An autonomous vehicle includes an array of sensors, a processor, and a switch. The array of sensors generate sensor data related to one or more objects in an external environment of the autonomous vehicle and the processor determines an environmental context. The switch transfers the sensor data from the array of sensors to the processor, where the switch is configured to: (a) receive first sensor data from a first sensor group of the array of sensors; (b) receive second sensor data from a second sensor group of the array of sensors; (c) determine an order of transmission of the first sensor data over the second sensor data in response to the environmental context; and (d) transmit the first sensor data to the processor prior to transmitting the second sensor data based on the order of transmission.

Systems and methods for updating navigational maps based using at least one sensor are provided. In one aspect, a control system for an autonomous vehicle, includes a processor and a computer-readable memory configured to cause the processor to: receive output from at least one sensor located on the autonomous vehicle indicative of a driving environment of the autonomous vehicle, retrieve a navigational map used for driving the autonomous vehicle, and detect one or more inconsistencies between the output of the at least one sensor and the navigational map. The computer-readable memory is further configured to cause the processor to: in response to detecting the one or more inconsistencies, trigger mapping of the driving environment based on the output of the at least one sensor, update the navigational map based on the mapped driving environment, and drive the autonomous vehicle using the updated navigational map.

A vehicle control apparatus comprises a first detection unit configured to have a first detection range, a second detection unit configured to have a second detection range which at least partially overlaps the first detection range, and a vehicle control unit configured to be capable of performing vehicle control based on a first control state and vehicle control based on a second control state which has a high vehicle control automation rate or a reduced degree of vehicle operation participation requested to a driver compared to the first control state. The vehicle control unit performs control to shift from the first control state to the second control state based on a condition that a match degree between pieces of preceding object information of a vehicle detected by the first detection unit and the second detection unit.

Systems, apparatuses and methods may provide for technology that generates a sequence of predictive maps based on a sequence of historical maps and overlays the sequence of predictive maps on one another to obtain a map overlay. The technology may also apply an attenuation factor to the map overlay. In one example, the map overlay includes a grid of cells and each cell includes an occupation probability in accordance with the attenuation factor.

A method for localizing a vehicle comprises transmitting first position data related to a first position of the vehicle at a first point in time from the vehicle to a server. The server computes second position data related to the first position of the vehicle at the first point in time based on the received first position data. The server transmits the second position data from the server to the vehicle. The vehicle computes third position data related to a second position of the vehicle at a second point in time based on the received second position data. The second point in time is later than the first point in time.

An autonomous electric mower for mowing a lawn comprises a frame, drive wheels, cutting deck, computer, a Lidar sensor, at least one color and depth sensing camera. The computer is programmed and operable to: determine the location of the mower; detect obstacles; and to instruct the mower to avoid the obstacles. Advantageously, the system is operable to analyze the data from the multiple sensors and to instruct the mower to continue to safely operate and cut the lawn despite one or more of the sensors being obstructed. Novel route planning methods are also described.

The technology relates to autonomous vehicles having hitched or towed trailers for transporting cargo and other items between locations. Aspects of the technology provide a smart hitch connection between the fifth-wheel of a tractor unit and the kingpin of a trailer. This avoids requiring a person to make physical pneumatic and electrical connections between the fifth-wheel and kingpin using external hoses and cables. Instead, the necessary connections are made internally, autonomously. For instance, the fifth-wheel may provide air pressure via one or more slots arranged on a connection surface, and the trailer is configured to receive the air pressure through one or more openings on a contact surface of the kingpin. An electrical connection section of the fifth-wheel may also provide electrical signals and/or power to an electrical contact interface of the kingpin. Rotational information about relative alignment of the trailer to the tractor unit may also be provided.

Techniques for determining distortion in a map caused by measurement errors are discussed herein. For example, such techniques may include implementing a model to estimate map distortion between the map frame and the inertial frame. Data such as sensor data, map data, and vehicle state data may be input into the model. A map distortion value output from the model may be used to compensate vehicle operations in a local region by approximating the distortion as linearly varying about the region. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on the trajectory.

Sensor data is received from an array of sensors configured to capture one or more objects in an external environment of an autonomous vehicle. A first sensor group is selected from the array of sensors based on proximity data or environmental contexts. First sensor data from the first sensor group is prioritized for transmission based on the proximity data or environmental contexts.

In one embodiment, a method includes determining an operational status of a vehicle including a radar antenna. The operational status is related to autonomous-driving operations of the vehicle in an environment. The method includes determining an expected amount of signaling resources associated with the radar antenna to be utilized by the vehicle while the vehicle performs the autonomous-driving operations, based at least on the operational status of the vehicle and the environment. The method includes determining to switch one or more of the signaling resources associated with the radar antenna from a first mode to a second mode based on the expected amount of signaling resources to be utilized by the radar antenna while the vehicle performs the autonomous-driving operations. The method includes causing the one or more of the signaling resources associated with the radar antenna to switch from the first mode to the second mode.