The present disclosure provides a method and apparatus for determining an attribute value of an obstacle in vehicle infrastructure cooperation. The method includes: acquiring vehicle-end data collected by at least one sensor of an autonomous driving vehicle; acquiring vehicle wireless communication V2X data transmitted by a roadside device; and fusing, in response to determining that an obstacle is at an edge of a blind spot of the autonomous driving vehicle, the vehicle-end data and the V2X data to obtain an attribute estimated value of the obstacle.

The present disclosure provides a method and apparatus for determining an attribute value of an obstacle in vehicle infrastructure cooperation. The method includes: acquiring vehicle-end data collected by at least one sensor of an autonomous driving vehicle; acquiring vehicle wireless communication V2X data transmitted by a roadside device; and fusing, in response to determining that an obstacle is at an edge of a blind spot of the autonomous driving vehicle, the vehicle-end data and the V2X data to obtain an attribute estimated value of the obstacle.

An embodiment vehicle includes a camera configured to acquire an external appearance image of the vehicle, a first sensor provided on an outside of the vehicle and configured to detect a position of an object adjacent to the vehicle, an alarm configured to output an alarm notification, and a controller configured to determine a reference area based on the external appearance image of the vehicle and to control the alarm to output the alarm notification when the detected position of the object adjacent to the vehicle is within the reference area.

An embodiment vehicle includes a camera configured to acquire an external appearance image of the vehicle, a first sensor provided on an outside of the vehicle and configured to detect a position of an object adjacent to the vehicle, an alarm configured to output an alarm notification, and a controller configured to determine a reference area based on the external appearance image of the vehicle and to control the alarm to output the alarm notification when the detected position of the object adjacent to the vehicle is within the reference area.

A computer-implemented method for detecting an obstacle on a route ahead of a first vehicle. In the method, information on a second vehicle driving ahead on the route is recorded in the first vehicle by at least one sensor of the first vehicle. In the first vehicle, depending on the recorded information, a computer detects an avoidance maneuver of the second vehicle due to an obstacle or detects that the second vehicle has driven over an obstacle. An obstacle is detected on the route depending on the detected avoidance maneuver or the detection that the vehicle has driven over an obstacle. A measure for protecting the vehicle and/or the obstacle is initiated depending on the detected obstacle.

A computer-implemented method for detecting an obstacle on a route ahead of a first vehicle. In the method, information on a second vehicle driving ahead on the route is recorded in the first vehicle by at least one sensor of the first vehicle. In the first vehicle, depending on the recorded information, a computer detects an avoidance maneuver of the second vehicle due to an obstacle or detects that the second vehicle has driven over an obstacle. An obstacle is detected on the route depending on the detected avoidance maneuver or the detection that the vehicle has driven over an obstacle. A measure for protecting the vehicle and/or the obstacle is initiated depending on the detected obstacle.

In general, techniques are described by which a computing system detects low-impact collisions. A computing system includes at least one processor and memory. The memory includes instructions that, when executed, cause the at least one processor to determine whether an object collided with a vehicle based on a comparison of data received from at least one motion sensor configured to measure at least an acceleration of the vehicle and data received from a plurality of level sensors, wherein each level sensor is configured to measure a relative position between a body of the vehicle and a respective wheel of a plurality of wheels of the vehicle. Execution of the instructions further causes the at least one processor to perform one or more actions in response to determining that the object collided with the vehicle.

In general, techniques are described by which a computing system detects low-impact collisions. A computing system includes at least one processor and memory. The memory includes instructions that, when executed, cause the at least one processor to determine whether an object collided with a vehicle based on a comparison of data received from at least one motion sensor configured to measure at least an acceleration of the vehicle and data received from a plurality of level sensors, wherein each level sensor is configured to measure a relative position between a body of the vehicle and a respective wheel of a plurality of wheels of the vehicle. Execution of the instructions further causes the at least one processor to perform one or more actions in response to determining that the object collided with the vehicle.

A vehicle is provided to automatically move according to a movement request received from a surrounding vehicle already parked when the vehicle is parked around the surrounding parked vehicle. The vehicle includes a communication unit and a sensor sensing an external surrounding. A steering device steers the vehicle wheels and a power device transmits power to the wheels. In response to receiving vehicle identification information and movement request from the surrounding parked vehicle through the communication unit, a controller determines whether the surrounding vehicle has an authority for the movement request based on the external surrounding and the vehicle identification information. The power device and the steering device are operated based on the movement request when the surrounding parked vehicle has the authority.

Systems, devices, computer-implemented methods, and/or computer program products that facilitate dynamic curve speed control adaptive to road conditions. In one example, a system can comprise a process that executes computer executable components stored in memory. The computer executable components can comprise a curvature component, a road condition component, and a safety component. The curvature component can generate composite curvature data for a curve of a road preceding a vehicle using digital map data and lane marker data. The road condition component can generate friction data for a surface of the road using sensor data obtained from an on-board sensor of the vehicle. The safety component can determine a safe operational profile for traversing the curve using the composite curvature data and the friction data.