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 method, system and computer-usable medium are disclosed for autonomous vehicle (AV) behavior synchronization. The AV driving pattern is adjusted to facilitate an occupant's satisfaction by receiving information as to person to form a driving history. The driving history is analyzed to identify preferences and patterns. Based on the driving history a driving preference model is formed for the person. AV driving algorithm(s) are adjusted based on the driving preference model for the person when the person is an occupant of the AV.

A computer-implemented method comprises: initiating, by an assisted-driving system that is controlling motion of a vehicle, a lane change of the vehicle from an ego lane to a target lane, the lane change to be performed based only on local information of the vehicle; defining, by the assisted-driving system, a parametric representation of the target lane, the parametric representation based on first waypoints of the target lane; receiving, by the assisted-driving system and during the lane change, a first output of a perception component of the vehicle, the first output being the local information and reflecting second waypoints of the target lane; performing, by the assisted-driving system, a comparison based on the first output and the parametric representation; and providing, by the assisted-driving system, references of a first global path to controllers of the vehicle for actuating the lane change, the references selected based on the comparison.

The present teaching relates to method, system, medium, and implementation of automatic motion planning for an autonomous driving vehicle. Information is obtained with respect to a current location of the autonomous driving vehicle operating in a current vehicle motion, wherein the information is to be used to estimate the operational capability of the autonomous driving vehicle with respect to the current location. Sensor data are obtained, via one or more sensors in one or more media types. A reaction of a passenger present in the vehicle with respect to a current vehicle motion is estimated based on the sensor data. Motion planning is performed based on the information with respect to the current location and the reaction of the passenger to the current vehicle motion.

A vehicle travel control device executes vehicle travel control such that a vehicle follows a target trajectory. An automated driving control device generates a first target trajectory that is the target trajectory for automated driving of the vehicle. The vehicle travel control device further determines whether or not an activation condition of travel assist control is satisfied. When the activation condition is satisfied, the vehicle travel control device generates a second target trajectory that is the target trajectory for the travel assist control. Even when the second target trajectory is generated during the automated driving, or when the second target trajectory is generated during the automated driving and a priority condition for giving priority to the first target trajectory is satisfied, the vehicle travel control device executes the vehicle travel control by giving more weight to the first target trajectory than to the second target trajectory.

A method of controlling a vehicle when the vehicle passes over a speed bump, may include: dividing sections of the road into a first section within a first time period before the front wheel of the vehicle collides with the speed bump, a second section while the front wheel collides with the speed bump, a third section within a second time period before the rear wheel collides with the speed bump, and a fourth section while the rear wheel collides with the speed bump; and controlling and distributing at least one of suspension damping force, driving power and braking force to the front wheel and the rear wheel for each of the first section, the second section, the third section and the fourth section to reduce the amount of impact to be applied when the vehicle collides with the speed bump and to reduce a vertical motion of the vehicle that occurs while the vehicle goes over the speed bump.

A steering apparatus includes an actuator and an actuator controller. The actuator is configured to generate an output for turning a steerable wheel of a vehicle. The actuator controller is configured to control the actuator in accordance with a steering input. The actuator controller includes a filtering processor configured to perform filtering processing for reducing or cutting a component in a predetermined frequency band from the steering input, and the actuator controller is configured to control the actuator in accordance with the steering input after the filtering processing.

A vehicle travel control device executes vehicle travel control such that a vehicle follows a target trajectory. An automated driving control device generates a first target trajectory that is the target trajectory for automated driving of the vehicle. The vehicle travel control device further determines whether or not an activation condition of travel assist control is satisfied. When the activation condition is satisfied, the vehicle travel control device generates a second target trajectory that is the target trajectory for the travel assist control. Even when the second target trajectory is generated during the automated driving, or when the second target trajectory is generated during the automated driving and a priority condition for giving priority to the first target trajectory is satisfied, the vehicle travel control device executes the vehicle travel control by giving more weight to the first target trajectory than to the second target trajectory.

In an apparatus for controlling driving of a vehicle, an input interface is configured to receive a reassurance indicator entered by a passenger of the vehicle. The reassurance indicator represents a preference of the passenger for security. A communication unit is configured to communicate with a plurality of roadside devices located in respective potentially risky areas where the vehicle may collide with a moving object. Each roadside device is configured to evaluate the collision possibility with the moving object. A vehicle controller is configured to, if receiving an evaluation result from the roadside device located in the potentially risky area forward of the own vehicle showing that the own vehicle is unlikely to collide with the moving object in the potentially risky area forward of the vehicle, perform avoidance control to control driving of the vehicle according to the reassurance indicator.

Systems and methods for avoiding or mitigating motion sickness in a vehicle are disclosed herein. The systems and methods may include (a) determining a profile of the individual inclination towards motion sickness; (b) predicting an individual effectiveness of countermeasures based on typing using the data from step (a); (c) evaluating the actual effectiveness of the predicted countermeasures after their implementation when travelling in the vehicle; wherein step (a) is repeated regularly and all data and results from step (c) are fed into a self-learning system in order to obtain an improved statement on the individual actual effectiveness of countermeasures taken and to make the selection of effective countermeasures based on them.