Abstract: A picture selection method and an electronic device are provided. The method includes: displaying a first interface in an interaction scenario of a first application, where the first interface includes a picture selection button; and displaying a target album interface in response to a first operation performed by a user to trigger the picture selection button, where the target album interface includes thumbnails of at least two pictures that carry different identifier information or at least two picture sets that carry different identifier information; and where identifier information is generated based on interaction scenarios of at least two applications, and the identifier information includes at least one of an application name of an application, time of an interaction scenario of an application, or a quantity of times in an interaction scenario of an application.

Abstract: A method may include learning reward functions for a plurality of first vehicles based on first vehicle data associated with the plurality of first vehicles using inverse reinforcement learning, associating each vehicle of the plurality of first vehicles with one cluster among a plurality of clusters based on the reward functions, determining a centroid reward function for each of the clusters based on the reward functions associated with each cluster, performing a comparison between second vehicle data associated with a second vehicle and the first vehicle data, determining a vehicle among the plurality of first vehicles having associated first vehicle data that is most similar to the second vehicle data based on the comparison, associating the second vehicle with the cluster associated with the determined vehicle, and controlling operation of the second vehicle based on the centroid reward function of the cluster associated with the second vehicle.

Abstract: A method may include receiving training data comprising a time series of gaps between an ego vehicle and one or more lead vehicles at a plurality of time steps, embedding the training data into a fixed-length sequence, inputting the fixed-length sequence into a Transformer-RNN model comprising a Transformer component and an RNN component, wherein the transformer component applies attention to each data point of the fixed-length sequence based on a fixed number of previous inputs, and training the Transformer-RNN model, using the training data, to output a predicted gap at a future time step based on an input sequence of gaps.

Abstract: A hybrid deterministic override to cloud based probabilistic advanced driver assistance systems. Under default driving conditions, an ego vehicle is controlled by a probabilistic controller in a cloud. An overall gap between the ego vehicle and a leading vehicle is divided into an emergency collision gap and a driver specified gap. The vehicle sensors monitor the overall gap. When the gap between the ego vehicle and the leading vehicle is less than or equal to the emergency collision gap, a deterministic controller of the ego vehicle overrides the cloud based probabilistic controller to control the braking and acceleration of the ego vehicle.

Abstract: A cooperative driving system includes a processor and a memory having one or more modules. The modules cause the processor to determine a route that includes a road segment to a destination for an ego vehicle based on (1) an ego vehicle operating parameter that has information regarding a preferred operation of an ego vehicle by an occupant, and (2) a location of a like vehicle with respect to the ego vehicle when the ego vehicle is traveling on at least a portion of the route. The one or more modules also cause the processor to determine a road-segment-level decision for the ego vehicle for the road segment based on the ego vehicle operating parameter, communicate with the like vehicle near the road segment to negotiate the road-segment-level decision to generate a negotiated road-segment-level decision, and maneuver the ego vehicle inside the road segment using the negotiated road-segment-level decision.

Abstract: The disclosure generally includes systems and methods of generating a preferred personalized adaptive cruise control (P-ACC) mode of operation. The method includes capturing vehicle data using one or more internal and external vehicle sensors, and adjusting one or more P-ACC mode of operation parameters, based on captured vehicle data, before operation of the P-ACC mode of operation. Vehicle data includes internal vehicle data comprising a type of passenger and number of passengers in the vehicle.

Abstract: Methods, vehicles, and systems described herein generate alerts based on a generated macro state level indicative of a traffic hazard risk in view of a set of parameters which may lead driver to make various sub-conscious decisions. The set of parameters can include at least one of a vehicle parameter, driver state parameter, or external parameter.

Abstract: Based on a model and an identity of an operator of a vehicle, a setting for a cruise control to maintain a distance between the vehicle and a preceding vehicle can be determined. The model can be based on historical distances maintained by the operator. The model can also be based on information about an environment in which the vehicle is operating or information about a state of the operator. The identity can be received from an interface configured to receive information associated with the identity, a cloud computing platform, or a biometric system configured to determine the identity. The cruise control can be caused to operate according to the setting. A rate of energy consumption by the vehicle when the vehicle is controlled by the cruise control can be less than the rate of energy consumption when the vehicle lacks being controlled by the cruise control.

Abstract: Systems, methods, and other embodiments described herein relate to generating a notification about a risk of a target vehicle toppling over due to wind. In one embodiment, a method includes determining a wind force of the wind at a location of the target vehicle and determining one or more characteristics of the target vehicle. The method includes determining whether there is a risk of the target vehicle toppling over based on the wind force of the wind and the one or more characteristics of the target vehicle. The method includes generating a notification about the risk.

Abstract: Systems and methods for protecting a vehicle at an intersection are disclosed herein. One embodiment detects that the vehicle is stopped at the intersection at one of a first position and a second position; detects that a driver of the vehicle is pressing on an accelerator pedal of the vehicle; delays acceleration of the vehicle automatically by a first predetermined period, when the vehicle has been detected at the first position; and delays acceleration of the vehicle automatically by a second predetermined period, when the vehicle has been detected at the second position. Delaying acceleration of the vehicle automatically prevents the vehicle from being struck by a cross-traffic vehicle that has proceeded through the intersection against a first traffic signal in a red-light state.

Abstract: System, methods, and other embodiments described herein relate to a control system to improve estimating motion for an automated vehicle related to cooperative driving. In one embodiment, a method includes monitoring, by an ego vehicle, a communication link for motion data of a target vehicle, used in a model to determine motion, for motion planning by the ego vehicle. The method also includes estimating trajectory of the target vehicle according to a motion estimate by the model when criteria for the communication link are unsatisfied. The method also includes adjusting the motion estimate using a modified model, adapted for speed of the target vehicle, when the criteria for the communication link are satisfied. The method also includes controlling the ego vehicle according to the trajectory and the motion estimate.

