A driving assistance apparatus is configured to perform an assistance control of assisting in driving a vehicle, when a first condition and a second condition are satisfied, in a situation in which a target is recognized. The driving assistance apparatus is provided with: a determinator configured to determine a state of the assistance control. The determinator is configured (i) to determine that the state of the assistance control is a standby state if a standby condition is satisfied, wherein the standby condition requires that the first condition is satisfied, but the second condition is not satisfied, in the situation in which the target is recognized, and (ii) to determine that the state of the assistance control is an interruption state if an interruption condition is satisfied, wherein the interruption condition requires that the first condition is no longer satisfied while the satisfaction of the standby condition is continued.

A system for user interaction with an automated device includes a control system configured to operate the device during an operating mode corresponding to a first state in which the control system automatically controls the device operation, and the operating mode prescribes that a user monitor the device operation during automated control. The control system is configured to allocate a time period for the device to transition to a temporary state in which automated control is maintained and the user is permitted to stop monitoring and perform a task unrelated to device operation. The system includes a user interaction system including a visual display configured to present trajectory information, an indication as to whether an area is conducive to putting the device in the temporary state, and time period allocation information, the user interaction system including an interface engageable by the user to manage scheduling of allocated time period(s).

In various embodiments, methods, systems, and vehicles are provided for executing a lane change for a host vehicle. In various embodiments, a method includes: receiving, by a processor, an indication that a lane change from an initial lane to an intended lane is desired for the host vehicle; defining, by the processor, an initial lane center target, a negotiation target, and an intended lane center target based on the desired lane change; and controlling, by the processor, the host vehicle to at least one of the initial lane center target, the negotiation target, and the intended lane center target based on a finite state machine, wherein the initial lane center target is at or in proximity to a determined center of the initial lane, wherein the intended lane center target is at or in proximity to a determined center of the intended lane, and wherein the negotiation target is offset from the initial lane center target and within the initial lane.

A driving assistance apparatus is configured to perform an assistance control of assisting in driving a vehicle, when a first condition and a second condition are satisfied, in a situation in which a target is recognized. The driving assistance apparatus is provided with: a determinator configured to determine a state of the assistance control. The determinator is configured (i) to determine that the state of the assistance control is a standby state if a standby condition is satisfied, wherein the standby condition requires that the first condition is satisfied, but the second condition is not satisfied, in the situation in which the target is recognized, and (ii) to determine that the state of the assistance control is an interruption state if an interruption condition is satisfied, wherein the interruption condition requires that the first condition is no longer satisfied while the satisfaction of the standby condition is continued.

Systems, methods, computer-readable media, techniques, and methodologies are disclosed for performing end-to-end, learning-based keypoint detection and association. A scene graph of a signalized intersection is constructed from an input image of the intersection. The scene graph includes detected keypoints and linkages identified between the keypoints. The scene graph can be used along with a vehicle's localization information to identify which keypoint that represents a traffic signal is associated with the vehicle's current travel lane. An appropriate vehicle action may then be determined based on a transition state of the traffic signal keypoint and trajectory information for the vehicle. A control signal indicative of this vehicle action may then be output to cause an autonomous vehicle, for example, to implement the appropriate vehicle action.

An apparatus and a method in an autonomous vehicle detect a traffic flow obstruction target. The apparatus detects information about at least one of a speed of another vehicle, a driving path of the other vehicle, or a position of the other vehicle. The apparatus calculates a degree to which the other vehicle interferes with traffic flow, based on the detected information and based on high definition map information stored in a memory. The apparatus selects a traffic flow obstruction target, based on the degree to which the other vehicle interferes with the traffic flow. The apparatus detects a target causing bypass driving, which is present on a driving path, to enhance the continued operation of autonomous driving.

The present disclosure relates to driving decision-making methods, apparatuses, and chips. One example method includes building a Monte Carlo tree based on a current driving environment state, where the Monte Carlo tree includes a root node and N-1 non-root nodes, each node represents one driving environment state, and a driving environment state represented by any non-root node is predicted by a stochastic model of driving environments. Based on at least one of an access count or a value function of each node in the Monte Carlo tree, a node sequence that starts from the root node and ends at a leaf node is determined, and a driving action sequence is determined based on a driving action corresponding to each node in the node sequence.

Systems, methods, computer-readable media, techniques, and methodologies are disclosed for performing end-to-end, learning-based keypoint detection and association. A scene graph of a signalized intersection is constructed from an input image of the intersection. The scene graph includes detected keypoints and linkages identified between the keypoints. The scene graph can be used along with a vehicle's localization information to identify which keypoint that represents a traffic signal is associated with the vehicle's current travel lane. An appropriate vehicle action may then be determined based on a transition state of the traffic signal keypoint and trajectory information for the vehicle. A control signal indicative of this vehicle action may then be output to cause an autonomous vehicle, for example, to implement the appropriate vehicle action.

An electrical infrastructure system and method of use of the system for a vehicle. There are several electronic control units (ECU) for one or several functional units (30n) for the vehicle. The ECUs are connected through a network (32). The infrastructure system is configured to implement a state map including various operational states Sn that the vehicle can adopt. These operational states are connected by one or several transitions Tn, where the transition from one operational state to another depends on predetermined transition conditions being satisfied. The infrastructure system is configured to receive one or several input signals (34) to at least one ECU, comprising parameter values that represent events. The at least one ECU is configured to analyze the input signals with the aid of the transition conditions, to determine an operational state, and to make the operational state that has been determined available on the network (32).

Autonomous control of a subject vehicle including a longitudinal motion control system includes determining states of parameters associated with a trajectory for the subject vehicle and parameters associated with a control reference determined for the subject vehicle. A range control routine is executed to determine a first parameter associated with a range control command based upon the states of the plurality of parameters, and a speed control routine is executed to determine a second parameter associated with a speed control command based upon the states of the plurality of parameters. An arbitration routine is executed to evaluate the range control command and the speed control command, and operation of the subject vehicle is controlled to achieve a desired longitudinal state, wherein the desired longitudinal state is associated with a minimum of the range control command and the speed control command.