A vehicle includes a cabin having a first room and a second room that are capable of accommodating at least one passenger, and configured to isolate one or more passengers accommodated in the first room from one or more passengers accommodated in the second room, a guidance apparatus configured to guide the at least one passenger to be accommodated in either the first room or the second room, and a control apparatus configured to control the guidance apparatus. When a user boards as the at least one passenger, the control apparatus determines which of the first room and the second room the user is to board, based on information regarding the user. The guidance apparatus is configured to guide and board the user to whichever of the first room and the second room determined by the control apparatus.

Detecting out-of-model scenarios for an autonomous vehicle including: determining, based on first sensor data from one or more sensors, an environmental state relative to the autonomous vehicle, wherein operational commands for the autonomous vehicle are based on a selected machine learning model, wherein the selected machine learning model comprises a first machine learning model; comparing the environmental state to a predicted environmental state relative to the autonomous vehicle; and determining, based on a differential between the environmental state and the predicted environmental state, whether to select a second machine learning model as the selected machine learning model.

Systems and methods to authenticate a vehicle operator for an autonomous vehicle on a vehicle service platform are provided. In one example embodiment, a computer-implemented method includes obtaining authentication request data indicative of an authentication request, the authentication request data including at least an operator identifier associated with the vehicle operator and a vehicle identifier associated with the autonomous vehicle. The method includes providing a service code associated with the authentication request to the autonomous vehicle. The method includes obtaining from a user device in response to providing the service code to the autonomous vehicle, operator data associated with the authentication request, the operator data including the service code. The method includes determining an authentication result associated with the authentication request based at least in part on the service code and the operator data. The method includes providing the authentication result to the user device.

Systems and methods to authenticate a vehicle operator for an autonomous vehicle on a vehicle service platform are provided. In one example embodiment, a computer-implemented method includes obtaining authentication request data indicative of an authentication request, the authentication request data including at least an operator identifier associated with the vehicle operator and a vehicle identifier associated with the autonomous vehicle. The method includes providing a service code associated with the authentication request to the autonomous vehicle. The method includes obtaining from a user device in response to providing the service code to the autonomous vehicle, operator data associated with the authentication request, the operator data including the service code. The method includes determining an authentication result associated with the authentication request based at least in part on the service code and the operator data. The method includes providing the authentication result to the user device.

System, methods, and other embodiments described herein relate to improving automated driving by testing for inputs during driving. In one embodiment, a method includes testing an input from a driver in a manual driving mode of a vehicle. The method also includes adapting a fixed time interval on a condition that a test result of the input satisfies criteria used to validate driver inputs. The method also includes monitoring, via an input system of the vehicle, for driver feedback according to the fixed time interval in an automated driving mode.

A method for determining information related to a lane change of a target vehicle includes obtaining information related to an environment of the target vehicle. The information related to the environment relates to a plurality of features of the environment of the target vehicle. The plurality of features are partitioned into two or more groups of features. The method further determines two or more weighting factors for the two or more groups of features. An attention mechanism is used for determining the two or more weighting factors. The method further determines the information related to the lane change of the target vehicle based on the information related to the environment of the target vehicle using a machine-learning network. A weighting of the plurality of features of the environment of the target vehicle within the machine-learning network is based on the two or more weighting factors for the two or more groups of features.

This application provides a data processing method performed a computer device. The method includes: generating an initial predicted lane change acceleration corresponding to a target vehicle in a current lane; generating target predicted position information corresponding to the target vehicle according to a predicted lane change time duration taken for the target vehicle to change from the current lane to a target lane, the target lane being a lane to which the target vehicle is expected to change to; determining a target obstacle vehicle in the target lane and adjacent to the target vehicle according to the target predicted position information; determining, according to a predicted position relationship between the target obstacle vehicle and the target vehicle, a target predicted lane change acceleration; and controlling, according to the target predicted lane change acceleration, the target vehicle to change from the current lane to the target lane.

A driving limitation system includes the steps of: identifying a driver by a driver authentication means; extracting information on the identified driver; extracting data relating to vehicle speed limitation according to the driver; extracting data relating to limitation in a vehicle traveling area according to the driver; performing selection or generation of map data regarding a traveling area; generating, on the basis of the extracted data relating to the vehicle speed limitation, vehicle traveling area limitation and the driver, a traveling speed control command within the limitation in the vehicle traveling area in accordance with the physical ability and/or cognitive ability of the driver; transmitting the traveling speed control command to a vehicle speed control means; generating a control command for controlling the vehicle speed on the basis of the safety speed indicated by the vehicle speed control means; and sending the control command to an engine and to a braking system to set the traveling speed of the vehicle to be equal to or lower than the safety speed according to the driver.

[Problem] To provide an automatic driving robot control device and control method that enable a vehicle to be operated smoothly while also being caused to conform to a command vehicle speed with high accuracy.

[Solution] The present invention provides an automatic driving robot (drive robot) 4 control device 10 that controls the automatic driving robot 4, which is installed in a vehicle 2 and causes the vehicle 2 to run, such that the vehicle 2 runs in accordance with a defined command vehicle speed, wherein the automatic driving robot 4 control device 10 is provided with: a running state acquisition unit 22 that acquires a running state of the vehicle 2 including a vehicle speed and the command vehicle speed; an operation content inference unit 31 that infers, on the basis of the running state, an operation sequence, which is a sequence of operations of the vehicle 2 at a plurality of times in the future that causes the vehicle 2 to run in accordance with the command vehicle speed, by using an operation inference learning model 40 that was trained by machine learning to infer the operation sequence; and a vehicle operation control unit 23 that extracts, from each of the operation sequences inferred a plurality of times in the past, the operations corresponding to a control time for subsequently controlling the automatic driving robot 4, calculates a weighted sum of these extracted plurality of operations to calculate a final operation value, generates, on the basis of the final operation value, a control signal for controlling the automatic driving robot 4, and transmits the control signal to the automatic driving robot 4.

The present invention is to obtain a control device and a control method capable of appropriately assisting driving of a straddle-type vehicle by a rider.

In a control device (12) and a control method of the present invention, an acquisition section of the control device (12) configured to control an operation of a straddle-type vehicle (10) acquires prediction information about a future lane change by a preceding vehicle that travels ahead of the straddle-type vehicle (10), and a control section of the control device (12) causes the straddle-type vehicle (10) to execute a safety operation (for example, causes a notification device (15) to issue a warning of the lane change to the rider), when the prediction information satisfies a determination criterion during a slipping-through traveling of the straddle-type vehicle (10).