Abstract: A region recognition method includes obtaining a scene image of a scene to-be-recognized; determining a specified boundary curve of the scene image; generating a contour map corresponding to the scene image according to the specified boundary curve; and determining a target region in the scene according to the contour map.

Abstract: The disclosure relates to a table (100). The table (100) has a table top (102), a plurality of legs (104) connectable to the table top (102), and an air purifier (106) adapted to be placed below the table top (102). A bottom side of the table top (102) is provided with at least one groove (112) extending in a circumferential direction for preventing liquid from traveling from an outer edge of the table top (102) along the bottom side of the table top (102) and reaching an inner section of the air purifier (106). The disclosure also relates to a method for purifying air using a table (100).

Abstract: A vehicle track prediction method and device, a storage medium and a terminal device are provided. The method includes: determining an obstacle vehicle entering a junction region in a case that an autonomous vehicle enters the junction region; acquiring historical traveling data of the obstacle vehicle in the junction region; predicting a potential track of the obstacle vehicle according to the historical traveling data and a current traveling state of the obstacle vehicle; and predicting a track of the autonomous vehicle in the junction region according to the potential track of the obstacle vehicle and a current traveling state of the autonomous vehicle. A decision making accuracy in self-driving may be effectively improved, and a driving risk may be reduced.

Abstract: A vehicle track prediction method and device, a storage medium and a terminal device are provided. The method includes: determining an obstacle vehicle entering a junction region in a case that an autonomous vehicle enters the junction region; acquiring historical traveling data of the obstacle vehicle in the junction region; predicting a potential track of the obstacle vehicle according to the historical traveling data and a current traveling state of the obstacle vehicle; and predicting a track of the autonomous vehicle in the junction region according to the potential track of the obstacle vehicle and a current traveling state of the autonomous vehicle. A decision making accuracy in self-driving may be effectively improved, and a driving risk may be reduced.

Abstract: A track prediction method and device for an obstacle at a junction are provided. The method includes: acquiring environment information of a junction to be passed by a vehicle, and acquiring information on a visible obstacle in a sensible range of the vehicle, wherein the environment information comprises road information and information on a junction obstacle located in an area of the junction; combining the information on the junction obstacle with the information on the visible obstacle, and selecting information on a blind-zone obstacle in a blind zone of the vehicle at the junction; and predicting a moving track of an obstacle corresponding to the information on the blind-zone obstacle, according to the road information.

Abstract: A travelling track prediction method and device for a vehicle are provided. The method includes that: calculating a plurality of potential positions to which is to be reached by a target vehicle within a preset time within a sensible range of a main vehicle; selecting at least two positions from the plurality of potential positions as target positions; predicting a plurality of travelling tracks to each target position for the target vehicle based on travelling state information of the target vehicle; and selecting at least one travelling track from the plurality of travelling tracks as a prediction result of the target vehicle based on environment information around the target vehicle. According to the embodiments, the travelling track of the target vehicle around the main vehicle may be accurately predicted based on travelling state information and the environment information.