Apparatuses, systems, and methods for open path laser spectroscopy with mobile platforms. An example system may include a first mobile platform and a second mobile platform, each of which supports a payload. A light beam directed from one payload to another may define a measurement path, which may be at a particular height above the ground. The payloads may determine a gas concentration along the measurement path. Wind information at the measurement height may be used to determine a gas flux. One or both of the mobile platforms may then move to a new location, and take a measurement along a new measurement path. By combining the measurement paths, gas flux through a flux surface may be determined.

Disclosed are a platooning control apparatus and control method, the apparatus including a communication unit obtaining performance information and sensing information of a controller for platooning and a control unit granting at least vehicle among plurality of vehicles platooning control authority based on the performance information and in response to determination that a vehicle in which the control unit is provided is the vehicle to be granted platooning control authority, generating a driving route on the sensing information and controlling the platooning based on the driving route.

An autonomous driving apparatus and method, in which the autonomous driving apparatus may include a sensor unit configured to detect a surrounding object including a surrounding vehicle around an ego vehicle that autonomously travels, a memory configured to store map information, and a processor configured to control autonomous driving of the ego vehicle based on an expected driving trajectory generated based on the map information stored in the memory.

An autonomous driving apparatus and method, in which the autonomous driving apparatus may include a sensor unit configured to detect a surrounding object including a surrounding vehicle around an ego vehicle that autonomously travels, a memory configured to store map information, and a processor configured to control autonomous driving of the ego vehicle based on an expected driving trajectory generated based on the map information stored in the memory.

Systems and methods for cooperatively managing mixed traffic at an intersection are disclosed herein. One embodiment determines, at an autonomous sensor-rich vehicle, that one or more other vehicles are following in the same lane, the one or more other vehicles including at least one legacy vehicle; communicates with the one or more other vehicles to form a platoon; receives Signal Phase and Timing (SpaT) information from a roadside unit associated with an intersection toward which the platoon is traveling; calculates at the autonomous sensor-rich vehicle, based at least in part on the SPaT information and location information for the platoon, a speed profile and a trajectory for the autonomous sensor-rich vehicle that minimize a delay of the platoon in traversing the intersection while accounting for fuel consumption; and executes the speed profile and the trajectory to control indirectly the one or more other vehicles while the platoon traverses the intersection.

A system for collecting vehicle crash data at a vehicle crash site of a vehicle crash is provided. The system may include an emergency response unit that includes an emergency response vehicle and an unmanned aerial vehicle (UAV) that is automatically deployed from the emergency response vehicle at the vehicle crash site. The UAV may be an autonomous or semi-autonomous drone, and include a processor, memory, and sensor, wherein the sensor collects vehicle crash data (such as image, video, or audio) at the crash site. The system may include a remote computing device and an insurance computing device to process the vehicle crash data collected by the UAV and/or initiate a crash insurance claim. The vehicle crash data may be used for one or more insurance-related purposes or activities, such as handling, adjusting, or generating auto or homeowners insurance claims; crash reconstruction; fault determination; damaged vehicle repair; and/or buildup identification.

A method for a leading transportation vehicle and for taking over control of a transportation vehicle to be controlled, including identifying the transportation vehicle to be controlled; determining a dynamic holding area relative to the leading transportation vehicle for the transportation vehicle to be controlled, wherein the dynamic holding area is defined so that the transportation vehicle to be controlled remains behind the leading transportation vehicle and uses a different lane; and transmitting a message relating to the dynamic holding area to the transportation vehicle to be controlled.

A method for a leading transportation vehicle and for taking over control of a transportation vehicle to be controlled, including identifying the transportation vehicle to be controlled; determining a dynamic holding area relative to the leading transportation vehicle for the transportation vehicle to be controlled, wherein the dynamic holding area is defined so that the transportation vehicle to be controlled remains behind the leading transportation vehicle and uses a different lane; and transmitting a message relating to the dynamic holding area to the transportation vehicle to be controlled.

The technology relates to assisting large self-driving vehicles, such as cargo vehicles, as they maneuver towards and/or park at a destination facility. This may include a given vehicle transitioning between different autonomous driving modes. Such a vehicles may be permitted to drive in a fully autonomous mode on certain roadways for the majority of a trip, but may need to change to a partially autonomous mode on other roadways or when entering or leaving a destination facility such as a warehouse, depot or service center. Large vehicles such as cargo truck may have limited room to maneuver in and park at the destination, which may also prevent operation in a fully autonomous mode. Here, information from the destination facility and/or a remote assistance service can be employed to aid in real-time semi-autonomous maneuvering.

The technology relates to assisting large self-driving vehicles, such as cargo vehicles, as they maneuver towards and/or park at a destination facility. This may include a given vehicle transitioning between different autonomous driving modes. Such a vehicles may be permitted to drive in a fully autonomous mode on certain roadways for the majority of a trip, but may need to change to a partially autonomous mode on other roadways or when entering or leaving a destination facility such as a warehouse, depot or service center. Large vehicles such as cargo truck may have limited room to maneuver in and park at the destination, which may also prevent operation in a fully autonomous mode. Here, information from the destination facility and/or a remote assistance service can be employed to aid in real-time semi-autonomous maneuvering.