Provided is a vehicle control device which, when traffic participants are waiting in the vicinity of a railroad crossing for a train to pass, appropriately controls driving of a vehicle about to pass through the railroad crossing. Entry of the vehicle into the railroad crossing is restrained until a waiting time elapses since when the railroad crossing transitioned from a passage blocking state to a passage allowing state, the waiting time being set in accordance with the kind or the number of the traffic participants present in the vicinity of the railroad crossing. When the waiting time has elapsed, the vehicle is caused to enter the railroad crossing and pass (through) the railroad crossing.

Systems and methods are provided for navigating based on sensed brake light patterns. In one implementation, a navigation system for a host vehicle may include at least one processing device. The at least one processing device may be programmed to receive, from a camera, a plurality of images representative of an environment ahead of the host vehicle; analyze the plurality of images to identify at least one target vehicle in the environment ahead of the host vehicle; identify, based on analysis of the plurality of images, at least one brake light associated with the target vehicle and at least one characteristic associated with changes in an illumination state of the at least one brake light; and cause a navigational change for the host vehicle based on the identified at least one characteristic associated with the changes in the illumination state of the at least one brake light.

A vehicle control system includes a vehicle selector configured to select, from among other vehicles present near an own vehicle, another vehicle that the own vehicle is to follow when traveling in front of a gate as a following target vehicle, and a gate passage controller configured to cause the own vehicle to travel following the following target vehicle selected by the vehicle selector when passing through the gate.

A method and a device for operating an assistance system of a vehicle involves detecting laterally static and laterally dynamic objects, which the vehicle is to drive past, as lateral boundary objects. A respective lateral distance of the vehicle from the respective lateral boundary object is detected. A speed of the respective laterally dynamic object is determined and at least the respectively laterally dynamic object is classified according to its type. A set of characteristic curves is stored in a control unit of the vehicle, the characteristic curves of the set being assigned in each case to an environmental situation predetermined depending on lateral boundary objects. It is predetermined by a respective characteristic curve for the respective environmental situation at what maximum speed the vehicle is to drive past a lateral boundary object at different lateral distances from the latter.

A vehicle-behavior prediction device includes: a priority determination section that determines the priority of a host vehicle and a vehicle concerned when the host vehicle and the vehicle concerned pass through a road section; and a vehicle control section that sets the time from when the vehicle concerned stops to when the host vehicle starts action to avoid the vehicle concerned to be shorter in a case where the priority of the host vehicle is low than in a case where the priority of the host vehicle is high.

For one embodiment of the present disclosure, systems and methods for dynamically responding to detected changes in a semantic map of an autonomous vehicle (AV) are described. A computer implemented method includes obtaining sensor signals from a sensor system of the AV to monitor driving operations, processing the sensor signals for sensor observations of the sensor system, determining whether a map change exists between the sensor observations and a prerecorded semantic map, determining whether the map change is located in a planned route of the AV when the map change exists, and generating a first scenario with a first priority level to to stop the AV at a safe location based on a location of the map change.

A computing device including interface for receiving from a sensor device sensor data representative of an environment surrounding a host vehicle, and a processor configured to obtain a planned driving action for accomplishing a navigational goal of a host vehicle operating in a first lane of a roadway, identify, from the sensor data, a moving target vehicle located in a second lane of the roadway, identify, based on the target vehicle speed and direction, the target vehicle predicted trajectory indicating a cut-in movement of the target vehicle from the second lane to the first lane, identify an intersection of a planned trajectory for the host vehicle with the predicted trajectory for the target vehicle, and determine a safety action of the host vehicle to respond to the movement of the target vehicle; and cause the safety action to be performed in the host vehicle.

A method is provided for controlling the movement of a host vehicle. Lane data related to road items are determined from a data source, and object data acquired from objects are determined via a sensor system. The road items and the objects are located in an external environment of the host vehicle. A machine-learning algorithm is fed with the lane data and the object data to generate a set of controlling functions for controlling the movement of the host vehicle by performing the steps of: classifying a predefined set of maneuvers for the host vehicle based on the lane data and the object data, and determining a set of controlling functions related to kinematics of the host vehicle. The movement of the host vehicle is controlled based on the classified maneuvers and the determined controlling functions.

Provided are a control unit and a method in a control unit to automatically control a lane change assist function in a vehicle. Multiple conditions indicating that a lane change assist function are activated at a current position of the vehicle are evaluated in the control unit. The conditions are selected from sensor based conditions, historical based conditions, and map based conditions. Sensor based conditions are conditions based on sensor. Historical based conditions are conditions based on historical data received. Map based conditions are conditions based on digital map data. If conditions from at least two different groups of conditions are evaluated as met, a digital signal is provided in the control unit enabling activation of the lane change assist function in the vehicle.

An onboard hazard detection system for installing on a vehicle includes a processor that is configured to receive sensed data that is acquired by one or more sensors that are installed on the vehicle and that are configured to sense a topography of a region in the vicinity of the vehicle. The received data is analyzed to generate a three-dimensional map of the region based on the sensed data. One or more characteristics of an approach of the vehicle toward a cliff or safety barrier is calculated, based on the sensed data. The system detects when the calculated characteristics are indicative of a hazard and performs an action.