Detection of objects outside the field of view of an optical sensor

Abstract

A system for detecting objects in an environment surrounding a vehicle includes an optical sensor having a base field of view (FOV), and an optical element disposed proximate the optical sensor, the optical element configured to receive light from an area outside the base FOV and direct the received light to the optical sensor. The system also includes a processor configured to receive an image from the optical sensor and detect an object in the area based on a visual artifact produced in the received image by the received light.

Description

Technical Field

[0001] This subject matter disclosure relates to sensing devices and monitoring systems. More specifically, this subject matter disclosure relates to the detection of traffic signals, traffic lights, and / or other objects. Background Technology

[0002] Vehicles (such as cars, trucks, airplanes, construction equipment, farm equipment, and automated factory equipment) are increasingly equipped with sensor systems for monitoring their surroundings. Optical cameras, radar, and / or lidar systems can be used to detect and track objects, for example, to avoid obstacles. Furthermore, such systems can be used to monitor the traffic environment, including other vehicles and traffic control equipment (such as traffic lights and stop signs), for purposes such as autonomous control and / or driver assistance. Summary of the Invention

[0003] In one exemplary embodiment, a system for detecting objects in an environment surrounding a vehicle includes an optical sensor having a basic field of view (FOV) and optical elements disposed close to the optical sensor, the optical elements being configured to receive light from an area outside the basic FOV and direct the received light to the optical sensor. The system also includes a processor configured to receive images from the optical sensor and detect objects in the area based on visual artifacts produced by the received light in the received images.

[0004] In addition to one or more features described herein, the optical elements are configured to allow light within the basic FOV to illuminate the optical sensor without being affected by the optical elements.

[0005] In addition to one or more features described herein, optical elements include diffraction gratings.

[0006] In addition to one or more features described herein, the diffraction grating is configured to be turned on to guide the received light and turned off so that light from that region does not illuminate the optical sensor.

[0007] In addition to one or more features described herein, the diffraction grating includes a body having a transparent central region, and the body is configured such that the transparent central region corresponds to the basic FOV of the optical sensor.

[0008] In addition to one or more features described herein, a diffraction grating is a polarization grating having a first portion and a second portion, the first portion being configured to diffract received light in a first direction and the second portion being configured to diffract received light in a second direction opposite to the first direction.

[0009] In addition to one or more features described in this paper, visual artifacts are chromatic aberrations in a received image.

[0010] In addition to one or more of the features described in this article, the object is a traffic light.

[0011] In another exemplary embodiment, a method for detecting objects in an environment surrounding a vehicle includes monitoring the environment and collecting images generated by an optical sensing device, the optical sensing device including an optical sensor having a basic field of view (FOV) and optical elements disposed close to the optical sensor, the optical elements being configured to receive light from a region outside the basic FOV and direct the received light to the optical sensor. The method also includes generating images by the optical sensing device, analyzing the images to detect visual artifacts produced in the images by the received light, and detecting objects in the region based on the visual artifacts.

[0012] In addition to one or more features described herein, the optical elements are configured to allow light within the basic FOV to illuminate the optical sensor without being affected by the optical elements.

[0013] In addition to one or more features described herein, optical elements include diffraction gratings.

[0014] In addition to one or more features described herein, the diffraction grating is configured to be activated to guide received light and deactivated so that light from that region does not illuminate the optical sensor.

[0015] In addition to one or more features described herein, the method includes activating the diffraction grating based on at least one of the following: user input and detection of environmental conditions.

[0016] In addition to one or more features described herein, the diffraction grating includes a body having a transparent central region, and the body is configured such that the central region corresponds to the basic FOV of the optical sensor.

[0017] In addition to one or more features described herein, a diffraction grating is a polarization grating having a first portion and a second portion, the first portion being configured to diffract received light in a first direction and the second portion being configured to diffract received light in a second direction opposite to the first direction.

[0018] In addition to one or more features described in this paper, visual artifacts are chromatic aberrations in a received image.

