The present disclosure relates to a vehicle and control method thereof, to a vehicle having a driver assistance system for assisting a driver. When a lane change is requested even though it does not meet the predetermined lane change condition, present disclosure provides a vehicle driver assistance system (ADAS) that can actively indicate a lane change intention to an adjacent vehicle through ‘deflected driving in a lane’ and perform lane change safely after confirming the yield/overtake intention of the adjacent vehicle. It is an aspect of the present disclosure to provide a control method of a vehicle, including: confirming whether the surrounding situation of the vehicle satisfies a lane change condition when a lane change command occurs while the vehicle is driving autonomously; performing deflected driving in the lane of the vehicle to indicate a lane change intention when the surrounding situation of the vehicle does not satisfy the lane change condition; and performing a lane change corresponding to the lane change command when the yield intention for the lane change intention is confirmed from another vehicle around the traveling lane after indicating the lane change intention through the deflected driving.

A system, method, and computer-readable medium having instructions stored thereon to enable an ego vehicle having an autonomous driving function to estimate and traverse a curved segment of highway utilizing radar sensor data. The radar sensor data may comprise stationary reflections and moving reflections. The ego vehicle may utilize other data, such as global positioning system data, for the estimation and traversal. The estimation of the curvature may be refined based upon a lookup table or a deep neural network.

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.

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.

[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.

Included are: an information acquiring unit to acquire information; a periphery recognizing unit to acquire peripheral environment information regarding a state of a peripheral environment based on the information acquired by the information acquiring unit and a first machine learning model and to acquire calculation process information indicating a calculation process when the peripheral environment information has been acquired; an explanatory information generating unit to generate explanatory information indicating information having a large influence on the peripheral environment information in the calculation process among the information acquired by the information acquiring unit based on the calculation process information acquired by the periphery recognizing unit; and an evaluation information generating unit to generate evaluation information indicating adequacy of the peripheral environment information acquired by the periphery recognizing unit based on the information acquired by the information acquiring unit and the explanatory information generated by the explanatory information generating unit.

A high-definition map creation method includes: obtaining point cloud data collected with respect to a target region, the point cloud data including K frames of point clouds and an initial pose of each frame of point cloud, K being an integer greater than 1; associating the K frames of point clouds with each other in accordance with the initial pose to obtain a first point cloud relation graph of the K frames of point clouds; performing point cloud registration on the K frames of point clouds in accordance with the first point cloud relation graph and the initial pose to obtain a target relative pose of each frame of point cloud in the K frames of point clouds; and splicing the K frames of point clouds in accordance with the target relative pose to obtain a point cloud map of the target region.

Provided are methods for efficient vehicle extent estimation, which can include bounding box generation. Some methods described include determining bounding boxes surrounding detected point clusters according to tangents to convex hulls of the point clusters, and minimizing continuous functions of distances between points and bounding box sides. Accordingly, best-fit bounding boxes are determined more efficiently and quickly, as well as more accurately. Systems and computer program products are also provided.

A vehicle and a system and method of controlling the vehicle. The system includes a sensor and a processor. The sensor obtains a first estimate of a force on a tire of the vehicle based on dynamics of the vehicle. The processor is configured to obtain a second estimate of the force on the tire using a tire model, determine an estimate of a coefficient of friction between the tire and the road from the first estimate of the force and the second estimate of the force, and control the vehicle using the estimate of the coefficient of friction.

An inertial measurement unit including a support structure having rectangular cuboid configuration, a first sensor configured to detect a first angular rate wherein the first sensor is affixed to a first side of the support structure, a second sensor configured to detect a second angular rate wherein the second sensor is affixed to a second side of the support structure, a third sensor configured to detect a third angular rate wherein the third sensor is affixed to a third side of the support structure, a processor configured to generate an aggregate angular rate in response to the first angular rate, the second angular rate and the third angular rate, and a vehicle controller configured to control a vehicle in response to the aggregate angular rate.