A method for qualification of a camera system and/or its image/camera signals of a partially autonomously or autonomously driving vehicle. Infrastructure information from a vehicle's surroundings is used. The image/camera signal of the camera system is monitored by a monitoring function with regard to at least the following criteria: image/camera signal in recurring, known time windows, sequence of the image/camera signals, corruption of the image/camera signals, signature checking of the camera system. Checking of the image/camera signals using unmoving land markings that are reconciled with a list is carried out and they are checked for a mapping signature. Then the image/camera signals and the infrastructure information contained in the updated map check function are communicated to an evaluation function and merged and compared in a signal fusion unit. Consequently, a qualified image/camera signal is obtained.

Provided are methods for selection of optimal minimal risk maneuver, which can include receiving at least one first parameter associated with a characteristic of a vehicle and at least one second parameter associated with at least one object external to the vehicle, generating at least one future state for at least one of the first and second parameters, selecting at least one maneuver from a plurality of maneuvers based on the generated future state, determining at least one reward value associated with the selected maneuver, updating the selected maneuver based on the determined reward value to generate an updated maneuver, and operating the vehicle based on the updated maneuver. Systems and computer program products are also provided.

Methods and systems for managing a speed of a vehicle are provided. The methods and systems obtain image data from one or more vision sensors disposed onboard a vehicle. A stopping distance of the vehicle is determined based at least in part on the image data. A moving speed of the vehicle and a speed limit of the vehicle are determined. The speed limit is determined based on the stopping distance that is determined from the image data. The methods and systems control movement of the vehicle based on a difference between the moving speed of the vehicle and the speed limit of the vehicle.

A vehicle control device comprises a unit to detect a distance to an obstacle; a unit to detect a speed of the vehicle; and a reduction support unit to, based on the speed and the distance, perform support for reduction of a speed of the vehicle. The reduction support unit changes the distance at which the reduction support is to be performed between a case where driving during parking and a case where not driving during parking. The distance at which the reduction support is to be performed is, for a first speed range, longer in a case where driving during parking than in a case where not driving during parking, and, for a second speed range, longer in a case where not driving during parking than in a case where driving during parking.

A system for determining a content of a message used to coordinate interactions among vehicles can include a processing device and a memory. The memory can store a discretization module and a communications module. The discretization module can include instructions that when executed by the processing device cause the processing device to: (1) analyze a critical maneuver trajectory of an ego vehicle and a current trajectory of another vehicle to determine an existence of a critical maneuver situation and (2) produce a discretized representation of a portion of the critical maneuver trajectory of the ego vehicle. A form of the discretized representation can be based on the existence of the critical maneuver situation. The communications module can include instructions that when executed by the processing device cause the processing device to communicate the discretized representation as the content of the message used to coordinate interactions among vehicles.

The control device for forward collision avoidance in a vehicle includes a forward object determination unit configured to recognize an object in front of the vehicle and to determine an attribute of the recognized object, a gear position detection unit configured to detect a gear position of the vehicle, and a forward collision-avoidance assist (FCA) control unit configured to finally determine the attribute of the object determined by the forward object determination unit according to the gear position input from the gear position detection unit and to set an FCA control range based on the finally determined object attribute.

A movable carrier auxiliary system includes an environmental detecting device, a control device, a state detecting device, and a parking auxiliary device. The environmental detecting device includes an image capturing module and an operation module. A parking auxiliary method thereof includes capture an environmental image around a movable carrier with the image capturing module; analyze whether the environmental image has a parking space with the operation module; detect a movement state of the movable carrier with the state detecting device; generate a prompting message with the parking auxiliary device based on an analysis result of the operation module and the movement state of the movable carrier, thereby the driver could manipulate the control device based on the prompting message to move the movable carrier to the parking space, improving a convenience and a safety when parking the movable carrier.

A method for clearing passage of a tunnel in the automated driving mode of a vehicle involves checking, in a manual driving mode with passive automated driving mode of the vehicle, whether the tunnel is suitable for passage in the automated driving mode of the vehicle. During the check control commands for actuators of a longitudinal and transverse movement of the vehicle are generated, but are not implemented by the actuators. In the event that no unmanageable situation is detected in the tunnel during the check and the generated control commands are plausible in relation to interventions made by a vehicle user in manual driving mode with passive automated driving mode, the tunnel is assessed as suitable for automated driving mode.

Techniques for determining unified futures of objects in an environment are discussed herein. Techniques may include determining a first feature associated with an object in an environment and a second feature associated with the environment and based on a position of the object in the environment, updating a graph neural network (GNN) to encode the first feature and second feature into a graph node representing the object and encode relative positions of additional objects in the environment into one or more edges attached to the node. The GNN may be decoded to determine a predicted position of the object at a subsequent timestep. Further, a predicted trajectory of the object may be determined using predicted positions of the object at various timesteps.

A method includes receiving, iteratively over time, sets of data including vehicle dynamics data, image data, sound data, third-party data, and wind speed sensor data, each detected at an autonomous vehicle and associated with a time period. The method also includes estimating a first wind speed and a first wind direction for each time period, in response to receiving the sets of data and based on the sets of data, via a processor of the autonomous vehicle. The method also includes iteratively modifying a lateral control and/or a longitudinal control of the autonomous vehicle based on the estimated first wind speed and the estimated first wind direction, via the processor of the autonomous vehicle and during operation of the autonomous vehicle.