[0019] In yet another exemplary embodiment, a vehicle system includes a memory having computer-readable instructions and a processing means for executing the computer-readable instructions, which control the processing means to perform a method. The method includes monitoring the environment around the vehicle and collecting images generated by an optical sensing device, the optical sensing device including an optical sensor having a basic field of view (FOV) and optical elements disposed proximate to the optical sensor, the optical elements being configured to receive light from a region outside the basic FOV and guide the received light to the optical sensor. The method also includes generating the image by the optical sensing device, analyzing the image to detect visual artifacts in the image caused by the received light, and detecting objects in the region based on the visual artifacts.

[0020] In addition to one or more features described herein, the optical elements are configured to allow light within the basic FOV to illuminate the optical sensor without being affected by the optical elements.

[0021] In addition to one or more features described herein, optical elements include diffraction gratings.

[0022] In addition to one or more features described herein, the diffraction grating includes a body having a transparent central region, and the body is configured such that the central region corresponds to the basic FOV of the optical sensor.

[0023] The above-described features and advantages, as well as other features and advantages, of this disclosure will become apparent when taken in conjunction with the accompanying drawings and the following detailed description. Attached Figure Description

[0024] Other features, advantages, and details appear by way of example only in the following detailed description, which is described in detail with reference to the accompanying drawings, wherein:

[0025] Figure 1 This is a top view of a motor vehicle including various aspects of a monitoring system according to an exemplary embodiment;

[0026] Figure 2 An optical sensing device according to an exemplary embodiment is depicted;

[0027] Figure 3 Depicting according to exemplary embodiments Figure 2 The optical element of the optical sensing device includes a diffraction grating;

[0028] Figure 4 An optical sensing device including a camera and a polarization diffraction grating according to an exemplary embodiment is depicted;

[0029] Figure 5 A machine learning module according to an exemplary embodiment is schematically depicted;

[0030] Figure 6It is a flowchart depicting various aspects of a method for monitoring the environment and detecting objects according to an exemplary embodiment;

[0031] Figure 7 The illustration depicts various aspects of an optical sensing device according to exemplary embodiments and examples of detecting objects outside the field of view of a camera; and

[0032] Figure 8 A computer system according to an exemplary embodiment is described. Detailed Implementation

[0033] The following description is exemplary in nature only and is not intended to limit this disclosure, its application, or use. It should be understood that throughout the drawings, corresponding reference numerals denote the same or corresponding parts and features.

[0034] According to one or more exemplary embodiments, methods and systems are provided for monitoring the environment and detecting traffic signals and / or other bright objects. Embodiments of the monitoring system include optical sensors, such as cameras, having a field of view (FOV). The monitoring system also includes optical elements disposed near the camera lens, configured to diffract or otherwise guide incident light outside the FOV to the camera lens. In embodiments, the optical element is a diffraction grating.

[0035] Embodiments of the method include detecting anomalies or artifacts in images generated by a camera. Artifacts can be chromatic aberrations (e.g., colored spots or smudges), ghosting, distortion in the image, or any other effect on the image due to light diffracted, refracted, or otherwise directed by optical elements. Detected artifacts can be related to or associated with objects outside the field of view. For example, chromatic aberration in an image can be associated with the presence of traffic lights. The approximate location of a traffic light can be inferred based on the location of the chromatic aberration in the image.

[0036] The embodiments described herein present numerous advantages. These embodiments enhance awareness by extending the field of view of the optical sensor. For example, the optical sensing devices described herein include optical sensors (e.g., cameras) and optical elements, such as diffraction gratings, which allow the optical sensing device to capture images at high resolution within the field of view (FOV) of the optical elements while also detecting objects outside the FOV.

[0037] The embodiments address the inherent trade-offs in choosing a wide or narrow FOV by providing the ability to detect distant objects at high resolution and nearby objects outside the camera's FOV with sufficient accuracy. Objects can be detected outside the FOV while maintaining the desired high resolution of the image within the FOV.

[0038] The choice of FOV is influenced by several criteria. For example, it is generally desirable to have the widest possible FOV to allow for the detection of many objects. However, an FOV that is too wide will reduce resolution. The embodiments allow for the selection of a camera's FOV that is narrow enough to provide the desired resolution while also allowing for the detection of objects not directly captured by the camera.

