Abstract: A method for monitoring surroundings of a first sensor system. The method includes: providing a temporal sequence of data of the first sensor system for monitoring the surroundings; generating an input tensor including the temporal sequence of data of the first sensor system, for a trained neural network; the neural network being configured and trained to identify, on the basis of the input tensor, at least one subregion of the surroundings, in order to improve the monitoring of the surroundings with the aid of a second sensor system; generating a control signal for the second sensor system with the aid of an output signal of the trained neural network, in order to improve the monitoring of the surroundings in the at least one subregion.

Abstract: A method is for tracking an object using an environment sensor. The object is represented by an object status. The method includes detecting a sensor value of the environment sensor, predicting a future object status of the object, and updating the object status using a Bayesian filter. The updating includes using an artificial intelligence module (“AI module”). The AI module is trained such that the detected sensor value is associated with the object and the object status of the object is updated based on the predicted future object status of the object and the detected sensor value.

Abstract: A method for the detection of an object in an environment of a vehicle as a function of sensor signals of a sensor for acquiring the environment of the vehicle. The method includes: processing the sensor signals using a region proposal network to obtain at least one object hypothesis per anchor, the object hypothesis including an object probability and a bounding box; selecting the best object hypothesis on the basis of a quality model, the quality model being a function of the anchor and the bounding box of the object hypothesis; identifying redundant object hypotheses relative to the selected object hypothesis, the redundant object hypotheses being identified as a function of the anchors of the redundant object hypotheses, using a target function assigned to the region proposal network; and fusing the selected object hypothesis with the identified redundant object hypotheses for the object detection.

Abstract: A method for creating a training dataset, a validation dataset, and/or a test dataset for an AI module from measurement data includes dividing the measurement data into divided portions based on time periods, applying a mathematical function to the divided portions of the measurement data in order to obtain signatures representing the divided portions, determining a measure of a frequency of occurrence of a respective signature of the obtained signatures, and creating the training dataset, the validation dataset, and/or the test dataset from the measurement data based on the determined measure of the frequency.

Abstract: A method for the detection of an object in an environment of a vehicle as a function of sensor signals of a sensor for acquiring the environment of the vehicle. The method includes: processing the sensor signals using a region proposal network to obtain at least one object hypothesis per anchor, the object hypothesis including an object probability and a bounding box; selecting the best object hypothesis on the basis of a quality model, the quality model being a function of the anchor and the bounding box of the object hypothesis; identifying redundant object hypotheses relative to the selected object hypothesis, the redundant object hypotheses being identified as a function of the anchors of the redundant object hypotheses, using a target function assigned to the region proposal network; and fusing the selected object hypothesis with the identified redundant object hypotheses for the object detection.

Abstract: A lift truck includes a frame, a pair of laterally spaced apart outriggers extending from the frame, and a load handling assembly secured to the frame adjacent to the outriggers. The load handling assembly includes a mast assembly positioned between the outriggers and a carriage assembly including fork structure for supporting a load on the load handling assembly. The carriage assembly is movable vertically along the mast assembly and laterally with respect to the mast assembly. Optical sensor structure of the truck monitors for conditions wherein movement of the carriage assembly would result in contact between the load and the outrigger(s). A vehicle controller receives a signal from the optical sensor structure and prevents movement of the carriage assembly toward the outrigger(s) if the signal from the optical sensor structure indicates that such movement would result in contact between the load and the outrigger(s).

Abstract: A lift truck includes a frame, a pair of laterally spaced apart outriggers extending from the frame, and a load handling assembly secured to the frame adjacent to the outriggers. The load handling assembly includes a mast assembly positioned between the outriggers and a carriage assembly including fork structure for supporting a load on the load handling assembly. The carriage assembly is movable vertically along the mast assembly and laterally with respect to the mast assembly. Optical sensor structure of the truck monitors for conditions wherein movement of the carriage assembly would result in contact between the load and the outrigger(s). A vehicle controller receives a signal from the optical sensor structure and prevents movement of the carriage assembly toward the outrigger(s) if the signal from the optical sensor structure indicates that such movement would result in contact between the load and the outrigger(s).