Facilitating autonomously linking movement of objects in four dimensions in advanced networks (e.g., 5G, 6G, and beyond) is provided herein. Operations of a method can include identifying, by a system comprising a memory and a processor, a first portion of a first traversal route grid associated with a first object and a second portion of a second traversal route grid associated with a second object. The identifying can be based on the first portion and the second portion being determined to be overlapping portions during a same time period. The method also can include linking, by the system, during the same time period, a first movement of the first object and a second movement of the second object across the overlapping portions.

Techniques described in this application are directed to determining safe path navigation of an autonomous personal mobility device, including a self-driving electric scooter, using sensors and/or data from other sources. The autonomous personal mobility device is configured to generate maps and transform the maps into tile segments that can be shared with other autonomous personal mobility devices. Techniques further include receiving a request for an autonomous personal mobility device at a location, and enabling the autonomous personal mobility device to self-drive to the location.

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.

Disclosed is a vehicle platooning control apparatus including a vehicle speed setter configured to set a target speed of a platooning group based on driving-speed-based fuel economy data of a vehicle included in the platooning group, a distance setter configured to set a separation distance between the vehicle and a preceding vehicle based on the target speed set by the vehicle speed setter, and a driving controller configured to control driving of the vehicle based on the target speed set by the vehicle speed setter or the separation distance set by the distance setter.

A teleoperations system that collaboratively works with an autonomous vehicle guidance system to generate a path for controlling the autonomous vehicle may comprise generating one or more trajectories at the teleoperations system based at least in part on environment data received from the autonomous vehicle and presenting the one or more trajectories to a teleoperator (e.g., a human user, machine-learned model, or artificial intelligence component). A selection of one of the trajectories may be received at the teleoperations system and transmitted to the autonomous vehicle. The one or more trajectories may be generated at the teleoperations system and/or received from the autonomous vehicle. Regardless, the autonomous vehicle may generate a control trajectory based on the trajectory received from teleoperations, instead of merely implementing the trajectory from the teleoperations system.

A method includes detecting, via a processor of an autonomous vehicle, an upcoming downhill road segment of a route on which the autonomous vehicle is currently travelling. The detection is based on map data, camera data, and/or inertial measurement unit (IMU) data. In response to detecting the upcoming downhill road segment, a descent plan is generated for the autonomous vehicle. The descent plan includes a speed profile and a brake usage plan. The brake usage plan specifies a non-zero amount of retarder usage and an amount of foundation brake usage for a predefined time period. The method also includes autonomously controlling the autonomous vehicle, based on the descent plan, while the autonomous vehicle descends the downhill road segment.

Disclosed are a method of controlling platooning according to a wind direction and a control server for implementing the same. The disclosed control server includes a communication unit configured to communicate with two or more autonomous vehicles which travel in a platoon, a memory configured to store one or more instructions, and a processor configured to execute the instructions. The communication unit receives a power loss value from a leading vehicle among the two or more vehicles and receives information about a direction of wind around the two or more vehicles from at least one vehicle among the two or more vehicles or an external server.

A method includes identifying route data including a threshold arrival time for a route for an autonomous vehicle (AV) and calculating, based on the route data and a fuel-efficient speed value for each segment of the route, an estimated arrival time. Responsive to the estimated arrival time not meeting the threshold arrival time, the method includes identifying at least a subset of segments that each represent a candidate for speed increase, computing, for each segment in the subset and based on the fuel economy data, a correlation metric that indicates a correlation between a change in fuel economy and a change in speed for a corresponding segment in the subset, and increasing, for at least one segment from the subset and based on a respective correlation metric, a fuel-efficient speed value of the corresponding segment from the subset to provide a speed profile reflecting the increased fuel-efficient speed value.

A method includes detecting, via a processor of an autonomous vehicle, an upcoming downhill road segment of a route on which the autonomous vehicle is currently travelling. The detection is based on map data, camera data, and/or inertial measurement unit (IMU) data. In response to detecting the upcoming downhill road segment, a descent plan is generated for the autonomous vehicle. The descent plan includes a speed profile and a brake usage plan. The brake usage plan specifies a non-zero amount of retarder usage and an amount of foundation brake usage for a predefined time period. The method also includes autonomously controlling the autonomous vehicle, based on the descent plan, while the autonomous vehicle descends the downhill road segment.

A method and a processor for controlling in-lane movement of a Self-Driving Vehicle (SDV) are provided. The method comprises: acquiring initial kinematic data associated with an obstacle; determining future kinematic data associated with the obstacle; acquiring initial kinematic data associated with the SDV; determining future kinematic data associated with the SDV which is indicative of at least two candidate future states of the SDV at a future moment in time; determining at least two candidate state-transition datasets for the SDV to transition from the initial state of the SDV to a respective one of the at least two candidate future states of the SDV using only in-lane movement; determining, by the electronic device, an energy efficiency score for a respective one of the at least two candidate state-transition datasets; determining a target state-transition dataset for the SDV based on respective energy efficiency scores.