A computer-implemented method of using telematics data associated with an originating vehicle at a destination vehicle is provided. The method may include receiving telematics data associated with the originating vehicle by (1) a mobile device or (2) a smart vehicle controller associated with a driver or vehicle. The mobile device or smart vehicle controller may analyze the telematics data received to determine that (i) a travel event exists, or (ii) that a travel event message or warning is embedded within the telematics broadcast received. If the travel event exits, the method may include automatically taking a preventive or corrective action, at or via the mobile device or smart vehicle controller, which alleviates a negative impact of the travel event on the driver or vehicle to facilitate safer or more efficient vehicle travel. Insurance discounts may be provided to insureds based upon their usage of the risk mitigation or prevention functionality.

The present disclosure discloses a method and system for assisting a robot to navigate in a warehouse setting. In some embodiments, robot is configured to receive a reserved route and move according to the reserved route as it navigates inside a warehouse. A reserved route includes one or more waypoints. Each waypoint represents a location and a size, the size being the space needed for accommodating a robot at the location. The one or more waypoints are listed in a sequence for a robot to follow sequentially. In some embodiments, each waypoint may further include a timestamp representing the time that the robot given the reserved route is supposed to arrive at the location.

A transportation system includes an artificial intelligence system for processing a sensory input from a wearable device in a self-driving vehicle to determine an emotional state of a rider and optimizing a vehicle operating parameter to improve the rider emotional state. The artificial intelligence system detects the rider emotional state in the self-driving vehicle by recognition of patterns of emotional state indicative data from a set of wearable sensors worn by the rider. The patterns are indicative of at least one of a favorable emotional state and an unfavorable emotional state of the rider. The artificial intelligence system is to optimize, for achieving at least one of maintaining a detected favorable emotional state of the rider and achieving a favorable emotional state of a rider subsequent to a detection of an unfavorable emotional state, the operating parameter of the vehicle in response to the detected emotional state of the rider.

A vehicle includes a display disposed to facilitate presenting an augmentation of content in an environment of a rider of the vehicle; a circuit for registering at least one of location and orientation of the vehicle; a machine learning circuit that determines at least one augmentation parameter by processing at least one input relating to at least one of the rider and the vehicle; and a reality augmentation circuit that, responsive to the at least one of the location or the orientation of the vehicle, generates an augmentation element for presenting in the display, the generating based at least in part on the at least one augmentation parameter.

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

In some embodiments, apparatuses and methods are provided herein useful to delivering merchandise using autonomous ground vehicles (AGVs) in cooperation with unmanned aerial vehicles (UAVs). In some embodiments, the system includes: an AGV having a motorized locomotion system, a storage area to hold merchandise, a sensor to detect obstacles, a transceiver, and a control circuit to operate the AGV; a UAV having a motorized flight system, a gripper mechanism to grab merchandise, a transceiver, an optical sensor to capture images; and a control circuit to operate the UAV. In some forms, the system may include a control circuit that instructs movement of the AGV along a delivery route; determines if the AGV has stopped due to an obstacle; and in certain circumstances, instructs the UAV to retrieve merchandise from the AGV, calculates a delivery route for the UAV to the delivery location, and instructs the UAV to deliver the merchandise.

A transportation system includes an artificial intelligence (AI) system for processing a sensory input from a wearable device in a self-driving vehicle to determine an emotional state of a rider and optimizing a vehicle operating parameter to improve the rider emotional state. The AI system includes a recurrent neural network to indicate a change in the emotional state of the rider by a recognition of patterns of emotional state indicative wearable sensor data from a set of wearable sensors worn by the rider. The patterns are indicative of a first degree of a favorable emotional state of the rider and/or a second degree of an unfavorable emotional state of the rider. The AI system further includes a radial basis function neural network to optimize, for achieving a target emotional state of the rider, the vehicle operating parameter in response to the indication of the change in the rider emotional state.

Systems and methods for path planning with latent state inference and spatial-temporal relationships are provided. In one embodiment, a system includes an inference module, a policy module, a graphical representation module, and a planning module. The inference module receives sensor data associated with a plurality of agents. The inference module maps the sensor data to a latent state distribution to identify latent states of the plurality of agents. The latent states identify agents as cooperative or aggressive. The policy module predicts future trajectories of the plurality of agents at a given time based on sensor data and the latent states of the plurality of agents. The graphical representation module generates a graphical representation based on the sensor data and a graphical representation neural network. The planning module generates a motion plan for the ego agent based on the predicted future trajectories and the graphical representation.

In one embodiment, a method includes, by a computing system associated with a vehicle, determining a current location of the vehicle in a first region, identifying one or more first sets of model parameters associated with the first region and one or more second sets of model parameters associated with a second region, generating, using one or more machine-learning models based on the first sets of model parameters, one or more first inferences based on first sensor data captured by the vehicle, switching the configurations of the models from the first sets of model parameters to the second sets of model parameters, generating, using the models having configurations based on the second sets of model parameters, one or more second inferences based on second sensor data generated by the sensors of the vehicle in the second region, and causing the vehicle to perform one or more operations based on the second inferences.

Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.