Vehicles and methods described herein include a vehicle that operates with a rider according to an operating parameter. The vehicle includes: a physiological monitoring sensor configured to measure a physiological parameter of the rider; an experience hybrid neural network trained on outcomes related to a rider in-vehicle experience associated with the physiological parameter to determine an emotional state of the rider; an augmented reality system configured to present augmented reality content to the rider of the vehicle based, at least in part, on the operating parameter; and an optimization hybrid neural network that identifies a variation in the operating parameter to change the emotional state of the rider and that generates a command to vary the operating parameter and the augmented reality content according to the variation.

There is provided an information processing apparatus including a controller that, when an autonomous mobile object estimates a self-position, determines which of a first estimation method using a result of sensing by a first sensor unit configured to sense internal world information in relation to the autonomous mobile object and a second estimation method using a result of sensing by a second sensor unit configured to sense external world information in relation to the autonomous mobile object is used by the autonomous mobile object based on whether a state of the autonomous mobile object is a stopped state.

Apparatus and methods for carpet drift estimation are disclosed. In certain implementations, a robotic device includes an actuator system to move the body across a surface. A first set of sensors can sense an actuation characteristic of the actuator system. For example, the first set of sensors can include odometry sensors for sensing wheel rotations of the actuator system. A second set of sensors can sense a motion characteristic of the body. The first set of sensors may be a different type of sensor than the second set of sensors. A controller can estimate carpet drift based at least on the actuation characteristic sensed by the first set of sensors and the motion characteristic sensed by the second set of sensors.

In one embodiment, a computing system of a vehicle may access sensor data associated with a surrounding environment of a vehicle. The system may generate, based on the sensor data, a first trajectory having one or more first driving characteristics for navigating the vehicle in the surrounding environment. The system may generate a second trajectory having one or more second driving characteristics by modifying the one or more first driving characteristics of the first trajectory. The modifying may use adjustment parameters based on one or more human-driving characteristics of observed human-driven trajectories such that the one or more second driving characteristics satisfy a similarity threshold relative to the one or more human-driving characteristics. The system may determine, based on the second trajectory, vehicle operations to navigate the vehicle in the surrounding environment.

A method for 3D object tracking is described. The method includes inferring first 2D semantic keypoints of a 3D object within a sparsely annotated video stream. The method also includes matching the first 2D semantic keypoints of a current frame with second 2D semantic keypoints in a next frame of the sparsely annotated video stream using embedded descriptors within the current frame and the next frame. The method further includes warping the first 2D semantic keypoints to the second 2D semantic keypoints to form warped 2D semantic keypoints in the next frame. The method also includes labeling a 3D bounding box in the next frame according to the warped 2D semantic keypoints in the next frame.

Systems and methods for path planning with latent state inference and spatial-temporal relationships are provided. 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 also maps the sensor data to a latent state distribution to identify latent states of the plurality of agents. The latent states identify agents of the plurality of 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.

A correction method for a gyro sensor which measures angular velocity of a vehicle about an axis in a vertical direction includes a generation process of performing a process on a difference between a measured azimuth obtained by performing a process on an sensor output, which is an output from the gyro sensor, and a vehicle azimuth estimated by using markers arranged along a traveling road of the vehicle to obtain correction information and a correction process of correcting the measured azimuth obtained by performing the process on the sensor output, which is the output from the gyro sensor, by using the correction information.

A mobile charging apparatus is provided. The apparatus comprises a battery for charging a work machine; a movement unit that causes the mobile charging apparatus to autonomously move; and a controller that executes a program to perform control such that the movement unit moves the mobile charging apparatus to a predetermined position to wait for the work machine.

The present invention discloses a path planning method, including the following steps: establishing an empirical map and a corresponding episodic cognitive map using a RatSLAM algorithm based on an episodic memory model; extracting a road edge in a historical memory image with a Canny operator; performing conversion to a world coordinate system from a pixel coordinate system based on the road edge, and preliminarily judging connectivity according to slope of the road edge; continuously injecting energy into the potential path detection network according to continuous observation of a potential path, so as to further judge the road connectivity; fusing the detected potential path and the original episodic cognitive map, and correspondingly updating the empirical map; and planning a path based on the updated episodic cognitive map. The potential safe path in an environment may be detected, and a better path may be planned based on the updated episodic memory model.

The present invention discloses a path planning method, including the following steps: establishing an empirical map and a corresponding episodic cognitive map using a RatSLAM algorithm based on an episodic memory model; extracting a road edge in a historical memory image with a Canny operator; performing conversion to a world coordinate system from a pixel coordinate system based on the road edge, and preliminarily judging connectivity according to slope of the road edge; continuously injecting energy into the potential path detection network according to continuous observation of a potential path, so as to further judge the road connectivity; fusing the detected potential path and the original episodic cognitive map, and correspondingly updating the empirical map; and planning a path based on the updated episodic cognitive map. The potential safe path in an environment may be detected, and a better path may be planned based on the updated episodic memory model.