A method for synchronizing devices in a vehicle may make use of the Controller Area Network (CAN) communication bus. A bus interface of each of two or more devices coupled to the bus may be configured to accept a same message broadcast via the communication bus, in which the message has a specific message identification (ID) header. A message may be received from the communication bus that has the specific message ID simultaneously by each of the two or more devices. Operation of the two or more devices may be synchronized by triggering a task on each of the two or more devices in response to receiving the message having the specific message ID.

A drive mode switch control device controls switching of driving between a driver and an autonomous driving function. The drive mode switch control device includes an operation information acquisition unit, a drive state switch unit, an attitude determination unit, and an approved target setting unit. The operation information acquisition unit acquires an operation information item associated with the driving operation input to at least one of a plurality of operation targets. The drive state switch unit executes an override that switches from an autonomous driving state to another driving state. The attitude determination unit acquires a plurality of detection information items related to driving attitudes of the driver, and determine whether each of the plurality of detection information items is appropriate for the driving operation. The approved target setting unit sets an approved operation target or a disapproved operation target to each of the plurality of operation targets.

A drive mode switch control device controls switching of driving between a driver and an autonomous driving function. The drive mode switch control device includes an operation information acquisition unit, a drive state switch unit, an attitude determination unit, and an approved target setting unit. The operation information acquisition unit acquires an operation information item associated with the driving operation input to at least one of a plurality of operation targets. The drive state switch unit executes an override that switches from an autonomous driving state to another driving state. The attitude determination unit acquires a plurality of detection information items related to driving attitudes of the driver, and determine whether each of the plurality of detection information items is appropriate for the driving operation. The approved target setting unit sets an approved operation target or a disapproved operation target to each of the plurality of operation targets.

In one embodiment, method performed by an autonomous driving vehicle (ADV) that determines, within a driving space, a plurality of routes from a current location of the ADV to a desired location. The method determines, for each route of the plurality of routes, an objective function to control the ADV autonomously along the route and, for each of the objective functions, performs Differential Dynamic Programming (DDP) optimization in view of a set of constraints to produce a path trajectory. The method determines whether at least one of the path trajectories satisfies each constraint and, in response to a path trajectory satisfying each of the constraints, selects the path trajectory for navigating the ADV from the current location to the desired location.

In one embodiment, method performed by an autonomous driving vehicle (ADV) that determines, within a driving space, a plurality of routes from a current location of the ADV to a desired location. The method determines, for each route of the plurality of routes, an objective function to control the ADV autonomously along the route and, for each of the objective functions, performs Differential Dynamic Programming (DDP) optimization in view of a set of constraints to produce a path trajectory. The method determines whether at least one of the path trajectories satisfies each constraint and, in response to a path trajectory satisfying each of the constraints, selects the path trajectory for navigating the ADV from the current location to the desired location.

A system and method for providing multiple agents for decision making, trajectory planning, and control for autonomous vehicles are disclosed. A particular embodiment includes: partitioning a multiple agent autonomous vehicle control module for an autonomous vehicle into a plurality of subsystem agents, the plurality of subsystem agents including a deep computing vehicle control subsystem and a fast response vehicle control subsystem; receiving a task request from a vehicle subsystem; dispatching the task request to the deep computing vehicle control subsystem or the fast response vehicle control subsystem based on the content of the task request or a context of the autonomous vehicle; causing execution of the deep computing vehicle control subsystem or the fast response vehicle control subsystem by use of a data processor to produce a vehicle control output; and providing the vehicle control output to a vehicle control subsystem of the autonomous vehicle.

A system and method for providing multiple agents for decision making, trajectory planning, and control for autonomous vehicles are disclosed. A particular embodiment includes: partitioning a multiple agent autonomous vehicle control module for an autonomous vehicle into a plurality of subsystem agents, the plurality of subsystem agents including a deep computing vehicle control subsystem and a fast response vehicle control subsystem; receiving a task request from a vehicle subsystem; dispatching the task request to the deep computing vehicle control subsystem or the fast response vehicle control subsystem based on the content of the task request or a context of the autonomous vehicle; causing execution of the deep computing vehicle control subsystem or the fast response vehicle control subsystem by use of a data processor to produce a vehicle control output; and providing the vehicle control output to a vehicle control subsystem of the autonomous vehicle.

A vehicle control device includes a recognizer configured to recognize situations around a vehicle; a determiner configured to determine any of lanes included in a road on which the vehicle travels as a reference lane; and a driving controller configured to control at least one of a speed and steering of the vehicle according to the situations recognized by the recognizer and the reference lane determined by the determiner, wherein the determiner is configured to, when the vehicle moves from a first road to a second road different from the first road, among a plurality of lanes included in the first road, according to a relative position of a first reference lane determined at a time before the vehicle moves to the second road with respect to the plurality of lanes, determine a second reference lane on the second road.

A method of operating a moving object on which a drone is mounted includes detecting, by the moving object, occurrence of an event; determining whether to use the drone, on the basis of the detected event; and determining an operation mode, among a first operation mode and a second operation mode, of the drone when the drone is used.

The present teaching relates to method, system, medium, and implementation of in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data are acquired continuously via a plurality of types of sensors deployed on the vehicle, where the plurality of types of sensor data provide information about surrounding of the vehicle. One or more items surrounding the vehicle are tracked, based on some models, from a first of the plurality of types of sensor data from a first type of the plurality of types of sensors. A second of the plurality of types of sensor data are obtained from a second type of the plurality of sensors and are used to generate validation base data. Some of the one or more items are labeled, automatically, via validation base data to generate labeled at least some item, which is to be used to generate model updated information for updating the at least one model.