# US 2025/0061698 A1 · Controller for providing a classification of sensor data for a motor vehicle by means of an optical neural network, and method for operating the controller > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0061698 A1 - Document ID: US20250061698A1 - Status: Published application - Kind: A1 - Application number: 18/723,311 - Filed: 2022-12-12 - Published: 2025-02-20 - Earliest claimed: 2021-12-28 - Assignees: Volkswagen Aktiengesellschaft - Applicants: Volkswagen Aktiengesellschaft - Inventors: Heiko Gustav Kurz - Web page: https://patentgrep.com/patent/US20250061698A1 ## CPC - G06V 10/82: using neural networks - G06N 3/0499: Feedforward networks - G06N 3/0675: {using electro-optical, acousto-optical or opto-electronic means} - G06V 20/56: exterior to a vehicle by using sensors mounted on the vehicle - G02F 3/02: Optical bistable devices - G06V 10/454: {Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]} - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G02F: OPTICAL DEVICES OR ARRANGEMENTS FOR THE CONTROL OF LIGHT BY MODIFICATION OF THE OPTICAL PROPERTIES OF THE MEDIA OF THE ELEMENTS INVOLVED THEREIN; NON-LINEAR OPTICS; FREQUENCY-CHANGING OF LIGHT; OPTICAL LOGIC ELEMENTS; OPTICAL ANALOGUE/DIGITAL CONVERTERS ## Abstract A controller for providing classification of sensor data for a motor vehicle using an optical neural network and a method for operating the controller are disclosed. The controller includes an optical neural network configured to provide optically coded sensor data and evaluate the data using multiple optical neurons with electromagnetically induced transparency properties. The evaluation results in optically coded classification information, which is converted into electronically coded classification information by a conversion device within the controller. The electronically coded classification information is then provided for use by the motor vehicle. ## Claims ### Claim at position 1 1-14. (canceled) ### Claim 15 A control device for providing a classification of sensor data for a motor vehicle, the control device comprising: an optical neural network configured to evaluate optically coded sensor data, the optical neural network comprising a plurality of optical neurons having electromagnetically induced transparency characteristics and, as a result of the evaluation by means of an evaluation device of the optical neural network, to provide optically coded classification information describing the classification of the sensor data; a conversion device configured to convert the optically coded classification information into electronically coded classification information; and the control device configured to provide the electronically coded classification information for the motor vehicle. ### Claim 16 (depends on 15) The control device according to claim 15, wherein the at least one optical neuron comprises a non-linear optical medium, comprising a quantum dot, quantum wire, quantum well, and/or vapor cell, with at least one atom or molecule having a predefined energy level. ### Claim 17 (depends on 15) The control device according to claim 15, wherein the optical neural network comprises: at least one layer with a plurality of optical neurons; at least one first optical waveguide configured to couple a probe laser beam and a coupling laser beam into each optical neuron; and at least one second optical waveguide configured to couple the probe laser beam away from each optical neuron to another optical neuron or directly to the evaluation device after passing through the optical neuron. ### Claim 18 (depends on 17) The control device according to claim 17, wherein the optical neural network is configured to evaluate the absorption spectrum and/or transmission spectrum of the probe laser beam in the at least one second optical waveguide to provide optically coded classification information, including an extreme value and/or phase of the spectrum. ### Claim 19 (depends on 17) The control device according to claim 17, wherein the optical neural network comprises at least one optical modulator, which is configured to influence at least one property of the probe laser beam, including an amplitude, a polarization, a frequency, a wavelength, and/or a phase of the probe laser beam. ### Claim 20 (depends on 17) The control device according to claim 17, wherein the optical neural network comprises a radiation source, which is configured to irradiate the at least one optical neuron with electromagnetic radiation so that the absorption spectrum and/or the transmission spectrum of the probe laser beam that is coupled into the at least one second optical waveguide undergoes splitting utilizing the dynamic Stark effect, and wherein the optical neural network is configured to evaluate the split absorption spectrum and/or transmission spectrum for providing the optically coded classification information. ### Claim 21 (depends on 20) The control device according to claim 20, wherein the optical neural network is configured to set a frequency, a phase curve, and/or an amplitude of the electromagnetic radiation, based on the sensor data. ### Claim 22 (depends on 15) The control device according to claim 