A vehicle behavior of a monitored vehicle is learned. A vehicle illumination of the monitored vehicle is detected and monitored. If a light-pattern occurs in the detected vehicle illumination, wherein the light-pattern corresponds to a frequency, intensity and/or color dependent glowing of the vehicle illumination, and further wherein the light-pattern starts with a flashing up of the detected vehicle illumination and ends after a certain time without glowing of the respective part of the detected vehicle illumination, then the method further monitors the light-pattern; monitors a vehicle movement of the monitored vehicle during the occurrence of the light-pattern; and compares the monitored light-pattern with a known light-pattern from a light-pattern data entry stored in an light-pattern database. If the comparison results in the monitored light-pattern being unknown, the method stores the light-pattern and the vehicle movement together as a new light-pattern data entry in the light-pattern database.

Systems and methods are provided for vehicle navigation. In one implementation, a system may comprise at least one processor. The processor may be programmed to receive images representative of an environment of the host vehicle and analyze the images to identify a first object and a second object. The processor may determine a first predefined navigational constraint implicated by the first object and a second predefined navigational constraint implicated by the second object, wherein the first and second predefined navigational constraints cannot both be satisfied, and the second predefined navigational constraint has a priority higher than the first predefined navigational constraint. The processor may determine a navigational action for the host vehicle satisfying the second predefined navigational constraint, but not satisfying the first predefined navigational constraint and, cause an adjustment of a navigational actuator of the host vehicle in response to the determined navigational action.

According to one embodiment, a system receives a captured image perceiving an environment of an autonomous driving vehicle (ADV) from an image capturing device of the ADV capturing a plurality of obstacles near the ADV. The system generates a first tunnel based on a width of a road lane for the ADV, where the first tunnel represents a passable lane for the ADV to travel through. The system generates one or more additional tunnels based on locations of the obstacles, where the one or more additional tunnels modify a width of the passable lane according to a level of invasiveness of the obstacles. The system generates a trajectory of the ADV based on the first and the additional tunnels to control the ADV according to the trajectory to navigate around the obstacles without collision.

Devices, systems and methods for operating a rear-facing perception system for vehicles are described. An exemplary rear-facing perception system contains two corner units and a center unit, with each of the two corner units and the center unit including a camera module and a dual-band transceiver. A method for operating the rear-facing perception system includes pairing with a control unit by communicating, using the dual-band transceiver, over at least a first frequency band, transmitting a first trigger signal to the two corner units over a second frequency band non-overlapping with the first frequency band, and switching to an active mode. In an example, the first trigger signal causes the two corner units to switch to the active mode, which includes orienting the camera modules on the center unit and the two corner units to provide an unobstructed view of an area around a rear of the vehicle.

Various technologies described herein pertain to generating an occupancy grid movie for utilization in motion planning for the autonomous vehicle. The occupancy grid movie can be generated for a given time and can include time-stepped occupancy grids for future times that are at predefined time intervals from the given time. The time-stepped occupancy grids include cells corresponding to regions in an environment surrounding the autonomous vehicle. Probabilities can be assigned to the cells specifying likelihoods that the regions corresponding to the cells are occupied at the future times. Moreover, cached query objects that respectively specify indices of cells of a grid occupied by a representation of an autonomous vehicle at corresponding orientations are described herein. An occupancy grid for the environment surrounding the autonomous vehicle can be queried to determine whether cells of the occupancy grid are occupied utilizing a cached query object from the cache query objects.

An automatic storage and retrieval system includes: a delivery vehicle; a storage container carried by the delivery vehicle; and an unloading station for unloading an item from the storage container while it is being carried by the delivery vehicle. The unloading station includes: an unloading device; and a destination conveyor configured to convey the item to a target destination, wherein the unloading device is configured to move the item through a side opening of the storage container to the destination conveyor.

Methods and systems for autonomous and semi-autonomous vehicle control, routing, and automatic feature adjustment are disclosed. Sensors associated with autonomous operation features may be utilized to search an area for missing persons, stolen vehicles, or similar persons or items of interest. Sensor data associated with the features may be automatically collected and analyzed to passively search for missing persons or vehicles without vehicle operator involvement. Search criteria may be determined by a remote server and communicated to a plurality of vehicles within a search area. In response to which, sensor data may be collected and analyzed by the vehicles. When sensor data generated by a vehicle matches the search criteria, the vehicle may communicate the information to the remote server.

A system and method for providing online multi-LiDAR dynamic occupancy mapping that include receiving LiDAR data from each of a plurality of LiDAR sensors. The system and method also include processing a region of interest grid to compute a static occupancy map of a surrounding environment of the ego vehicle and processing a dynamic occupancy map. The system and method further include controlling the ego vehicle to be operated based on the dynamic occupancy map.

A robot includes a base, at least one roller bearing having an inner hub supported to the base and an outer hub rotated about the inner hub, a spacer contacting the outer hub, a spin body to which the spacer is attached, and a spin mechanism coupled to the spin body to rotate the spin body. The spacer separates the spin body from the roller bearing between the spin body and the roller bearing. A surface hardness of the spacer is greater than a surface hardness of the spin body.

Systems and method are provided for controlling a vehicle. In one embodiment, a method includes: initiating, by a controller onboard the vehicle, a first laser pulse from a first laser device; initiating, by a controller onboard the vehicle, a second laser pulse from a second laser device, wherein the initiating the second laser pulse is based on a phase shift angle; receiving, by the controller onboard the vehicle, first return data and second return data as a result of the first laser pulse and the second laser pulse; interleaving, by the controller onboard the vehicle, the first return pulse and the second return pulse to form a point cloud; and controlling, by the controller onboard the vehicle, the vehicle based on the point cloud.