A system and a method for autonomous decisioning and operation by an autonomous agent includes: collecting decisioning data including: collecting a first stream of data includes observation data obtained by onboard sensors of the autonomous agent, wherein each of the onboard sensors is physically arranged on the autonomous agent; collecting a second stream of data includes observation data obtained by offboard infrastructure devices, the offboard infrastructure devices being arranged geographically remote from and in an operating environment of the autonomous agent; implementing a decisioning data buffer that includes the first stream of data from the onboard sensors and the second stream of data from the offboard sensors; generating current state data; generating/estimating intent data for each of one or more agents within the operating environment of the autonomous agent; identifying a plurality of candidate behavioral policies; and selecting and executing at least one of the plurality of candidate behavioral policies.

A system and a method for autonomous decisioning and operation by an autonomous agent includes: collecting decisioning data including: collecting a first stream of data includes observation data obtained by onboard sensors of the autonomous agent, wherein each of the onboard sensors is physically arranged on the autonomous agent; collecting a second stream of data includes observation data obtained by offboard infrastructure devices, the offboard infrastructure devices being arranged geographically remote from and in an operating environment of the autonomous agent; implementing a decisioning data buffer that includes the first stream of data from the onboard sensors and the second stream of data from the offboard sensors; generating current state data; generating/estimating intent data for each of one or more agents within the operating environment of the autonomous agent; identifying a plurality of candidate behavioral policies; and selecting and executing at least one of the plurality of candidate behavioral policies.

A vehicle includes: a detection sensor configured to acquire a front view of the vehicle, and configured to detect an obstacle in the front view; and a controller including at least one processor which is configured to process data obtained from the detection sensor. The controller may be configured to determine a predicted position of a collision between the vehicle and the obstacle, determine an avoidance position capable of avoiding a collision with the obstacle based on the collision prediction position, generate a plurality of avoidance paths corresponding to a plurality of predetermined conditions, respectively, based on the avoidance position, and control a steering angle adjustment unit to follow any one of the plurality of avoidance paths.

A vehicle includes an automated driving assistance system that controls maneuvering of the vehicle under certain conditions. When the vehicle comes to a stop, the driving assistance system dynamically selects a threshold stop time corresponding to a duration of time that the vehicle can remain stopped before the driving assistance system will either detect a physical action from the user to resume automated driving assistance or time out and cease the driving assistance.

For a vehicle at a first location, a blind zone is defined that is outside fields of view of available vehicle sensors. A path is determined that avoids the blind zone and moves the vehicle away from the first location. The path includes a straight portion having a first end at the first location, and a turning portion starting at a second end of the straight portion. The straight portion is defined such that, at the second end of the straight portion, the fields of view encompass the blind zone. The vehicle is determined to operate along the turning portion based on obtaining sensor data from the blind zone upon reaching the end of the straight portion.

A computer-implemented method for controlling a vehicle comprises: receiving tracking data associated with a surrounding environment of the vehicle; detecting, based upon the tracking data, an object in the surrounding environment of the vehicle; determining a location of the object; determining, based on navigation assistance data, whether the location of the object is at least partially within a classified area in the surrounding environment; and configuring a control system of the vehicle to: initiate, based upon determining that the location of the object is not at least partially within the classified area, a first collision avoidance response procedure for responding to the object; and initiate, based upon determining that the location of the object is at least partially within the classified area, a second collision avoidance response procedure for responding to the object, the second collision avoidance response procedure different from the first collision avoidance response procedure.

Provided is an artificial intelligence (AI) amphibious vertical take-off and landing modular hybrid flying automobile. The automobile may include a vehicle and a drone. The vehicle may include a vehicle body, a chassis, an engine, a transmission unit, a steering unit, a brake unit, an AI vehicle control unit, and one or more batteries. The vehicle may further include a wind turbine, a fuel cell stack, a hydrogen storage tank, an AI control unit, a plurality of sensors, and an obstacle detection module in communication with the plurality of sensors. The obstacle detection module may be configured to detect an obstacle and activate the brake unit. The drone may include a connection unit configured to releasably attach to a top of the vehicle body of the vehicle, a drone body, propellers configured to provide a vertical take-off and landing, and an AI drone control unit.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for autonomous driving with surfel maps. In some implementations, a three-dimensional representation of a real-world environment is obtained. Each of the surfels can correspond to a respective point of plurality of points in a three-dimensional space of the real-world environment. Input sensor data is received from multiple sensors installed on the autonomous vehicle. A pedestrian is detected from the input sensor data. A determination is made that the pedestrian is located behind a barrier. A driving plan is updated based on determining that the pedestrian is located behind a barrier.

A mobile robot can be caused to move according to a planned trajectory. The mobile robot can be a vehicle. Information about agents in an environment of the mobile robot can be received from sensors. At a first time, a spatiotemporal graph can be produced. The spatiotemporal graph can represent relationships among the agents in the environment. The mobile robot can be one of the agents in the environment. Information from the spatiotemporal graph can be input to neural networks to produce information for a mixture of affine time-varying systems. The mixture of affine time-varying systems can represent an evolution of agent states of the agents. Using the mixture of affine time-varying systems and information associated with the first time, a prediction of the agent states at a second time can be calculated. The mobile robot can be caused to move according to the planned trajectory determined from the prediction.

A pedestrian collision prevention system of a vehicle including a detection unit to detect one or both of a fixed object and a pedestrian located in a vicinity of the vehicle; a prediction unit to predict a collision possibility with the pedestrian based on one or both of the fixed object and the pedestrian detected by the detection unit; and a control unit to set a detection sensitivity based on the collision possibility predicted by the prediction unit and to control one or both of generation of a warning signal and driving of the vehicle based on the detection sensitivity.