[0039] Figure 1 An embodiment of a motor vehicle 10 is shown, which includes a body 12 that at least partially defines a passenger compartment 14. The body 12 also supports various vehicle subsystems, including a propulsion system 16 and other subsystems to support the functions of the propulsion system 16 and other vehicle components, such as a braking subsystem, a suspension system, a steering subsystem, and, if the vehicle is a hybrid electric vehicle, a fuel injection subsystem, an emission subsystem, etc.

[0040] Vehicle 10 may be an internal combustion engine vehicle, an electric vehicle (EV), or a hybrid vehicle. In an embodiment, vehicle 10 is a hybrid vehicle that includes an internal combustion engine system 18 and at least one electric motor assembly. In an embodiment, propulsion system 16 includes an electric motor 20 and may include one or more additional motors located at various positions.

[0041] The vehicle 10 also includes various control devices for controlling various aspects of vehicle operation. Such devices include, for example, an engine 18 and a motor 20, a steering wheel 22, an accelerator pedal 24, a front brake 26, and a rear brake 28. The control devices and actuators can be controlled via one or more control units, which are generally represented by a controller 30.

[0042] Vehicle 10 also includes an environmental monitoring system for detecting and monitoring the environment surrounding the vehicle. The monitoring system includes one or more optical sensing devices 40, configured to capture images, which may be still images and / or video images. Additional devices or sensors may be included, such as one or more radar components 42 included in vehicle 10. The monitoring system is not limited to this and may include other types of sensors, such as lidar and infrared devices. The monitoring system may also include a monitoring unit 44 for performing various functions, such as controlling the operation of one or more sensors (e.g., by controlling parameters of optical elements as described herein), receiving image data, processing image data, detecting objects or features, etc.

[0043] Each optical sensing device 40 includes an optical sensor 46, such as a camera 46. One or more of the optical sensing devices 40 include an optical element 48 positioned close to the optical camera 46, such that the optical element 48 receives light from an area outside the basic field of view (FOV) of the camera 46. The received light is diffracted or otherwise directed to illuminate the lens of the optical camera. As a result, visual indications of objects outside the basic FOV are projected onto the resulting image.

[0044] In this embodiment, the diffracted or guided light does not produce a clear image of objects outside the field of view (FOV), but instead causes ghosting, blurring, or other visual artifacts. The monitoring unit 44 (or other processor) can detect and / or identify objects by analyzing these artifacts.

[0045] As used herein, “artifacts” refer to any distortion or diffraction of light on an image. Artifacts can be chromatic aberrations, brightness anomalies (e.g., areas of increased brightness), resolution anomalies (e.g., blurred areas), or any other visual effect on an image. For example, chromatic aberrations such as yellow, green, or red trails or spots in an image can be detected and correlated with traffic lights, and the location of the trails in the image can be used to infer the direction and / or location of the traffic lights.

[0046] Any number of optical sensing devices 40 may include optical elements as described herein. For example, such as Figure 1 As shown, the camera 46 at the front of the vehicle 10 is equipped with optical elements for detecting objects outside the camera's field of view (FOV). The embodiments are not limited to this, as all optical sensing devices 40 may include optical elements 48 or any subset thereof. For example, the optical sensing device 40 on one side of the vehicle 10 may be equipped with optical elements 48.

[0047] The vehicle 10, monitoring system, controller 30, monitoring unit 44, and / or other vehicle systems include or are connected to an onboard computer system 50, which includes one or more processing units 52 and a user interface 54. The user interface 54 may include a touchscreen, a voice recognition system, and / or various buttons for allowing users to interact with features of the vehicle.

[0048] Figure 2 An embodiment of the optical sensing device 40 is depicted. The camera 46 includes a lens 60 and an image sensor 62, and has a field of view 64 (referred to as normal or basic field of view 64).

[0049] The basic FOV 64 has a longitudinal axis L and is defined based on the parameters of the image sensor 62 and the lens 60. For example, FOV 64 is expressed by the following formula:

[0050] FOV = 2 * atan(h / 2f),

[0051] Where h is the size of the image sensor 62 (e.g., the area size of the imaging pixels), and f is the focal length of the lens 60. This equation applies to a field of view (FOV) of less than 180 degrees, where the lens is a non-fisheye lens.

