Abstract: A method for identifying distracted pedestrians. The method includes determining operating conditions of a vehicle using a plurality of vehicle controllers. Pedestrian parameters for a pedestrian in a vicinity of the vehicle are acquired using a plurality of vehicle sensors. The pedestrian parameters include at least one of face positions, body positions, gait and hand gestures. Information related to an environment surrounding the vehicle is acquired. Pedestrian awareness level is determined based on the acquired pedestrian parameters and based on the information related to the environment surrounding the vehicle. A determination is made whether the pedestrian awareness level is below a predefined threshold. The pedestrian is classified as distracted, in response to determining that the pedestrian awareness level is below the predefined threshold.

Abstract: A method for improving pedestrian safety is provided. The method includes determining operating conditions of a vehicle using a plurality of vehicle sensors. A path of the vehicle is predicted based on the determined operating conditions. Pedestrian parameters for a pedestrian in a vicinity of the vehicle are acquired using the plurality of vehicle sensors. The pedestrian parameters include at least one of a position, a speed of the pedestrian, gait, body posture and a level of distractedness. A determination is made whether a notification of the pedestrian is necessary based on the determined vehicle operating conditions and the acquired pedestrian parameters. A mode of notification of the pedestrian is selected from a plurality of modes of notification, in response to determining that the notification of the pedestrian is necessary. The pedestrian is notified using the selected mode of notification.

Abstract: A method for optimal notification of a relevant object in a potentially unsafe situation includes training a machine learning model using a plurality of object parameters and a plurality of vehicle state parameters to generate a trained machine learning model. Output data is predicted using the trained machine learning model. The output data represents an optimal mode of notification and a set of notification parameters for a specific state of interaction between the vehicle and the relevant object.

Abstract: A method for identifying distracted pedestrians. The method includes determining operating conditions of a vehicle using a plurality of vehicle controllers. Pedestrian parameters for a pedestrian in a vicinity of the vehicle are acquired using a plurality of vehicle sensors. The pedestrian parameters include at least one of face positions, body positions, gait and hand gestures. Information related to an environment surrounding the vehicle is acquired. Pedestrian awareness level is determined based on the acquired pedestrian parameters and based on the information related to the environment surrounding the vehicle. A determination is made whether the pedestrian awareness level is below a predefined threshold. The pedestrian is classified as distracted, in response to determining that the pedestrian awareness level is below the predefined threshold.

Abstract: A method for improving pedestrian safety is provided. The method includes determining operating conditions of a vehicle using a plurality of vehicle sensors. A path of the vehicle is predicted based on the determined operating conditions. Pedestrian parameters for a pedestrian in a vicinity of the vehicle are acquired using the plurality of vehicle sensors. The pedestrian parameters include at least one of a position, a speed of the pedestrian, gait, body posture and a level of distractedness. A determination is made whether a notification of the pedestrian is necessary based on the determined vehicle operating conditions and the acquired pedestrian parameters. A mode of notification of the pedestrian is selected from a plurality of modes of notification, in response to determining that the notification of the pedestrian is necessary. The pedestrian is notified using the selected mode of notification.

Abstract: A vehicle information system for projecting images with respect to a vehicle is provided. The vehicle information system includes an image projection device disposed on the vehicle. The image projection device is operable to project an image on a surface with respect to the vehicle. The vehicle information system also includes a vehicle-based portion that receives information regarding a status of the vehicle and determines whether the projecting images is acceptable. The vehicle-based portion also causes the image projection device to project the image on the surface with respect to the vehicle when projecting images is acceptable.

Abstract: A method and system that distribute driving resources are provided. The method includes: receiving driving and situation resource information of a host vehicle, comparing the received driving and situation resource information with occupant preference information to determine whether there is a deficiency of driving resources, a surplus of driving resources, or that the driving and situation resources meet the occupant preferences, offering a surplus resource to target vehicles if it is determined there is a surplus of driving resources, and making an offer for a resource corresponding to deficient resources of the host vehicle if it is determined there is a deficiency of driving resources.

Abstract: Systems, Apparatuses and Methods for implementing a neural network system for controlling an autonomous vehicle (AV) are provided, which includes: a neural network having a plurality of nodes with context to vector (context2vec) contextual embeddings to enable operations of the AV; a plurality of encoded context2vec AV words in a sequence of timing to embed data of context and behavior; a set of inputs which comprise: at least one of a current, a prior, and a subsequent encoded context2vec AV word; a neural network solution applied by the at least one computer to determine a target context2vec AV word of each set of the inputs based on the current context2vec AV word; an output vector computed by the neural network that represents the embedded distributional one-hot scheme of the input encoded context2vec AV word; and a set of behavior control operations for controlling a behavior of the AV.

Abstract: Systems and methods are provided for controlling a vehicle. Control signals are generated at a high-level controller based on one or more sources of input data, comprising at least one of: sensors that provide sensor output information, map data and goals. The high-level controller comprises first controller modules comprising: an input processing module, a projection module, a memories module, a world model module, and a decision processing module that comprises a control model executor module. The control signals are processed at a low-level controller to generate commands that control a plurality of vehicle actuators of the vehicle in accordance with the control signals to execute one or more scheduled actions to be performed to automate driving tasks.

Abstract: Systems, Apparatuses and Methods for implementing a neural network system for controlling an autonomous vehicle (AV) are provided, which includes: a neural network having a plurality of nodes with context to vector (context2vec) contextual embeddings to enable operations of the of the AV; a plurality of encoded context2vec AV words in a sequence of timing to embed data of context and behavior; a set of inputs which comprise: at least one of a current, a prior, and a subsequent encoded context2vec AV word; a neural network solution applied by the at least one computer to determine a target context2vec AV word of each set of the inputs based on the current context2vec AV word; an output vector computed by the neural network that represents the embedded distributional one-hot scheme of the input encoded context2vec AV word; and a set of behavior control operations for controlling a behavior of the AV.

Abstract: Presented are scenario-planning and route-generating distributed computing systems, methods for operating/constructing such systems, and vehicles with scenario-plan selection and real-time trajectory planning capabilities. A method for controlling operation of a motor vehicle includes determining vehicle state data, such as a current position and velocity of the vehicle, and path plan data, such as an origin and desired destination of the vehicle. A remote computing node off-board from the motor vehicle generates a list of trajectory plan candidates based on the vehicle state data, the path plan data, and current road scenario data. The remote computing node then calculates a respective travel cost for each candidate in the trajectory plan candidates list, and sorts the list from lowest to highest travel cost. The candidate with the lowest travel cost is transmitted to a resident vehicle controller. The vehicle controller executes an automated driving operation based on the received trajectory plan candidate.

Abstract: A vehicle, system and method of autonomous navigation of the vehicle. A reference trajectory for navigating a training traffic scenario along a road section is received at a processor of the vehicle. The processor determines a coefficient for a cost function associated with a candidate trajectory that simulates the reference trajectory. The determined coefficient is provided to a neural network to train the neural network. The trained neural network generates a navigation trajectory for navigating the vehicle using a cost coefficient determined by the neural network. The vehicle is navigated along the road section using the navigation trajectory.

Abstract: Systems and methods are provided for controlling a vehicle. Control signals are generated at a high-level controller based on one or more sources of input data, comprising at least one of: sensors that provide sensor output information, map data and goals. The high-level controller comprises first controller modules comprising: an input processing module, a projection module, a memories module, a world model module, and a decision processing module that comprises a control model executor module. The control signals are processed at a low-level controller to generate commands that control a plurality of vehicle actuators of the vehicle in accordance with the control signals to execute one or more scheduled actions to be performed to automate driving tasks.