Abstract: Systems and methods are provided for effectuating and using a mobility digital twin (MDT) framework including a digital space (where digital twins reside) and a physical space (where physical objects/processes reside). The MDT framework is realized in a cloud-based system/on a cloud platform. Digital twins may represent not only vehicular entities, but human and traffic entities as data/models representative of these different physical objects/processes may be applicable to more than just a directly-related entity. Additionally, the MDT framework is able to leverage data associated with different time horizons (e.g., real-time data as well as historical data).

Abstract: The disclosure includes embodiments for a medic system to respond to a medical condition of a vehicle occupant. A method according to some embodiments is executed by a processor. The method also includes determining, by the processor, that a driver of an ego vehicle is experiencing a debilitating medical condition. The method also includes overriding a protocol to decrease an autonomy level of the ego vehicle responsive to inattentiveness of the driver to a driving interface of the ego vehicle so that the driver can be inattentive to the driving interface and the autonomy level is not decreased. The method also includes modifying an operation of an autonomous driving system of the ego vehicle to increase the autonomy level of the ego vehicle to decrease a driving responsibility of the driver responsive to the debilitating medical condition.

Abstract: Systems and methods for personalizing adaptive cruise control in a vehicle are disclosed herein. One embodiment collects vehicle-following-behavior data associated with a particular driver; trains a Gaussian Process (GP) Regression model using the collected vehicle-following-behavior data to produce a set of adaptive-cruise-control (ACC) parameters pertaining to the particular driver, the set of ACC parameters modeling learned vehicle-following behavior of the particular driver; generates an acceleration command for the vehicle based, at least in part, on the set of ACC parameters; applies a predictive safety filter to the acceleration command to produce a certified acceleration command that has been vetted for safety; and controls acceleration of the vehicle automatically in accordance with the certified acceleration command to regulate a following distance between a lead vehicle and the vehicle in accordance with the learned vehicle-following behavior of the particular driver.

Abstract: A learning-based lane change prediction algorithm, and systems and methods for implementing the algorithm, are disclosed. The prediction algorithm evaluates the driving behaviors of a target human driver and predicts lane change maneuvers based on those personalized driving behaviors. The algorithm may include an online lane change decision prediction phase and an offline prediction training and cost function recovery phase. During the offline training phase, a machine learning model may be trained based on historical vehicle states. During the online validation phase, driving data may be collected and fed to the trained model to predict a driver's lane change maneuver, identify potential vehicle trajectories, and determine a most probable vehicle trajectory based on a driver's cost function recovered during the offline phase.

Abstract: Driver classification systems and methods are disclosed herein. The driver classification method includes collecting first vehicle driving data from a first vehicle, processing the first vehicle driving data using a driver classification learning model including a machine learning algorithm at one of an edge server and the first vehicle to assign a driver classification to the first vehicle, updating the driver classification learning model based on additional driver classification learning models received from a plurality of additional vehicles, sending the driver classification to an insurance provider, receiving an insurance rate for the first vehicle from the insurance provider based on the driver classification of the first vehicle, and providing the insurance rate to the first vehicle.

Abstract: A system including sensors and a controller for cooperative escape zone detection for a group of vehicles are provided. The sensors obtain driving condition information indicating driving environments and vehicle conditions for the group of vehicles. For each vehicle, the controller determines, based on the driving environment of the vehicle, one or more distances associated with the vehicle that are between the vehicle and one or more obstacles that surround the vehicle and determines an escape zone status for the vehicle based on the one or more distances and the driving environment and the vehicle condition of the vehicle. When the escape zone status of one in the group of vehicles fails to satisfy a pre-defined condition, the controller sends one or more control signals to one or more vehicles in the group of vehicles to create an additional escape zone for the one in the group of vehicles.

Abstract: Systems and methods are provided for predictive assessment of driver perception abilities based on driving behavior personalized to the driver in connection with, but not necessarily, autonomous and semi-autonomous vehicles. In accordance with on embodiment, a method comprises receiving first vehicle operating data and associated first gaze data of a driver operating a vehicle; training a model for the driver based on the first vehicle operating data and the first gaze data, the model indicating driving behavior of the driver; receiving second vehicle operating data and associated second gaze data of the driver; and determining that an ability of the driver to perceive hazards is impaired based on applying the model to the second vehicle operating data and associated second gaze data.

Abstract: A personalized adaptive cruise control (P-ACC) system and associated algorithm are disclosed for determining a driver's preferred following gap in relation to vehicle speed based on periods of steady-state operation of a vehicle. While the P-ACC system is activated, vehicle transition states initiated by driver manual interventions such as takeover or overwrite events are used to identify subsequent periods of vehicle steady-state operation. Vehicle dynamics data captured during periods of steady-state operation is stored as steady-state data, which is then used to train a machine learning model to learn the driver's preferred following gap. This learned relationship is fed into second-order vehicle dynamics to determine a target acceleration for achieving the desired following gap while the P-ACC system is activated. Upon achieving the desired following gap, the vehicle speed may be held constant to maintain the following gap unless a change in lead vehicle speed necessitates updating the following gap.

Abstract: The disclosed technologies relate to reducing audible distractions for a driver of a vehicle. A method includes obtaining audio data based on sound detected inside the vehicle, identifying an audio event based on the audio data, determining a distraction rating for the audio event, the distraction rating indicating an estimated level of distraction caused by the audio event, and generating an alert when the distraction rating exceeds a threshold.