[0052] Optical element 48 may be a diffraction grating 48. Although the embodiment is described in conjunction with a diffraction grating, the embodiment is not limited thereto. Other examples of optical elements include lenses with different shapes.

[0053] For example, Figure 2 The embodiment includes a diffraction grating 48, which is oriented orthogonal to the longitudinal axis L. The diffraction grating 48 is circular and has a center point aligned with the longitudinal axis L. Furthermore, the diffraction grating 48 includes a transparent central region 66 (e.g., Figure 3 As shown), it has a size or diameter that is selected to ensure that the basic FOV 64 is unobstructed.

[0054] In use, camera 46 generates a high-resolution image of the region or area within the basic FOV 64. Light from outside the basic FOV 64 passes through diffraction grating 48 and diffracts toward lens 60. As a result, the image includes a high-resolution image of the region and includes one or more artifacts produced by diffraction. In this way, objects outside the basic FOV 64 but within the extended region 68 are captured. The basic FOV 64 and the extended region 68 constitute the extended effective FOV of optical sensing device 40.

[0055] In this embodiment, the optical element 48 is configured to be turned on and off, so that the optical sensing device 40 can be activated as needed. For example, the diffraction grating 48 is a liquid crystal grating, which can be connected to a control device (e.g., Figure 1 The monitoring unit 44 is used to selectively activate the detection function described herein.

[0056] Figure 3 An embodiment of a diffraction grating 48 in the form of a ring or annular diffraction grating is shown. The grating is formed on a ring or annular body 70. In this embodiment, the central region 66 is a circular opening (or a region made of transparent material) with a size corresponding to the size of the lens 60, such that the basic field of view (FOV) of the lens 60 is unaffected by the diffraction grating 48. The central region 66 is centered on the longitudinal axis L and is located along the axis L at a position that allows all light rays from the basic FOV 64 to pass through without attenuation.

[0057] In this embodiment, the diffraction grating 48 is a polarization diffraction grating having one or more grating orders. For example, the diffraction grating 48 includes a first grating segment 72 on one side of the longitudinal axis L and a second grating segment 74 on the other side. The first grating segment 72 is of order +1, and the second grating segment 74 is of order -1. The grating segments can have any grating order, as long as the light is properly guided to the lens 60 (e.g., light passing through the first grating segment 72 is diffracted to the right, and light passing through the second grating segment 74 is diffracted to the left). The size, shape, and grating order of each segment 72, 74 can be selected as needed.

[0058] The diffraction grating 48 may include segments with other grating orders. For example, additional regions with other grating orders (e.g., +2 and -2) may be added around the first and second grating segments to further extend the effective FOV of the optical sensing device 40. Furthermore, the diffraction grating or optical element 48 is not limited to any particular size or shape and can have any size or shape such that light from the extended region 68 (or a portion thereof) is directed to the camera 46.

[0059] Figure 4 This is a cross-section of an example of an optical sensing device 40, a polarization diffraction grating 48, and components for defining the diffraction direction. In this example, components are provided to give the diffraction grating 48 a preferred diffraction order of -1 or +1. The polarization diffraction grating 48 includes a linear polarizer 76, a fixed waveplate 78, and a polarization grating 80. The linear polarizer 76 controls the polarization of the linearly incident beam i (outside the basic FOV 64) and is followed by the fixed waveplate 78, which converts the polarization from linear to circular. The fixed waveplate 78 may be a quarter-wave plate, which introduces a phase delay between the components of the linearly polarized incident beam i. The polarization grating 48 diffracts circularly polarized light only in the -1 or +1 direction, producing a refracted ray r.

[0060] The grating 48 is designed such that the angle θ between the incident beam i and the longitudinal axis L is... i The maximum edge ray angle is greater than the basic FOV 64. Select the line spacing d of the diffraction grating 48 (e.g., Figure 3 As shown), the angle θ of the refracted ray r. r Within the cone tracked by the imaging lens 60 to the imaging sensor 62. The line spacing can be expressed by the following formula:

[0061] d = mλ,

[0062] Where m is the order (e.g., +1 or -1), and λ is the wavelength of the light. The grating spacing is chosen such that θ r The angle of light received by lens 60 and imager 62 is less than the maximum angle of light received by lens 60 and imager 62.

