Ego actions for a mobile robot in the presence of at least one agent are autonomously planned. In a sampling phase, a goal for an agent is sampled from a set of available goals based on a probabilistic goal distribution, as determined using an observed trajectory of the agent. An agent trajectory is sampled, from a set of possible trajectories associated with the sampled goal, based on a probabilistic trajectory distribution, each trajectory of the set of possible trajectories reaching a location of the associated goal. In a simulation phase, an ego action is selected from a set of available ego actions and based on the selected ego action, the sampled agent trajectory, and a current state of the mobile robot, (i) behaviour of the mobile robot, and (ii) simultaneous behaviour of the agent are simulated, in order to assess the viability of the selected ego action.

The application disclose an autonomous-driving-based control method performed by a computer device. The method includes: acquiring scene information of a target vehicle; determining a current lane changing scene type of the target vehicle according to the scene information; recognizing, when the current lane changing scene type is a mandatory lane changing scene type, a first lane for completing a navigation travel route, and, when the first lane satisfies a lane changing safety check condition, controlling the target vehicle to perform lane changing operation according to the first lane. The second lane for optimizing the travel time is recognized according to the scene information when the current lane changing scene type is the free lane changing scene type. When the second lane satisfies the lane changing safety check condition, the target vehicle is controlled to perform lane changing operation according to the second lane.

The present document relates to a simulation method for an autonomous vehicle, a method for controlling the autonomous vehicle, a device, an electronic apparatus, a computer-readable storage medium, and a computer program product. The method for the simulation of the autonomous vehicle comprises acquiring current state information of the autonomous vehicle; performing the simulation based on the current state information to acquire the prediction information of the autonomous vehicle; and sending the prediction information to the autonomous vehicle.

In one embodiment, an exemplary method includes the operations of receiving, at a profiling application, a record file recorded by the ADV for a driving scenario in an area, and a high definition map matching the area; extracting planning messages and perception messages from the record file; and aligning the planning message and the perception messages based on their timestamps. The method further includes calculating an individual performance score for each planning cycle of the ADV for the driving scenario based on the planning messages; calculating a weight for each planning cycle based on the perception messages and the high definition map; and then calculating a weighted score for the driving scenario based on individual performance scores and their corresponding weights.

The present disclosure relates to an apparatus and method for controlling an autonomous vehicle to allow an autonomous vehicle to safely pass through a road according to a driver's choice when the width of the road is narrow. The apparatus includes a sensor for acquiring information data of obstacles and vehicles in front of and on a side of a host vehicle, a signal processor for outputting data with respect to positions and media of obstacles and a determination signal representing presence or absence of a vehicle on a driving path, a controller for determining whether driving is possible by analyzing information acquired by the sensor and outputting a control signal corresponding to a selection signal of the driver, an interface for displaying an image processed by the signal processor, and an autonomous driving function unit for performing autonomous driving according to the control signal.

A device may receive emergency data, traffic data, network performance data, crime data, and gunshot data associated with a geographical area and may identify a location within the geographical area based on the emergency data, the traffic data, the network performance data, the crime data, and the gunshot data. The device may determine, based on the emergency data, the traffic data, the network performance data, the crime data, and the gunshot data for the location, a risk level for the location and may identify an autonomous vehicle based on the risk level, the traffic data, and the network performance data for the location. The device may determine a route for the autonomous vehicle to the location based on the traffic data and the network performance data for the location, and may perform actions based on the autonomous vehicle and the route.

A test system includes a master electronic control module (ECM) configured to receive user input for performing a test action. The master ECM determines one or more subsystem ECMs associated with the requested test action and a sequence of operations to be controlled by the subsystem ECMs to perform the requested test action. The master ECM provides instructions to the subsystem ECMs to perform the operations, along with parameters for those operations. The master ECM may determine whether a test action is appropriate to perform, based on sensor data, before instructing subsystem ECMs to perform the operations of the test action.

The present disclosure provides a route processing method and apparatus. The solution includes: acquiring an initial traveling route, which includes a plurality of track points, corresponding to a vehicle; determining a vehicle traveling area, which includes an area where the vehicle is located when traveling to the track point, corresponding to each track point; determining at least one target track point in the plurality of track points according to the vehicle traveling area, where a first obstacle exists in the vehicle traveling area; performing updating processing on a position of each target track point in the initial traveling route respectively according to the position of the each target track point and a position of the first obstacle, and obtaining a target traveling route according to the target track point for which the updating processing has been performed; and controlling the vehicle to travel according to the target traveling route.

An agricultural system includes a target vehicle configured to harvest crops and a work vehicle. The work vehicle includes a controller. The controller includes a memory and a processor, and the controller is configured to receive or determine a plurality of vehicle paths as well as a location of the target vehicle. The controller is also configured to identify an active path of the plurality of vehicle paths based on the location of the target vehicle. The target path is a path traversed by the target vehicle.

Trajectory generation for controlling motion or other behavior of an autonomous vehicle may include alternately determining a candidate action and predicting a future state based on that candidate action. The technique may include determining a cost associated with the candidate action that may include an estimation of a transition cost from a current or former state to a next state of the vehicle. This cost estimate may be a lower bound cost or an upper bound cost and the tree search may alternately apply the lower bound cost or upper bound cost exclusively or according to a ratio or changing ratio. The prediction of the future state may be based at least in part on a machine-learned model's classification of a dynamic object as being a reactive object or a passive object, which may change how the dynamic object is modeled for the prediction.