The invention relates to a method for detecting an object (303, 304) in the vicinity of a road user. The method recognizes objects (303, 304) detected by the sensor unit (101) with the object recognition unit (102) of a device (100), stores detected objects (303, 304) in an object list with the memory unit (103), transfers the object list with the transfer unit (104) to a data cloud (306), determines a position of the first road user, transmits the position of the first road user to the data cloud (306), identifies an object (303, 304) near the first traffic participant in the data cloud (306) using the object list, transmits a position and an object type of the identified object (303, 304) from the data cloud (306) to a mobile device of the first road user and displays the object (303, 304) and its position on the mobile device of the first road user.

A method of preventing a blind spot collision includes recognizing a reflector installed on a road using a front camera, determining whether at least one other vehicle image exists in the reflector, determining whether the at least one other vehicle is a preceding vehicle or an opposite vehicle through image analysis when the at least one other vehicle image exists in the reflector, generating a braking command when the at least one other vehicle is the preceding vehicle, and generating a steering torque for maintaining a lane when the at least one other vehicle is the opposite vehicle.

A system and method for inducing vehicles to yield right of way determines whether a common vehicle is an emergency vehicle through a cloud, which transmits and receives requests and response signals between vehicles, when the common vehicle transmits a request signal showing that the common vehicle is an emergency vehicle, and informs surrounding vehicles that there is an emergency vehicle in a driving path and simultaneously provides a yielding driving guide signal, which includes a driving direction for yielding driving, a lane change direction of the emergency vehicle, whether to accelerate and decelerate, etc., to the surrounding vehicles so that yielding driving may be easily performed for specific vehicles with an emergency patient other than emergency vehicles such as an ambulance and a fire truck.

Techniques for a perception system of a vehicle that can detect and track objects in an environment are described herein. The perception system may include a machine-learned model that includes one or more different portions, such as different components, subprocesses, or the like. In some instances, the techniques may include training the machine-learned model end-to-end such that outputs of a first portion of the machine-learned model are tailored for use as inputs to another portion of the machine-learned model. Additionally, or alternatively, the perception system described herein may utilize temporal data to track objects in the environment of the vehicle and associate tracking data with specific objects in the environment detected by the machine-learned model. That is, the architecture of the machine-learned model may include both a detection portion and a tracking portion in the same loop.

An autonomous system may selectively displace human driver control of a host vehicle. The system may receive an image representative of an environment of the host vehicle and detect an obstacle in the environment of the host vehicle based on analysis of the image. The system may monitor a driver input to a throttle, brake, and/or steering control associated with the host vehicle. The system may determine whether the driver input would result in the host vehicle navigating within a proximity buffer relative to the obstacle. If the driver input would not result in the host vehicle navigating within the proximity buffer, the system may allow the driver input to cause a corresponding change in one or more host vehicle motion control systems. If the driver input would result in the host vehicle navigating within the proximity buffer, the system may prevent the driver input from causing the corresponding change.

Disclosed are methods, systems, and non-transitory computer readable media that control an autonomous vehicle via at least two sensors. One aspect includes capturing an image of a scene ahead of the vehicle with a first sensor, identifying an object in the scene at a confidence level based on the image, determining the confidence level of the identifying is below a threshold, in response to the confidence level being below the threshold, directing a second sensor having a field of view smaller than the first sensor to generate a second image including a location of the identified object, further identifying the object in the scene based on the second image, controlling the vehicle based on the further identification of the object.

An image processing device includes an information acquisition unit, a condition determination unit, and an image change unit. The information acquisition unit acquires information indicating a position of an emergency vehicle. The condition determination unit determines, based on the information acquired by the information acquisition unit, (a) a first condition which is satisfied when the emergency vehicle is determined to be approaching the subject vehicle, and (b) a second condition which is satisfied when a distance from the emergency vehicle to the subject vehicle or an estimated reach time required for the emergency vehicle to reach the subject vehicle is equal to or less than a preset threshold. The image change unit changes the image in response to the first condition and the second condition being satisfied during an autonomous driving state of the subject vehicle. The image after change is an overhead view image.

A vehicle control apparatus that controls a vehicle, comprising a controller configured to execute lane departure suppression control to suppress the vehicle from departing from a division line, wherein in a case in which the shape of a road is a curve, the controller delays an operation timing of the lane departure suppression control compared to a case in which the shape of the road is not the curve, and in a case in which the shape of the road is the curve and an oncoming vehicle is detected, the controller decreases an amount of delay of the operation timing of the lane departure suppression control compared to a case in which the shape of the road is the curve and the oncoming vehicle is not detected.

A system includes multiple microphone arrays positioned at different locations on a roof of an autonomous vehicle. Each microphone array includes two or more microphones. Internal clocks of each microphone array are synchronized by a processor and used to generate timestamps indicating when microphones capture a sound. Based on the timestamps, the processor is configured to localize a source of the sound.

When a vehicle performing driverless operation of encounters a blockade situation, a probable blockade time duration of the blockade situation is predicted based on a situational analysis. Support by a teleoperator is requested when the predicted blockade time duration of the blockade situation is greater than a predetermined time duration or when the vehicle has waited longer than the predicted blockade time duration for a resolution of the blockade situation.