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.

Provided is a control apparatus for a vehicle configured to perform driving support control when a driving support operation state is an on state, the control apparatus being further configured to, in a case where the driving support operation state is the on state and a driving operation switching request for changing the driving support operation state to an off state is issued, calculate a target speed at a driving operation switching time point at which the driving support operation state is changed from the on state to the off state, and perform deceleration control for decelerating the vehicle such that a speed of the vehicle matches the target speed at the driving operation switching time point.

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 remote parking control system and a fail-safe method thereof may include at least one controller and a remote parking control apparatus that performs braking control according to motor reverse torque control and transmission stage control, when a braking controller among the at least one controller fails, upon remote parking control.

A method of generating an output trajectory of an ego vehicle includes recording trajectory data of the ego vehicle and pedestrian agents from a scene of a training environment of the ego vehicle. The method includes identifying at least one pedestrian agent from the pedestrian agents within the scene of the training environment of the ego vehicle causing a prediction-discrepancy by the ego vehicle greater than the pedestrian agents within the scene. The method includes updating parameters of a motion prediction model of the ego vehicle based on a magnitude of the prediction-discrepancy caused by the at least one pedestrian agent on the ego vehicle to form a trained, control-aware prediction objective model. The method includes selecting a vehicle control action of the ego vehicle in response to a predicted motion from the trained, control-aware prediction objective model regarding detected pedestrian agents within a traffic environment of the ego vehicle.

A system and method for estimating and communicating a path of travel of a reference vehicle by road side equipment (RSE) that includes establishing communication between the RSE and an on-board equipment of the reference vehicle and receiving vehicle parameters of the reference vehicle from the on-board of the reference vehicle. The system and method also include estimating the path of travel of the reference vehicle based on the vehicle parameters of the reference vehicle and environmental parameters determined by the RSE. The system and method further include establishing communication between the RSE and an on-board equipment of a target vehicle and communicating the estimated path of travel of the reference vehicle from the RSE to the target vehicle, wherein a probability of collision between the reference vehicle and the target vehicle is determined based on the estimated path of travel of the reference vehicle.

An object of the present invention is to enhance the reliability of an autonomous driving system. The autonomous driving system includes: a higher-level control device 1 that outputs a control target value of an actuator group based on an action plan of a vehicle; and a lower-level control device 2 that controls the actuator group of the vehicle based on a command from the higher-level control device 1. The lower-level control device 2 holds the control target value of the vehicle provided by the higher-level control device 1 over a specific period. When the higher-level control device 1 does not satisfy a desired function, the lower-level control device 2 is configured to be controlled based on the held control target value. The action plan is followed by determining and correcting a difference between an actual action value and the control target value of the vehicle.

In general, techniques are described by which a computing system detects low-impact collisions. A computing system includes at least one processor and memory. The memory includes instructions that, when executed, cause the at least one processor to determine whether an object collided with a vehicle based on a comparison of data received from at least one motion sensor configured to measure at least an acceleration of the vehicle and data received from a plurality of level sensors, wherein each level sensor is configured to measure a relative position between a body of the vehicle and a respective wheel of a plurality of wheels of the vehicle. Execution of the instructions further causes the at least one processor to perform one or more actions in response to determining that the object collided with the vehicle.

An apparatus for controlling a vehicle includes: a sensor that obtains vehicle surrounding environment information and vehicle driving information; and a controller that determines whether an engagement of an Electronic Parking Brake (EPB) is possible based on the vehicle driving information, performs control for preventing a slip based on the vehicle surrounding environment information upon determining that the engagement of the EPB is impossible, calculates a steering angle for preventing the slip, transmits the steering angle to a portable terminal, receives a steering control command from the portable terminal, and controls steering based on the received steering control command.

A driving assistance system includes a front camera module (FCM). The system, responsive to processing captured image data from the FCM, generates FCM control signals. The system includes a plurality of vehicle sensors capturing sensor data and an advanced driving-assistance system (ADAS) controller. The ADAS controller, responsive to processing the sensor data, generates ADAS control signals. The ADAS controller generates an ADAS status signal indicating a reliability of the generated ADAS control signals. With the ADAS status signal indicating the reliability of the generated ADAS control signals is at or above a threshold ADAS reliability level, the system controls the vehicle using the ADAS control signal and, responsive to determining that the ADAS status signal indicates that the reliability of the generated ADAS control signals is below the threshold, switches from controlling the vehicle based on the ADAS control signals to controlling the vehicle based on the FCM control signals.