15, wherein the control device comprises a processor unit, including a computer and/or a graphics processor, and a further conversion device, the processor unit being configured to provide electronically coded sensor data, and the further conversion device being configured to convert the provided electronically coded sensor data into the optically coded sensor data and to provide these. ### Claim 23 (depends on 15) The control device according to claim 15, wherein the conversion device is configured to interfere local oscillator information, which is provided by a local oscillator, with the provided optically coded classification information so as to provide the electronically coded classification information. ### Claim 24 (depends on 15) The control device according to claim 15, wherein the optical neural network is arranged on at least one chip, which is designed as at least one of the following components: an electronic and photonic cointegrated semiconductor chip; a photonic integrated circuit; a multi-chip module; and/or a chip mounted by means of flip chip assembly. ### Claim 25 (depends on 15) The control device according to claim 15, wherein the control device comprises a communication interface to a control unit of a sensor device of the motor vehicle and is configured to receive sensor data detected by means of the sensor device via the communication interface. ### Claim 26 A method for providing a classification of sensor data for a motor vehicle, the method comprising: evaluating optically coded sensor data using an optical neural network, the optical neural network comprising a plurality of optical neurons having electromagnetically induced transparency characteristics; providing optically coded classification information describing the classification of the sensor data as a result of the evaluation by means of an evaluation device of the optical neural network; converting the optically coded classification information into electronically coded classification information using a conversion device; and providing the electronically coded classification information for the motor vehicle. ### Claim 27 (depends on 26) The method according to claim 26, further comprising using a non-linear optical medium, comprising a quantum dot, quantum wire, quantum well, and/or vapor cell, with at least one atom or molecule having a predefined energy level, to facilitate the optical neurons' evaluation of sensor data. ### Claim 28 (depends on 26) The method according to claim 26, further comprising: coupling a probe laser beam and a coupling laser beam into each optical neuron via at least one first optical waveguide; and coupling the probe laser beam away from each optical neuron to another optical neuron or directly to the evaluation device via at least one second optical waveguide after passing through the optical neuron. ### Claim 29 (depends on 28) The method according to claim 28, further comprising evaluating the absorption spectrum and/or transmission spectrum of the probe laser beam in the at least one second optical waveguide to provide optically coded classification information, including an extreme value and/or phase of the spectrum. ### Claim 30 (depends on 28) The method according to claim 28, further comprising influencing at least one property of the probe laser beam, including amplitude, polarization, frequency, wavelength, and/or phase, using at least one optical modulator. ### Claim 31 (depends on 28) The method according to claim 28, further comprising: irradiating the at least one optical neuron with electromagnetic radiation to cause splitting of the absorption spectrum and/or transmission spectrum of the probe laser beam that is coupled into the at least one second optical waveguide utilizing the dynamic Stark effect; and evaluating the split absorption spectrum and/or transmission spectrum to provide the optically coded classification information. ### Claim 32 (depends on 31) The method according to claim 31, further comprising setting a frequency, a phase curve, and/or an amplitude of the electromagnetic radiation based on the sensor data. ### Claim 33 (depends on 26) The method according to claim 26, further comprising interfering local oscillator information, which is provided by a local oscillator, with the optically coded classification information to provide the electronically coded classification information. ### Claim 34 A vehicle, comprising: a control device for providing a classification of sensor data for a motor vehicle, the control device comprising: an optical neural network configured to evaluate optically coded sensor data, the optical neural network comprising a plurality of optical neurons having electromagnetically induced transparency characteristics and, as a result of the evaluation by means of an evaluation device of the optical neural network, to provide optically coded classification information describing the classification of the sensor data; a conversion device configured to convert the optically coded classification information into electronically coded classification information; and the control device configured to provide the electronically coded classification information for the motor vehicle.