first sensor and a second input from a second sensor, generating the disparate tracks including first sensor tracks and second sensor tracks for the observed objects that correspond to the first input and the second input. The method includes identifying correlations between the first sensor tracks and the second sensor tracks by computing association likelihoods between the first tracks and the second tracks within a permutation matrix according to an objective cost function. The method includes controlling a vehicle according to the correlations.

Abstract: This disclosure describes various embodiments for steering wheel recoupling for an autonomous vehicle. In an embodiment, a steering system is described. The steering system may comprise a steering wheel, wheels controlled by the steering wheel, and a wheel alignment control module. The wheel alignment control module may be configured to determine the steering wheel is decoupled from the steering system, determine a current angle of the wheels controlled by the steering wheel, activate an indicator, the indicator determined based, at least in part, upon the current angle, determine the steering wheel is positioned for recoupling, and recouple the steering wheel to the steering system.

Abstract: A method of modeling an intersection structure of a roadway. The method includes receiving a first data set including road lane information, and receiving a second data set including vehicle trajectory information for an intersection structure of a roadway. The method includes determining lane node locations from at least one of the first and second data sets. A set of potential links between the lane node locations may be compiled. The method may further include assessing, for each link, a probability that the link is a valid link, and assigning each link with a probability value. The links may be filtered based on a predetermined threshold probability value and a set of valid links is generated. A model of the intersection structure is created based on the set of valid links.

Abstract: Arrangements relating to personal storage with shared vehicles are described. A shared vehicle, a user device, a storage compartment, a storage computing system, and/or a storage depot can be communicatively linked. The storage compartment can be configured to store one or more physical items as well as electronic data. The storage compartment can be configured to be selectively loaded within and unloaded from a shared vehicle during a use by a user. The storage compartment can be configured to be communicatively linked to the shared vehicle when in a loaded condition such that a component of the shared vehicle may access the electronic data during the use of the shared vehicle. Systems and methods described herein can be implemented with shared autonomous vehicles.

Abstract: Arrangements relating to the transitioning of a vehicle between operational modes are described. The vehicle can transition between a first operational mode and a second operational mode. The second operational mode has a greater degree of manual involvement than the first operational mode. For instance, the first operational mode can be an unmonitored autonomous operational mode, and the second operational mode can be a monitored autonomous operational mode or a manual operational mode. It can be determined whether an operational mode transition event has occurred while the vehicle is operating in the first operational mode. In response to determining that an operational mode transition event has occurred, a time buffer for continuing in the first operational mode before switching to the second operational mode can be determined. A transition alert can be presented within the vehicle. The transition alert can represent the determined time buffer.

Abstract: Arrangements relate to the interaction between an autonomous vehicle and an external environment of the autonomous vehicle. Such interaction can occur in various ways. For example, a non-verbal human gesture in the external environment can be detected. The non-verbal human gesture can be identified. A future driving maneuver can be determined based on the identified non-verbal human gesture. The autonomous vehicle can be caused to implement the determined future driving maneuver. As another example, the external environment of the autonomous vehicle can be detected to identify a person (e.g. a human pedestrian, a human bicyclist, a human driver or occupant of another vehicle, etc.) therein. The identified person can be located. It can be determined whether the person is potentially related to a future driving maneuver of the autonomous vehicle. The autonomous vehicle can be caused to send a directional message to the person.