Methods, systems, devices and apparatuses for an infrastructure planning tool. The infrastructure planning tool includes a navigation unit. The navigation unit is configured to determine a location of a vehicle. The infrastructure planning tool includes a processor. The processor is configured to determine a route within an off-road real property to traverse by the vehicle using the location of the vehicle over a period of time. The processor is configured to determine or estimate an amount of energy used by the vehicle to traverse the route. The processor is configured to determine a location along the route to place critical based on the amount of energy used. The processor is configured to render, on a display, a graphical representation that includes the route, the off-road real property and the location along the route to place the critical infrastructure.

An execution device executes an obtaining process that obtains a state of a vehicle, an operating process that operates an electronic device of the vehicle based on the state of the vehicle obtained by the obtaining process and operation data, a performance determining process that determines whether an environmental performance of the vehicle when the electronic device is operated is lower than a determination performance, and a data updating process that updates the operation data so as to increase the environmental performance of the vehicle when the performance determining process determines that the environmental performance of the vehicle is lower than the determination performance.

Described herein is a server computing system that controls an autonomous vehicle to perform an operation to at least one of measure or isolate an effect of a variable on an actual power consumption by the autonomous vehicle. Data indicative of the actual power consumption, which is generated based on the operation, and data indicative of a projected power consumption, which is accumulated based on prior execution of the operation by a same or different autonomous vehicle, is received by the server computing system to determine whether an energy efficiency of the autonomous vehicle is degraded. The operation may be performed to identify a degraded vehicle system or component of the autonomous vehicle or to identify an autonomous vehicle in a fleet of autonomous vehicles for which further analysis is desirable. An output is generated by the server computing system that is indicative of the energy efficiency prognostics.

A method for optimizing inter-vehicle order and distance includes communicating vehicle characteristics representative of a first and at least one second vehicle as the first vehicle approaches the at least one second vehicle. Based on the vehicle characteristics, a position in a driving order of the first vehicle with respect to the at least one second vehicle is selected and executed. A real-time fuel economy is recorded as the vehicle in the trailing position moves from a maximum following distance to a minimum following distance from the vehicle in the lead position. Based on at least the recorded real-time fuel economy, an optimal inter-vehicle distance between the vehicles in the lead and trailing positions is selected and executed.

A controller obtains topographical data indicative of the topography of a work site. The controller obtains material data indicative of the position of a material. The controller computes an evaluation function based on the material data for each of a plurality of candidates of the travel path to be decided from the topographical data. The evaluation function includes a material function pertaining to an amount of the material. The controller decides a candidate having a smallest evaluation function of the plurality of candidates as the travel path.

An energy management system for an electric autonomous vehicle comprises a battery and a controller comprising a processor and a non-transitory computer-readable medium. The system comprises a pre-allocation input mechanism transmitting an input signal to the processor relating to a travel destination for the vehicle. The system comprises a navigation module receiving a wireless signal from a satellite network relating to a current location of the vehicle. The system comprises an autonomous input mechanism powered by the battery and transmitting an autonomous signal to the processor relating to dynamic conditions for autonomous operation of the vehicle. The processor determines a route between the current location and the travel destination, performs autonomous operation of the vehicle along the route, and selectively powers or tunes the usage of the at least one autonomous input mechanism with the battery during autonomous operation of the vehicle.

A method for assembling a vehicle from a set of modules for travelling a planned route, wherein the set of modules comprises at least one functional module and a plurality of drive modules. Each drive module comprises a pair of wheels, electrical motor, and an interface releasably connectable to a corresponding interface on another module, wherein each drive module is configured to operate autonomously and has an individual set of energy parameters. The method comprising obtaining route information associated with route segments of the planned route, selecting a first drive module having an individual set of energy parameters matching route information associated with a first route segment and selecting a second drive module having an individual set of energy parameters matching route information associated with a second route segment, and thereafter commanding the drive modules to connect together and with a functional module.

Systems and methods are provided for identifying an imminent low state of charge of a battery in an autonomous vehicle, and automatically powering down the vehicle before a zero state of charge event occurs. In particular, the autonomous vehicle automatically powers down while there is enough energy in the batteries to restart the vehicle and drive the vehicle a short distance to a charging station.

An automotive arithmetic system includes a main arithmetic device that determines a target motion of a motor vehicle so that the motor vehicle travel on a route generated based on a vehicle external environment estimated using deep learning based on an output from a vehicle external information acquisition device; and a backup arithmetic device that determines a backup target motion for causing the motor vehicle to travel on a travel route which is generated based on the output from the vehicle external information acquisition device and which the traveling motor vehicle takes until the motor vehicle stops at a stop position that satisfies a preset criterion. The automotive arithmetic system outputs a backup control signal to actuators in preference to a control signal when the main arithmetic device fails.

A vehicle arithmetic system includes a single information processing circuitry. performs control of vehicle external environment estimation circuitry configured to receive outputs from sensors that obtain information of a vehicle external environment, and estimate the vehicle external environment including a road and an obstacle; a route generation circuitry configured to generate a traveling route of the vehicle which avoids the obstacle estimated on the road estimated, based on an output from the vehicle external environment estimation unit; and a target motion determination circuitry configured to determine a target motion of the vehicle so that the vehicle travels along the traveling route generated by the route generation circuitry.