An adaptive lane-keeping system for a commercial vehicle, including: an input module for entering sensor data from at least one sensor which is configured to detect the surroundings of the commercial vehicle; an evaluation module for evaluating the sensor data to determine a relative position of the commercial vehicle on a road; a lane-keeping module for controlling a steering system of the commercial vehicle based on a lane-keeping profile that defines a torque to be applied to a steering wheel of the commercial vehicle to support keeping in a lane; and a change module for changing the lane-keeping profile in response to a change in the detected environment. Also described is a related commercial vehicle, method, and computer readable medium.

An adaptive lane-keeping system for a commercial vehicle, including: an input module for entering sensor data from at least one sensor which is configured to detect the surroundings of the commercial vehicle; an evaluation module for evaluating the sensor data to determine a relative position of the commercial vehicle on a road; a lane-keeping module for controlling a steering system of the commercial vehicle based on a lane-keeping profile that defines a torque to be applied to a steering wheel of the commercial vehicle to support keeping in a lane; and a change module for changing the lane-keeping profile in response to a change in the detected environment. Also described is a related commercial vehicle, method, and computer readable medium.

An automatic parking system is provided. The automatic parking system includes a camera processor that acquires images around a subject vehicle, converts the acquired images into external images and synthesizes the external images. A sensor processor measured spaced distances between the subject vehicle and surrounding vehicles. A parking space recognizing unit periodically receives the spaced distances and the external images and comparing the consecutive external images with the spaced distances using an image recognition technology to recognize parking areas. A controller calculates a moving path between a current position of the subject vehicle and an optimal parking area and operates the subject vehicle based on the moving path.

An automatic parking system is provided. The automatic parking system includes a camera processor that acquires images around a subject vehicle, converts the acquired images into external images and synthesizes the external images. A sensor processor measured spaced distances between the subject vehicle and surrounding vehicles. A parking space recognizing unit periodically receives the spaced distances and the external images and comparing the consecutive external images with the spaced distances using an image recognition technology to recognize parking areas. A controller calculates a moving path between a current position of the subject vehicle and an optimal parking area and operates the subject vehicle based on the moving path.

The technology relates to developing a highly accurate understanding of a vehicle's sensor fields of view in relation to the vehicle itself. A training phase is employed to gather sensor data in various situations and scenarios, and a modeling phase takes such information and identifies self-returns and other signals that should either be excluded from analysis during real-time driving or accounted for to avoid false positives. The result is a sensor field of view model for a particular vehicle, which can be extended to other similar makes and models of that vehicle. This approach enables a vehicle to determine when sensor data is of the vehicle or something else. As a result, the detailed modeling allowing the on-board computing system to make driving decisions and take other actions based on accurate sensor information.

The technology relates to developing a highly accurate understanding of a vehicle's sensor fields of view in relation to the vehicle itself. A training phase is employed to gather sensor data in various situations and scenarios, and a modeling phase takes such information and identifies self-returns and other signals that should either be excluded from analysis during real-time driving or accounted for to avoid false positives. The result is a sensor field of view model for a particular vehicle, which can be extended to other similar makes and models of that vehicle. This approach enables a vehicle to determine when sensor data is of the vehicle or something else. As a result, the detailed modeling allowing the on-board computing system to make driving decisions and take other actions based on accurate sensor information.

A target trajectory generation device generates and outputs target trajectories each including a target position and a target speed of a vehicle. A first target trajectory is intended to perform at least one of steering, acceleration, and deceleration of the vehicle. A second target trajectory is intended to decelerate and stop the vehicle. When a malfunctioning device does not exist, a vehicle traveling control device executes vehicle traveling control based on the first target trajectory. When the malfunctioning device exists, the vehicle traveling control device stops the vehicle by executing the vehicle traveling control based on the second target trajectory output before the malfunction occurs, or based on the second target trajectory output from the target trajectory generation device other than the malfunctioning device.

A driving support apparatus comprises a controller for performing collision avoidance braking control and lane deviation suppressing control. When a performing condition of the lane deviation suppressing control is satisfied at a timing of the collision avoidance braking control is about to be performed, the controller makes direction determination processing for determining whether or not the own vehicle travels to a direction toward which it will collide with a target object or to a direction toward which it will avoid colliding with the target object. In the direction determination processing, when it is determined the own vehicle travels to a collision direction, the controller stops the lane deviation suppressing control to perform the collision avoidance braking control, and when it is determined the own vehicle travels to a collision avoidance direction, the controller performs cooperative control for making the lane deviation suppressing control cooperate with the collision avoidance braking control.

A machine is disclosed. The machine may include at least one of a propulsion system or a steering system configured to operate under automatic control in an autonomous mode of the machine; and a controller configured to obtain one or more parameters associated with a task that is to be performed in the autonomous mode, determine an estimated completion time for the task based on the one or more parameters associated with the task, and perform one or more actions based on the estimated completion time for the task.

A machine is disclosed. The machine may include at least one of a propulsion system or a steering system configured to operate under automatic control in an autonomous mode of the machine; and a controller configured to obtain one or more parameters associated with a task that is to be performed in the autonomous mode, determine an estimated completion time for the task based on the one or more parameters associated with the task, and perform one or more actions based on the estimated completion time for the task.