Systems and methods for operating a robot. The methods comprise: performing, by a processor, operations to detect an object that is moving; identifying, by the processor, detected behavior of the object that constitutes an unrecognized behavior; predicting, by the processor, future movement of the object based on a circle having a radius that is function of a velocity of the object; and controlling operations of the robot based on the predicting.

Disclosed herein are systems and method including a method for managing an autonomous vehicle. The method includes obtaining labels associated with various aspects of right-of-way interactions between an autonomous vehicle and an agent, wherein the right-of-way interactions occur when a human driver takes over for the autonomous vehicle and performs the right-of-way interactions, running an autonomous vehicle stack that is untrained for processing right-of-way interactions between the autonomous vehicle and the agent, injecting the labels into the autonomous vehicle stack and determining, based on the injecting of the labels into the autonomous vehicle stack, a performance of the autonomous vehicle stack.

The present invention provides a local trajectory planning method and apparatus for a smart vehicle, pre-acquiring path planning information from a starting location to a destination; the method comprising: determining a target lane; sampling alternative curves from a current location of the smart vehicle to a target lane according to the path planning information; performing speed planning for the sampled alternative curves according to a current travel environment; selecting one of the alternative curves after the speed planning is performed as a target trajectory. Local trajectory planning of the smart vehicle is achieved through the present invention.

The present technology pertains to predicting trajectories of objects near an autonomous vehicle. The predictions may be obtained as output from a trajectory prediction machine learning model. The inputs to the trajectory prediction machine learning model may be based on a first map of an area surrounding an autonomous vehicle, and a second map of an area around an object within the first area. The second map may have a smaller area and a higher resolution relative to the first map.

A driving supporting apparatus comprises an acceleration operation element operated by a driver; and a control unit capable of performing an acceleration limiting control to control the vehicle in such a manner that an acceleration of the vehicle does not exceed a predetermined limiting acceleration. The control unit sets a distance threshold to a value varying depending on a kind of a front target object that is present in front of the vehicle, and performs the acceleration limiting control when a distance condition and an erroneous operation condition are satisfied. The distance condition is a condition that is to be satisfied when a distance between the vehicle and the front target object is equal to or shorter than the distance threshold. The erroneous operation condition is a condition that is to be satisfied when the driver is erroneously operating the acceleration operation element.

Embodiments described herein may provide a method for management of vehicles at intersections. Methods may include: receiving probe data from probe apparatuses proximate an intersection between two or more road segments; identifying a first vehicle approaching the intersection from the probe data; determining, for the first vehicle approaching the intersection, a safe stop distance, where the safe stop distance includes a distance to stop the first vehicle from a current speed of the first vehicle; generating a warning message in response to the safe stop distance of the first vehicle being greater than a distance of the first vehicle from the intersection; and providing the warning message to one or more other vehicles approaching, at, or within the intersection, where the warning message provides an indication that the first vehicle cannot safely stop before entering the intersection,

The subject disclosure relates to techniques for improving performance of a machine learning algorithm that at least receives data descriptive of a 3-D object and provides an output, where the 3-D object occurs infrequently in a training dataset. A process of the disclosed technology can include determining that the machine learning algorithm performed below a threshold performance score when receiving data descriptive of the 3-D object in a real-world scene, wherein the 3-D object is classified as a first type of object, creating at least one 3-D representation of the first type of object for use in a simulation, modifying a plurality of simulated scenes to include the at least one 3-D representation of the first type of object, and training the machine learning algorithm with the modified simulated scenes, whereby the machine learning algorithm has greater exposure to the first type of object.

A lane management system for operating an automated vehicle includes a navigation-device, a vehicle-detector, and a controller suitable for use on a host-vehicle. The navigation-device is used to determine a preferred-route to a destination of the host-vehicle. The vehicle-detector is used to determine a relative-location of an other-vehicle proximate to the host-vehicle. The controller is in communication with the navigation-device and the vehicle-detector. The controller is configured to determine an alternate-route when the relative-location is such that a preferred-lane of the preferred-route is obstructed whereby the host-vehicle is unable to follow the preferred-route. Alternatively, the controller is configured to determine an initiate-time to perform a lane-change necessary to maneuver the host-vehicle into a preferred-lane of the preferred-route so the host-vehicle can follow the preferred-route, wherein the initiate-time is determined based on the relative-location.

A collision determination apparatus is provided. The collision determination apparatus is provided with an acquiring unit that acquires a posture and movement characteristics of an object to be determined whether a collision risk is present; and a reliability determination unit that determines a reliability of the movement characteristics using the posture and the movement characteristics acquired by the acquiring unit.

Systems and methods for an autonomous vehicle are provided. In one aspect, an autonomous vehicle includes a perception sensor and a processor configured to: receive detected roadway conditions data including roadway grade data from the perception sensor, retrieve mapped data having grade data, and determine that the roadway has a grade based on the detected roadway grade data and the retrieved roadway grade data. The processor can be further configured to, in response to determining that the roadway has a grade, determine that the grade of the roadway is greater than or equal to a predetermined high grade value and less than a predetermined grade limit, and in response to determining that the grade of the roadway is greater than or equal to the predetermined high grade value and less than the predetermined grade limit, operate the autonomous vehicle to change lane to a right-most lane.