Aspects of the disclosure provide for generation of trajectories for a vehicle driving in an autonomous driving mode. In one instance, a default number of trajectories to be generated may be identified. A set of maneuvering options may be selected from a set of predetermined maneuvering options based on the number of trajectories. The set of maneuvering options may be filtered based on the default number of trajectories. A set of trajectories may be generated based on the filtered set of maneuvering option such that each trajectory of the set corresponds to a different maneuvering behavior. A cost for each trajectory of the set of trajectories may be determined, and one of the trajectories of the set of trajectories may be selected based on the determined costs. The vehicle may be maneuvered in the autonomous driving mode according to the selected one of the trajectories.

An autonomous driving system of a vehicle includes: an autonomous module configured to, during autonomous driving, control at least one of: steering of the vehicle; braking of the vehicle; and acceleration and deceleration of the vehicle; and a driving control module configured to: enable and disable autonomous driving; determine a future time for beginning a period of autonomous driving; and at least one of: selectively delay the beginning of autonomous driving to after the future time; and cancel the period of autonomous driving.

Techniques for determining a safety area for a vehicle are discussed herein. In some cases, a first safety area can be based on a vehicle travelling through an environment and a second safety area can be based on a steering control or a velocity of the vehicle. A width of the safety areas can be updated based on a position of a bounding box associated with the vehicle. The position can be based on the vehicle traversing along a trajectory. Sensor data can be filtered based on the sensor data falling within the safety area(s).

Techniques and methods for determining regions. For instance, a vehicle may determine a trajectory of the vehicle and a trajectory of an agent, such as a pedestrian. The vehicle may then determine one or more contextual factors. In some examples, the one or more contextual factors are associated with a location of the agent with respect to a crosswalk, a location of the vehicle with respect to the crosswalk, a state of the crosswalk, and/or the like. The vehicle may then determine the region using the trajectory of the vehicle, the trajectory of the agent, and the one or more contextual factors. Additionally, using a time buffer value and a distance buffer value associated with the region, the vehicle may determine whether to yield to the agent within the region.

A method for controlling a vehicle. In the method, data of a digital road map are read in, zones are determined for the digital road map, and possible sequences of trips along a road of the digital road map are ascertained as a function of the determined zones. Furthermore, it is ascertained, as a function of sensor data and/or current driving data of the vehicle, whether a current or predicted traffic situation is outside the possible sequences or corresponds to a possible sequence that is determined as being outside an intended operating range. If the current or predicted traffic situation is outside the possible sequences or corresponds to the possible sequence outside the intended operating range, a measure is determined and the vehicle is controlled as a function of the measure that is taken.

An intersection navigation system includes: a complexity module configured to determine a complexity value for an intersection of two or more roads, where the complexity value for the intersection corresponds to a level of complexity for a vehicle to navigate the intersection during autonomous driving; and a driving control module configured to: during autonomous driving of a vehicle, control at least one of: steering of the vehicle; braking of the vehicle; and acceleration of the vehicle; and based on the complexity value of the intersection, selectively adjust at least one aspect of the autonomous driving.

Disclosed are an intersection driving control system and method for vehicles, the intersection driving control system for vehicles including a monitoring array configured to recognize and monitor peripheral vehicles driving proximate to a host vehicle, a pattern detector configured to detect reduction patterns of the peripheral vehicles in a longitudinal direction, and an intersection entry confirmer configured to confirm whether the host vehicle enters an intersection based on whether the longitudinal reduction patterns of the peripheral vehicles are similar to an intersection entry pattern.

A vehicle control apparatus includes: a road recognizer that recognizes a road form around a vehicle; a second-vehicle recognizer that recognizes a state of another vehicle around the vehicle; and a driving controller that allows the vehicle to travel by controlling one or both of steering and acceleration/deceleration of the vehicle and that prevents, upon passing of the vehicle through an intersection, passing of the vehicle through the intersection based on a presence of the other vehicle recognized by the second-vehicle recognizer, wherein in a case where the driving controller recognizes, by the road recognizer, that a plurality of lanes are present in a road of a right/left turn destination of the vehicle and recognizes, by the second-vehicle recognizer, that the other vehicle, which was an opposing vehicle approaching from a direction opposing the vehicle, has entered a lane on a rear side in a view from the vehicle among the plurality of lanes in the road of the right/left turn destination, the driving controller continues an entry control to the road of the right/left turn destination of the vehicle.

Techniques are discussed for predicting locations of an object based on attributes of the object and/or attributes of other object(s) proximate to the object. The techniques can predict locations of a pedestrian proximate to a crosswalk as they traverse or prepare to traverse through the crosswalk. The techniques can predict locations of objects as the object traverses an environment. Attributes can comprise information about an object, such as a position, velocity, acceleration, classification, heading, relative distances to regions or other objects, bounding box, etc. Attributes can be determined for an object over time such that, when a series of attributes are input into a prediction component (e.g., a machine learned model), the prediction component can output, for example, predicted locations of the object at times in the future. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on the predicted locations.

A system and a method for controlling movement of a robotic vehicle. The robotic vehicle comprises a computer system, a chassis controller, and a plurality of sensors. Data measured by the plurality of sensors is received by the computer system. A base trajectory and an emergency trajectory for the robotic vehicle are generated based on the data. During normal operations of the robotic vehicle the chassis controller causes the robotic vehicle to travel along the base trajectory. After determining that an error has occurred, the chassis controller causes the robotic vehicle to travel along the emergency trajectory.