A robotic orchard spraying system having autonomous delivery vehicles (ADV), each autonomously delivering an amount of a premixed solution over a non-overlapping path verified by a forward-looking sensor, video, or both. Also, a mobile control center, configured to wirelessly inform the autonomous delivery vehicle of the path within the areas and to confirm that the autonomous delivery vehicle is following the path within the area. Additionally, a mapper vehicle generates the path within the area, the mapper vehicle being configured to communicate information about the path and the area to the command center. The mapper vehicle senses the path with a forward-looking LiDAR sensor, and senses the area with a GPS sensor. Moreover, a nurse truck has a reservoir of premixed solution for replenishing a tank of the ADV. ADVs and the control center communicate over a radio network, which may be a mesh network, a cellular network, or both.

An integrated speed prediction framework based on historical traffic data mining and real-time V2I communications for CAVs. The present framework provides multi-horizon speed predictions with different fidelity over short and long horizons. The present multi-horizon speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs) as a case study. The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the present data mining strategy over long prediction horizons. Despite the uncertainty in long-range CAV speed predictions, the vehicle level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal-driving and conventional BTM strategy.

An integrated speed prediction framework based on historical traffic data mining and real-time V2I communications for CAVs. The present framework provides multi-horizon speed predictions with different fidelity over short and long horizons. The present multi-horizon speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs) as a case study. The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the present data mining strategy over long prediction horizons. Despite the uncertainty in long-range CAV speed predictions, the vehicle level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal-driving and conventional BTM strategy.

An unmanned aerial vehicle (UAV) or drone executes a neural network to assist with detecting and responding to attacks. The neural network may monitor, in real time, the data stream from a plurality of onboard sensors during navigation and may communicate with a high-altitude pseudosatellite (HAPS) platform. For example, if the neural network detects a cyber-attack but determines that it does not interfere with external communications, it may shift navigation control of the drone to the HAPS.

An unmanned aerial vehicle (UAV) or drone executes a neural network to assist with detecting and responding to attacks. The neural network may monitor, in real time, the data stream from a plurality of onboard sensors during navigation and may communicate with a high-altitude pseudosatellite (HAPS) platform. For example, if the neural network detects a cyber-attack but determines that it does not interfere with external communications, it may shift navigation control of the drone to the HAPS.

A system and method for providing precise navigation for a motorized mobile system (MMS) in data deprived environments, the system comprising at least one camera that is operably configured to generate one or more of an image of objects in a field-of-view of the sensors, wherein the object is identified and used to enhance navigation or operation of the MMS.

Systems and techniques are described for implementing autonomous control of powered earth-moving vehicles, including to automatically determine and control movement around a site having potential obstacles. For example, the systems/techniques may determine and implement autonomous operations of powered earth-moving vehicle(s) (e.g., obtain/integrate data from sensors of multiple types on a powered earth-moving vehicle, and use it to determine and control movement of the powered earth-moving vehicle around a site), including in some situations to implement coordinated actions of multiple powered earth-moving vehicles and/or of a powered earth-moving vehicle with one or more other types of construction vehicles. The described techniques may further include determining current location and positioning of the powered earth-moving vehicle on the site, determining a target destination location and/or path of the powered earth-moving vehicle, identifying and classifying obstacles (if any) along a desired path or otherwise between current and destination locations, and implementing actions to address any such obstacles.

According to certain aspects, a computer-implemented method for operating an autonomous or semi-autonomous vehicle may be provided. With the customer's permission, an identity of a vehicle operator may be identified and a vehicle operator profile may be retrieved. Operating data regarding autonomous operation features operating the vehicle may be received from vehicle-mounted sensors. When a request to disable an autonomous feature is received, a risk level for the autonomous feature is determined and compared with a driver behavior setting for the autonomous feature stored in the vehicle operator profile. Based upon the risk level comparison, the autonomous vehicle retains control of vehicle or the autonomous feature is disengaged depending upon which is the safer driverthe autonomous vehicle or the vehicle human occupant. As a result, unsafe disengagement of self-driving functionality for autonomous vehicles may be alleviated. Insurance discounts may be provided for autonomous vehicles having this safety functionality.

Systems and techniques are described for implementing autonomous control of powered earth-moving vehicles, including to automatically determine and control movement around a site having potential obstacles. For example, the systems/techniques may determine and implement autonomous operations of powered earth-moving vehicle(s) (e.g., obtain/integrate data from sensors of multiple types on a powered earth-moving vehicle, and use it to determine and control movement of the powered earth-moving vehicle around a site), including in some situations to implement coordinated actions of multiple powered earth-moving vehicles and/or of a powered earth-moving vehicle with one or more other types of construction vehicles. The described techniques may further include determining current location and positioning of the powered earth-moving vehicle on the site, determining a target destination location and/or path of the powered earth-moving vehicle, identifying and classifying obstacles (if any) along a desired path or otherwise between current and destination locations, and implementing actions to address any such obstacles.

Autonomous vehicles are capable of executing missions that abide by on-street rules or regulations, while also being able to seamlessly transition to and from zones, including off-street zones, with their our set(s) of rules or regulations. An on-board memory stores roadgraph information. An on-board computer is operative to execute commanded driving missions using the roadgraph information, including missions with one or more zones, each zone being defined by a sub-roadgraph with its own set of zone-specific driving rules and parameters. A mission may be coordinated with one or more payload operations, including zone with free drive paths as in a warehouse facility with loading and unloading zones to pick up payloads and place them down, or zone staging or entry points to one or more points of payload acquisition or placement. The vehicle may be a warehousing vehicle such as a forklift.