Provided is a control method of an automatic driving vehicle switchable between manual driving in which the vehicle is made to travel depending on an operation of an occupant and automatic driving in which driving characteristics in automatic travel are set and the vehicle is made to automatically travel based on the driving characteristics. Manual driving characteristics in the manual driving by the occupant are learned and, when switching from the manual driving to the automatic driving is performed, the automatic driving is performed with the manual driving characteristics maintained for a preset manual characteristic maintaining time. As a result, uneasiness of the occupant can be suppressed.

A vehicle safety support apparatus includes: a driver monitoring sensor configured to monitor a driver; an external environment monitoring sensor configured to monitor an external environment of a vehicle; and at least one processor configured to: determine whether the vehicle is in an immediate hazard situation based on data acquired from the driver monitoring sensor and the external environment monitoring sensor; determine, in response to determining that the vehicle is in the immediate hazard situation, whether to perform a recovery maneuver or a rescue maneuver based on the data acquired from the driver monitoring sensor and the external environment monitoring sensor to get out of the immediate hazard situation; and perform, in response to determining to perform the rescue maneuver, autonomous driving to move the vehicle to a safe area by taking over a driving control from the driver.

Methods and systems are disclosed for determining sensor degradation by actively controlling an autonomous vehicle. Determining sensor degradation may include obtaining sensor readings from a sensor of an autonomous vehicle, and determining baseline state information from the obtained sensor readings. A movement characteristic of the autonomous vehicle, such as speed or position, may then be changed. The sensor may then obtain additional sensor readings, and second state information may be determined from these additional sensor readings. Expected state information may be determined from the baseline state information and the change in the movement characteristic of the autonomous vehicle. A comparison of the expected state information and the second state information may then be performed. Based on this comparison, a determination may be made as to whether the sensor has degraded.

Arrangements related to operating an autonomous vehicle in view-obstructed environments are described. At least a portion of an external environment of the autonomous vehicle can be sensed to detect one or more objects located therein. An occupant viewable area of the external environment can be determined. It can be determined whether one or more of the detected one or more objects is located outside of the determined occupant viewable area. Responsive to determining that a detected object is located outside of the determined occupant viewable area, one or more actions can be taken. For instance, the action can include presenting an alert within the autonomous vehicle. Alternatively or in addition, the action can include causing a current driving action of the autonomous vehicle to be modified.

A navigation system for a host vehicle is provided. The system may comprise at least one processing device programmed to receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify a navigational state associated with the host vehicle; determine a first predefined navigational constraint and a second predefined navigational constraint implicated by the navigational state; determine, based on the identified navigational state, a first navigational action for the host vehicle where both the first predefined navigational constraint and the second predefined navigational constraint can be satisfied; determine, based on the identified navigational state, a second navigational action for the host vehicle where the first predefined navigational constraint and the second predefined navigational constraint cannot both be satisfied; and cause at least one adjustment of a navigational actuator of the host vehicle.

Provided are methods and systems for semantic behavior filtering for prediction improvement. A method for operating an autonomous vehicle is provided. The method includes obtaining, by at least one processor, semantic image data associated with an environment in which an autonomous vehicle is operating. The method includes determining, by the at least one processor, at least one agent in the environment. The method includes determining a predicted action for the at least one agent. The method includes determining an agent predicted path for the at least one agent. The method includes determining a vehicle path of the autonomous vehicle. The method includes determining a predicted collision of the at least one agent and the autonomous vehicle. The method includes simulating actions to avoid the predicted collision. The method includes categorizing the predicted collision as a primary predicted collision based on the simulating actions.

Provided are methods and systems for semantic behavior filtering for prediction improvement. A method for operating an autonomous vehicle, is provided. The method includes obtaining, by at least one processor, semantic image data associated with an environment where an autonomous vehicle is operating. The method includes determining, by the at least one processor, a set of agents in the environment based on the semantic image data. The method includes determining a set of predicted actions for at least one agent of the set of agents. The method includes determining, from the set of predicted actions, a set of secondary predicted actions for the at least one primary agent using semantic data. The method includes determining, from the set of predicted actions, a set of primary predicted actions other than secondary predicted actions The method includes generating a path for the autonomous vehicle based on the set of primary predicted actions.

The technologies described herein relate to a radar system that is configured to generate point clouds based upon radar tensors generated by the radar system. More specifically, the radar system is configured to generate heatmaps based upon radar tensors, wherein a neural network receives the radar tensors as input and constructs the heatmaps as output. Point clouds are generated based upon the heatmaps. A computing system detects objects in an environment of an autonomous vehicle (AV) based upon the point clouds, and the computing system further causes the AV to perform a driving maneuver based upon the detected objects.

A system for trajectory prediction using a predicted endpoint conditioned network includes one or more processors and a memory that includes a sensor input module, an endpoint distribution module, and a future trajectory module. The modules cause the one or more processors to the one or more processors to obtain sensor data of a scene having a plurality of pedestrians, determine endpoint distributions of the plurality of pedestrians within the scene, the endpoint distributions representing desired end destinations of the plurality of pedestrians from the scene, and determine future trajectory points for at least one of the plurality of pedestrians based on prior trajectory points of the plurality of pedestrians and the endpoint distributions of the plurality of pedestrians. The future trajectory points may be conditioned not only on the pedestrian and their immediate neighbors' histories (observed trajectories) but also on all the other pedestrian's estimated endpoints.

A method for adaptive risk modeling for an autonomous vehicle, the method comprising: retrieving parameters of an identified driving mission of the autonomous vehicle; in response to the parameters of the identified driving mission, generating values of: a comparative autonomous parameter, a mix model parameter, a surrounding risk parameter, a geographic operation parameter, and a security risk parameter upon evaluating situational inputs associated with the identified driving mission with a comparative autonomous model, a mix model, a sensor-surrounding model, a geography-dependent model, and a security risk model generated using sensor and supplementary data extraction systems associated with the autonomous vehicle; upon generating values, generating a risk analysis with a rule-based algorithm; and contemporaneously with execution of the identified driving mission, implementing a response action associated with control of the autonomous vehicle, based upon the risk analysis.