An ADK is attachable to and removable from a VP and issues an instruction for autonomous driving to the VP. The VP includes a steering system for steering of the VP. (An ADS of) the ADK includes a compute assembly and communication module configured to communicate with the VP. The compute assembly obtains from the VP, a prescribed reference on which control of a wheel steer angle is premised, calculates the steer angle in accordance with a driving plan for autonomous driving based on the obtained prescribed reference, and transmits a wheel steer angle command indicating the calculated steer angle to the VP. When the attachable and removable ADK that issues the instruction for autonomous driving controls the VP, without storage of the prescribed reference on which control of the wheel steer angle is premised in advance in the ADK, control suited to a base vehicle can be carried out.

Techniques for controlling a vehicle based on a collision avoidance algorithm are discussed herein. The vehicle receives sensor data and can determine that the sensor data represents an object in an environment through which the vehicle is travelling. A computing device associated with the vehicle determines a collision probability between the vehicle and the object at predicted locations of the vehicle and object at a first time. Updated locations of the vehicle and object can be determined, and a second collision probability can be determined. The vehicle is controlled based at least in part on the collision probabilities.

A dynamic velocity planning method for an autonomous vehicle is performed to plan a best velocity curve of the autonomous vehicle. An information storing step is performed to store an obstacle information, a road information and a vehicle information. An acceleration limit calculating step is performed to calculate the vehicle information according to a calculating procedure to generate an acceleration limit value range. An acceleration combination generating step is performed to generate a plurality of acceleration combinations according to the obstacle information, the road information, and the acceleration limit value range. An acceleration filtering step is performed to filter the acceleration combinations according to a jerk threshold and a jerk switching frequency threshold to obtain a selected acceleration combination. An acceleration smoothing step is performed to execute a driving behavior procedure to adjust the selected acceleration combination to generate the best velocity curve.

A vehicle travel control device executes trajectory following control to make the vehicle follow a target trajectory. A delay time represents control delay of the trajectory following control. A delay compensation time is at least a part of the delay time. The trajectory following control includes: displacement estimation processing that estimates a displacement of the vehicle in the delay compensation time; and delay compensation processing that corrects a deviation between the vehicle and the target trajectory based on the estimated displacement to compensate the control delay. The displacement estimation processing is effective in an effective period and ineffective in an ineffective period. When the ineffective period is included in the delay time of the trajectory following control, the displacement estimation processing is executed in a temporary mode by using sensor-detected information in the effective period without using the sensor-detected information in the ineffective period.

The present invention achieves a technology that enables estimating a ground contact load in a vehicle with sufficiently high accuracy. A ground contact load estimation device according to the present invention is configured to estimate a ground contact load of a vehicle by: acquiring a wheel angular velocity, a steady load, and an inertial load of the vehicle; calculating a first gain using the steady load and the inertial load; estimating a road surface load using the first gain, a prescribed vehicle specification, and a second gain representing a hysteresis characteristic of a tire installed on the vehicle and referencing said road surface load.

The invention relates to a method for determining kinetosis in a vehicle user of a vehicle during at least one travel event in which at least one body part of the vehicle user is monitored, as a result of which image data are generated. Driving dynamics of the vehicle are monitored as the vehicle is being driven, as a result of which driving dynamics data are generated for every travel event while the vehicle is in motion. The image data are evaluated to determine the formation of sweat on the at least one body part of the vehicle user, as a result of which approximated electrodermal activity data are generated. The driving dynamics data are associated with the approximated electrodermal activity data, as a result of which the kinetosis of the vehicle user in at least one of the travel events is determined.

An occupancy grid manager having a non-uniform occupancy which includes a plurality of cells, configurable in a plurality of cell sizes, each cell representing a region of an environment of a vehicle; and one or more processors, configured to determine one or more context factors; select a cell size for a cell of the plurality of cells based on the one or more context factors; process sensor data provided by one or more sensors; and determine a probability that the cell of the plurality of cells is occupied based on the sensor data.

A system is provided for vehicle range prediction. The system determines a change in mass to a vehicle while driving. Additionally, the system calculates a vehicle load in response to determining the change in mass and adjusts a vehicle range in response to calculating the vehicle load. The vehicle range is indicative of a distance in which the vehicle is predicted to travel with a remaining fuel. The adjusted vehicle range is based on the vehicle load.

Systems and methods for vehicle motion control are provided. The method includes: calculating a correction factor using one of three different sets of operations when the vehicle is performing a limit handling maneuver, wherein the correction factor is calculated using a first set of operations when the vehicle is operating in an understeer state, calculated using a second set of operations when the vehicle is operating in an oversteer state, and calculated using a third set of operations when the vehicle is operating in a neutral steer state; adjusting a desired lateral acceleration and a desired yaw rate by applying the correction factor to account for a reduced level of friction experienced by the vehicle when traveling on a non-ideal friction surface; calculating optimal control actions based on the adjusted desired lateral acceleration and adjusted desired yaw rate; and applying the optimal control actions with vehicle actuators during vehicle operations.

A vehicle control device, a vehicle control method, a vehicle motion control system, and a lane estimation device according to the present invention obtain first information on lane markings defining a lane in which a vehicle travels based on external information obtained from an external recognition unit, obtain second information on a curvature of the lane based on information on a road shape obtained from a road shape information acquisition unit, obtain third information on a behavior of the vehicle based on a physical quantity that is related to a motion state of the vehicle and obtained from a vehicle motion state detection unit, and estimate lane information including information on curvatures of the lane markings and information on relative positions of the vehicle with respect to the lane markings based on the first information, the second information, and the third information.