Abstract: A method of detecting lanes includes the steps: capturing (S1) a camera image (K) of a vehicle environment by a camera device (2) of a vehicle (5); determining (S2) feature points (P1 to P15) in the camera image (K), which feature points correspond to regions of possible lane boundaries (M1, M2); generating (S3) image portions of the captured camera image (K) respectively around the feature points (P1 to P15); analyzing (S4) the image portions using a neural network to classify the feature points (P1 to P15); and determining (S5) lanes in the vehicle environment taking account of the classified feature points (P1 to P15).

Abstract: The invention relates to a method for detecting lanes (F), having the steps of: capturing (S1) a camera image (K) of a vehicle environment by means of a camera device (2) of a vehicle (5); determining (S2) feature points (P1 to P15) in the captured camera image (K), which correspond to regions of possible lane boundaries (M1, M2); generating (S3) image portions of the captured camera image (K) around each feature point (P1 to P15); analyzing (S4) the image portions using a neural network in order to classify the feature points (P1 to P15); and determining (S5) lanes in the vehicle environment taking account of the classified feature points (P1 to P15).

Abstract: A method for automatically establishing extrinsic parameters of a camera of a vehicle includes repeatedly establishing the extrinsic parameters of the camera during a measurement run, wherein sensor data are generated by a sensor each time the extrinsic parameters of the camera are established. The established parameters and the associated sensor data respectively are stored in a database. A convolutional neural network is trained based on the extrinsic parameters stored in the database and based on the sensor data. The extrinsic parameters of the camera are subsequently determined online by utilizing the trained convolutional neural network.

Abstract: A method for automatically establishing extrinsic parameters of a camera of a vehicle includes repeatedly establishing the extrinsic parameters of the camera during a measurement run, wherein sensor data are generated by a sensor each time the extrinsic parameters of the camera are established. The established parameters and the associated sensor data respectively are stored in a database. A convolutional neural network is trained based on the extrinsic parameters stored in the database and based on the sensor data. The extrinsic parameters of the camera are subsequently determined online by utilizing the trained convolutional neural network.