The technology relates to assisting large self-driving vehicles, such as cargo vehicles, as they maneuver towards and/or park at a destination facility. This may include a given vehicle transitioning between different autonomous driving modes. Such a vehicles may be permitted to drive in a fully autonomous mode on certain roadways for the majority of a trip, but may need to change to a partially autonomous mode on other roadways or when entering or leaving a destination facility such as a warehouse, depot or service center. Large vehicles such as cargo truck may have limited room to maneuver in and park at the destination, which may also prevent operation in a fully autonomous mode. Here, information from the destination facility and/or a remote assistance service can be employed to aid in real-time semi-autonomous maneuvering.

The present disclosure relates to a system and a method for addressing an error in a localization system that includes monitoring a plurality of sensors of a driver assistance system in real-time, with each sensor generating a data stream. The method further includes identifying a sensor having an anomalous data stream and calculating a primary localization and a backup localization. The primary localization calculation includes the anomalous data stream and the backup localization calculation does not include the anomalous data stream. Further, the method includes executing an action when the backup localization error estimate exceeds a threshold.

The present disclosure relates to a system and a method for addressing an error in a localization system that includes monitoring a plurality of sensors of a driver assistance system in real-time, with each sensor generating a data stream. The method further includes identifying a sensor having an anomalous data stream and calculating a primary localization and a backup localization. The primary localization calculation includes the anomalous data stream and the backup localization calculation does not include the anomalous data stream. Further, the method includes executing an action when the backup localization error estimate exceeds a threshold.

An automatic traveling vehicle includes a vehicle structure including a first top plate, and an electronic control unit. The electronic control unit is configured to execute a storage mode when a storage execution condition is satisfied. The storage mode includes a storage posture formation process of causing the vehicle to automatically travel so as to take a predetermined storage posture together with a counterpart automatic traveling vehicle. In the storage posture, the vehicle is in a superposition state in which the vehicle overlaps with the counterpart vehicle in a plan view, or a parallel state in which the vehicle is lined up with the counterpart vehicle while the first top plate and a second top plate of the counterpart vehicle are standing and facing each other so as to be parallel to or substantially parallel to a vertical direction.

A method for constraining robot autonomy language includes receiving a navigation command to navigate a robot to a mission destination within an environment of the robot and generating a route specification for navigating the robot from a current location in the environment to the mission destination in the environment. The route specification includes a series of route segments. Each route segment in the series of route segments includes a goal region for the corresponding route segment and a constraint region encompassing the goal region. The constraint region establishes boundaries for the robot to remain within while traversing toward the goal region. The route segment also includes an initial path for the robot to follow while traversing the corresponding route segment.

A method for constraining robot autonomy language includes receiving a navigation command to navigate a robot to a mission destination within an environment of the robot and generating a route specification for navigating the robot from a current location in the environment to the mission destination in the environment. The route specification includes a series of route segments. Each route segment in the series of route segments includes a goal region for the corresponding route segment and a constraint region encompassing the goal region. The constraint region establishes boundaries for the robot to remain within while traversing toward the goal region. The route segment also includes an initial path for the robot to follow while traversing the corresponding route segment.

A physical space contains stationary objects that do not move over time (e.g., a couch) and may have non-stationary objects that do move over time (e.g., people and pets). An autonomous mobile device (AMD) determines and uses an occupancy map of stationary objects to find a route from one point to another in a physical space. Non-stationary objects are detected and prevented from being incorrectly added to the occupancy map. Point cloud data is processed to determine first candidate objects. Image data is processed to determine second candidate objects. These candidate objects are associated with each other and their characteristics assessed to determine if the candidate objects are stationary or non-stationary. The occupancy map is updated with stationary obstacles. During navigation, the occupancy map may be used for route planning while the non-stationary objects are used for local avoidance.

Methods and systems for autonomous vehicle recharging or refueling are disclosed. Autonomous electric vehicles may be automatically recharged by routing the vehicles to available charging stations when not in operation, according to methods described herein. A charge level of the battery of an autonomous electric vehicle may be monitored until it reaches a recharging threshold, at which point an on-board computer may generate a predicted use profile for the vehicle. Based upon the predicted use profile, a time and location for the vehicle to recharge may be determined. In some embodiments, the vehicle may be controlled to automatically travel to a charging station, recharge the battery, and return to its starting location in order to recharge when not in use.

Embodiments herein relate to an autonomous vehicle or self-driving vehicle. The system can determine a collision avoidance path by: 1) predicting the behavior/trajectory of other moving objects (and identifying stationary objects); 2) given the driving trajectory (issued by autonomous driving system) or predicted driving trajectory (human), establishing the probability for a collision that can be calculated between the vehicle and one or more objects; and 3) finding a path to minimize the collision probability.

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.