A rear side warning system for a vehicle includes at least one processor configured to: sense an external obstacle of the vehicle; classify the external obstacle as either one of a fixed object and a moving object; and control a rear side warning signal of the vehicle based on a result of the classifying of the external obstacle.

Systems and methods for determining object prioritization and predicting future object locations for an autonomous vehicle are provided. A method can include obtaining, by a computing system comprising one or more processors, state data descriptive of at least a current or past state of a plurality of objects that are perceived by an autonomous vehicle. The method can further include determining, by the computing system, a priority classification for each object in the plurality of objects based at least in part on the respective state data for each object. The method can further include determining, by the computing system, an order at which the computing system determines a predicted future state for each object based at least in part on the priority classification for each object and determining, by the computing system, the predicted future state for each object based at least in part on the determined order.

An autonomous controller, a system including the same, and a method thereof include a processor that detects a target object attempting to cut in at a low speed during autonomous driving and that performs response control. The controller, system, and method include a storage storing data and an algorithm for detecting the target object and performing the response control. The processor calculates a final distance value on the basis of a point tracking a distance between a host vehicle and the target object and compares the final distance value with a predetermined threshold to determine whether the target object cuts in.

A method for reducing a risk of collision with an obscured object. The method includes detecting, by a detector, the object in proximity of the detector, and determining, by the detector, object data of the detected object in response to the detection. The method further includes determining, by the detector, any vehicle in motion in a direction towards an area where the detected object is or may be present, and transmitting, by the detector, the determined object data of the detected object to the vehicle. The method furthermore includes receiving, by the vehicle, the transmitted object data, processing, by the vehicle, the received object data to assess a collision risk based on a predicted route of the vehicle, and mitigating the collision risk, in the vehicle, based on the processed object data when the collision risk has been assessed.

A method for operating a vehicle assistance system is described. In one embodiment, when the vehicle is in the autonomous driving mode, encountering an obstacle is prevented independent of the passage height, due to a control unit if is determined that the obstacle is a load supported by a working machine, where the load projects into a driving corridor of the vehicle or moves in the driving corridor. Also described is a device for carrying out the method and to a vehicle having such a device.

The present disclosure is directed to apparatus, methods, and non-transitory storage medium for controlling the maximum speed of a vehicle as that vehicle travels along a route. Apparatus and methods consistent with the present disclosure may receive location information from an electronic device that is located at a vehicle and may provide information to the electronic device at that controls the maximum speed of the vehicle as speed limits change along the route.

Generally, the disclosed systems and methods utilize multi-task machine-learned models for object intention determination in autonomous driving applications. For example, a computing system can receive sensor data obtained relative to an autonomous vehicle and map data associated with a surrounding geographic environment of the autonomous vehicle. The sensor data and map data can be provided as input to a machine-learned intent model. The computing system can receive a jointly determined prediction from the machine-learned intent model for multiple outputs including at least one detection output indicative of one or more objects detected within the surrounding environment of the autonomous vehicle, a first corresponding forecasting output descriptive of a trajectory indicative of an expected path of the one or more objects towards a goal location, and/or a second corresponding forecasting output descriptive of a discrete behavior intention determined from a predefined group of possible behavior intentions.

When a collision avoidance target is a pedestrian or a bicycle, a driving assistance ECU performs automatic braking control. In this case, accelerator override cannot be performed. When the collision avoidance target is an automobile and when an accelerator operation amount is equal to or larger than a first operation amount threshold, the driving assistance ECU prohibits the automatic braking control. In this case, the accelerator override can be performed. When the accelerator operation amount is smaller than the first operation amount threshold, the driving assistance ECU performs the automatic braking control.

An anticipating module for a device for controlling, in real time, the path of a motor vehicle includes a sub-module for computing a turning command for compensating for the curvature of a bend in the lane of the vehicle and a variable-gain device that is connected to an output of the computing sub-module. The gain of the variable-gain device is connected to a controller to adjust the gain so as to decrease the lateral offset between the centre of gravity of the vehicle and the centre of the lane of the vehicle depending on the result of the comparison of components of a vector of current measurements of state variables of the device to one another and to a detection threshold, the output of the variable-gain device being the steering command for compensating for the curvature of the bend.

A method for operating a vehicle configured for highly automated or autonomous driving involves adapting control of the driving mode to the weather conditions of a vehicle's environment. Moreover, control of the driving mode is adapted by reference control parameters if the weather conditions deviate from a specified criterion. For generating the reference control parameters, driving behaviors of a plurality of vehicles in the vehicle's environment, adjacent to the vehicle, are determined. From the determined driving behaviors, an average speed, an average distance, an average acceleration, and an average deceleration are determined and are taken into account when generating the reference control parameters.