A controller that controls driving of an autonomously moving vehicle includes a first sensor that detects an obstacle and a direction of travel of the vehicle, and a processor that sets a virtual region surrounding the vehicle. Processor stops the vehicle when the obstacle is detected therein, determines whether the obstacle is present in the direction of travel, determines whether the vehicle has been stopped for a predetermined amount of time when the obstacle is present, reduces a length of the virtual region in the direction of travel to provide an adjusted virtual region when the vehicle is determined to have been stopped for the predetermined amount of time, causes the vehicle to drive when the obstacle is not detected within the adjusted virtual region, and stops the vehicle when the obstacle is detected within the adjusted virtual region.

An information processing apparatus acquires first information on a behavior of a first vehicle associated with each operation performed by a first driver on the first vehicle and second information on a change in emotion of a user who shares the first vehicle, extracts information on a change in the user's emotion associated with each behavior of the first vehicle by associating the first information with the second information, determines a user's evaluation of the first driver's driving based on information on the change in the user's emotion associated with each behavior of the first vehicle, and stores the determined evaluation in a storage unit.

Systems and methods of determining trajectories of an actor in an environment in which a vehicle is operating are provided. The method includes detecting an actor that may move within a scene in the environment by an object detection system of a vehicle in the environment, determining a kinematic history of the actor, and using context of the scene and the kinematic history of the actor to determine a plurality of reference polylines for the actor. The method further includes generating a contextual embedding of the kinematic history of the actor to generate a plurality of predicted trajectories of the actor, in which the generating conditions each of the predicted trajectories to correspond to one of the reference polylines. The method additionally includes using, by the vehicle, the plurality of predicted trajectories to plan movement of the vehicle.

An autonomous vehicle configured for active sensing may also be configured to weigh expected information gains from active-sensing actions against risk costs associated with the active-sensing actions. An example method involves: (a) receiving information from one or more sensors of an autonomous vehicle, (b) determining a risk-cost framework that indicates risk costs across a range of degrees to which an active-sensing action can be performed, wherein the active-sensing action comprises an action that is performable by the autonomous vehicle to potentially improve the information upon which at least one of the control processes for the autonomous vehicle is based, (c) determining an information-improvement expectation framework across the range of degrees to which the active-sensing action can be performed, and (d) applying the risk-cost framework and the information-improvement expectation framework to determine a degree to which the active-sensing action should be performed.

A determination is made regarding whether an autonomous motor vehicle is located on a specified lane marking. If so, the autonomous motor vehicle is controlled on the specified lane marking and the vehicle speed is limited to a specified speed value. If the autonomous motor vehicle is not located on the specified lane marking, the vehicle speed is limited to a specified safe value.

A system and method for the detection of inconsistencies in perception systems of autonomous vehicles is described. The system receives the observations of objects in the surrounding environment from one or more sensors or perception systems of an automated vehicle. At actual time, the system estimates the consistency of the currently observed elements of the perception system according to the previous inputs received. This consistency is decided by calculating the boundaries of possible states of the previously observed elements, based on the received information and on assumptions.

A driving assistance device includes a determination unit configured to, in a case where a preceding vehicle located in front of a vehicle in a stopped state starts, determine whether an object is located inside a specified area that is a predetermined area in front of the vehicle, and a driving assistance unit configured to maintain the stopped state of the vehicle in a case where an object is located inside the specified area in a case where the preceding vehicle starts.

A method includes determining identification information of a vehicle entering a space within a structure or building. The method also includes determining an expected route of the vehicle within the space. The method also includes determining venue map data corresponding to the expected route, the venue map data indicating one or more safety devices disposed at one or more locations along or near the expected route. The method also includes repeatedly tracking a current location of the vehicle as the vehicle moves within the space. The method also includes in response to determining that the vehicle is in proximity to a first safety device of the one or more safety devices, instructing the first safety device to emit an alert.

A braking control system includes obstacle detection means for detecting an obstacle ahead of a vehicle, first collision determination means for determining whether the vehicle would collide with the obstacle ahead of the vehicle, following vehicle detection means for detecting a following vehicle traveling behind the vehicle, information acquisition means for acquiring a maximum deceleration set in the following vehicle, second collision determination means for determining whether the following vehicle would collide with the vehicle based on the maximum deceleration, and braking control means for controlling braking means of the vehicle so that an absolute value of a deceleration of the vehicle does not exceed an absolute value of the maximum deceleration of the following vehicle when the first collision determination means determines that the vehicle would collide with the obstacle and the second collision determination means determines that the following vehicle would collide with the vehicle.

A system and method for providing long term and key intentions for trajectory prediction that include receiving image data and LiDAR data associated with RGB images and LiDAR point clouds that are associated with a surrounding environment of an ego agent and processing a long term and key intentions for trajectory prediction dataset (LOKI dataset) that is utilized to complete joint trajectory and intention prediction for heterogeneous traffic agents. The system and method also include encoding a past observation history of each of the heterogeneous traffic agents and sampling a respective goal. The system and method further include decoding and predicting future trajectories associated with each of the heterogeneous traffic agents based on data included within the LOKI dataset, the encoded past observation history, and the respective goal.