Provided are systems and methods for a deep learning based beam control. Sensor data associated with the environment and the corresponding detected objects from a perception system are obtained. Object features and image features are extracted. The extracted object features and image features are fused into fused features. A beam control status is predicted according to the fused features, wherein the beam control status indicates a high beam illumination intensity or a low beam illumination intensity of a light emitting device.

A driver assistance system of an ego vehicle is operated. The ego vehicle has at least one surroundings sensor for detecting the surroundings of the ego vehicle. Movements of multiple vehicles are detected with the at least one surroundings sensor in the surroundings of the ego vehicle. A movement model is generated based on the detected movements of the respective vehicles. A traffic situation is ascertained and a probability of correct classification of the traffic situation on the basis of the generated movement model by a machine learning method. The traffic situation and the probability of the correct classification of the traffic situation are ascertained by the machine learning method on the basis of the learned characteristic features of the movement model. The driver assistance system of the ego vehicle is adapted to the ascertained traffic situation.

While a host vehicle is within an area, a target vehicle is identified based on detecting the target vehicle within the area. Upon determining a component output, first instructions specifying the component output and a target vehicle component are provided to the target vehicle. A host clock for the host vehicle is synchronized with a clock maintained by a remote server computer. Then second instructions specifying to initiate, at a target time, actuation of the target vehicle component to provide the component output are provided to the target vehicle. Then a host vehicle component is actuated at a host time to provide the component output.

A method for recognizing which lamps at a vehicle are activated. The method includes: providing multiple image recordings of candidate areas at the vehicle in which an activated lamp is presumed; converting the image recordings into an intermediate product by executing a recurrent encoder network (ERNN), the output of at least one pass of the ERNN is supplied as input to a further pass of the ERNN, and different image recordings of candidate areas are supplied as input to different passes of the ERNN; assignments of the image recordings of candidate areas are ascertained to classes which represent specific lamps of the vehicle from the intermediate product by executing a recurrent decoder network (DRNN) multiple times, the output of at least one pass of the DRNN is supplied as input to a further pass of the DRNN.

A deceleration assistance device including: a target information acquisition unit for acquiring information of a target located in front of a vehicle; a position estimation unit for estimating an estimated position of a deceleration object; a position recognition unit for recognizing a position of the deceleration object; and a control unit for executing, based on the estimated position, first deceleration control of decelerating a vehicle at a first deceleration and executing, based on the recognized position, second deceleration control of decelerating the vehicle at a second deceleration. The control unit executes processing of gradually changing a deceleration of the vehicle from the first deceleration toward the second deceleration when the deceleration control is caused to transition from the first to the second deceleration control, and when a difference exists between the first and second deceleration.

An approach is provided for traffic Signal Phase and Timing (SPaT) verification using sensor data and probe data. The approach involves, for instance, retrieving image data captured using a sensor of a vehicle traveling within proximity of a traffic light. The approach also involves processing the image data to identify at least one transition of the traffic light between one or more traffic light states. The approach further involves determining a transition time, a cycle time, or a combination thereof between the one or more traffic light states based on the identified at least one transition of the traffic light. The approach further involves performing a comparison of signal phase and timing (SPaT) data of the traffic light with the transition time, the cycle time, or a combination thereof and providing the comparison of the SPaT data as an output.

A control apparatus includes a controller configured to acquire driving data regarding driving of a vehicle in which a medical examination can be performed, determine a driving condition of the vehicle based on the acquired driving data, and adjust implementation timing of at least one examination item included in the medical examination according to the determined driving condition.

Systems and methods use cameras to provide autonomous navigation features. In one implementation, a method for navigating a user vehicle may include acquiring, using at least one image capture device, a plurality of images of an area in a vicinity of the user vehicle; determining from the plurality of images a first lane constraint on a first side of the user vehicle and a second lane constraint on a second side of the user vehicle opposite to the first side of the user vehicle; enabling the user vehicle to pass a target vehicle if the target vehicle is determined to be in a lane different from the lane in which the user vehicle is traveling; and causing the user vehicle to abort the pass before completion of the pass, if the target vehicle is determined to be entering the lane in which the user vehicle is traveling.

Techniques and methods for performing collision monitoring using error models. For instance, a vehicle may generate sensor data using one or more sensors. The vehicle may then analyze the sensor data using systems in order to determine parameters associated with the vehicle and parameters associated with another object. Additionally, the vehicle may process the parameters associated with the vehicle using error models associated with the systems in order to determine a distribution of estimated locations associated with the vehicle. The vehicle may also process the parameters associated with the object using the error models in order to determine a distribution of estimated locations associated with the object. Using the distributions of estimated locations, the vehicle may determine the probability of collision between the vehicle and the object.

Traveling environment information is recognized. A predicted traveling path is calculated based on a driving condition of a vehicle. An oncoming-vehicle predicted traveling path is calculated based on behavior of an oncoming vehicle. It is determined whether the vehicle has an intention to enter a first intersecting road at an intersection. When the vehicle cannot enter the first intersecting road, the predicted traveling path is corrected to a limit traveling path. It is determined whether the oncoming vehicle has an intention to enter a second intersecting road at the intersection. When the oncoming vehicle cannot enter the second intersecting road, the oncoming-vehicle predicted traveling path is corrected to an oncoming-vehicle limit traveling path. The oncoming vehicle is set as a control target against which emergency braking is executed when the predicted traveling path and the oncoming-vehicle predicted traveling path overlap each other at least in part.