[0063] Object detection or recognition in extended region 68 can be performed in any suitable manner. For example, lookup tables or other data structures can be used to associate different types of artifacts with objects. Other examples include machine learning techniques that utilize machine learning models trained on previously collected artifacts.

[0064] Figure 5An embodiment of module 81, which can be used for machine learning and object recognition based on image artifacts, is described. Machine learning module 81 utilizes neural networks or other machine learning models, which are trained, for example, on previously collected images. The images may be images collected using optical sensing device 40, and / or other images having artifacts corresponding to known objects.

[0065] The machine learning module 81 includes a machine learning model 82. In an embodiment, the machine learning model 82 includes at least one feature space or embedding space 84.

[0066] Machine learning model 82 receives input data including an image 86 (or a portion of an image) generated by optical sensing device 40, and may include other information. Examples of other information include vehicle location data 88 (e.g., location data such as Global Positioning System (GPS) and / or Global Navigation Satellite System (GNSS) data, and map data), and operational data 90 representing operational parameters such as speed, acceleration, and braking. Other information (such as the location of intersections and other areas where extended FOV would be useful) may also be input to model 82.

[0067] Machine learning module 81 is configured to convert received data into feature vectors 92, which are then input into model 82. Model 82 outputs object detection information 94. For example, model 82 classifies artifacts in an image as objects (such as traffic lights or other objects that generate light, bright objects such as reflective signs, etc.) and outputs object identification information 94. Information 94 may include other information, such as the estimated location of the object.

[0068] Figure 6 An embodiment of a method 100 for monitoring the environment and detecting objects is shown. Aspects of method 100 may be executed by a monitoring unit 44, a machine learning module 81, and / or other suitable processing devices or combinations thereof.

[0069] For illustrative purposes, combined with Figure 1 Vehicle 10 and Figure 2 The method 100 is described using an optical sensing device 40. Embodiments are not limited thereto, as the method 100 can be performed in conjunction with any suitable system.

[0070] Method 100 includes multiple steps or stages represented by boxes 101-106. Method 100 is not limited to the number or order of the steps, because some steps represented by boxes 101-106 may be performed in a different order than that described below, or fewer than all steps may be performed.

[0071] At frame 101, at least one optical imaging device 40 is used to monitor the environment around vehicle 10. During monitoring, images are collected continuously or periodically.

[0072] In one embodiment, the optical imaging device 40 includes a controllable diffraction grating that can be turned on and off. For example, the diffraction grating 48 is or includes a liquid crystal grating. Therefore, in this embodiment, the optical imaging device 40 can initially capture an image when the grating is off, thereby producing a standard high-resolution image.

[0073] At block 102, in this embodiment, the optical imaging device 40 is activated by opening the diffraction grating 48. Activation can occur in response to various conditions and inputs. For example, the imaging device 40 can be manually activated by a user or driver. In another example, the imaging device 40 can be activated by a processing device (e.g., Figure 1 The monitoring unit 44) is activated based on the vehicle's position and / or environmental characteristics. For example, the optical imaging device 40 is automatically activated based on the determination that the vehicle 10 is approaching an intersection or that it would be useful or desirable to detect objects outside the basic FOV 64.

[0074] Figure 7 An example of an optical sensing device 40 is depicted, which is attached to a vehicle (not shown) located at or near an intersection. As shown, there is a traffic light 110 located outside the basic FOV 64, which will not be captured by the camera 46 alone.

[0075] Return to Figure 6 At frame 103, an image is collected when the diffraction grating is active, and light from inside the basic FOV 64 propagates through lens 60 and illuminates image sensor 62. Light from outside the basic FOV 64 (e.g., light generated by or reflected from objects in extended region 68) diffracts toward the lens and also illuminates image sensor.

[0076] At box 104, the image is analyzed to identify artifacts. Figure 7 In the example, the light emitted by traffic light 110 produces a red trail or smudge in a portion of the image.

[0077] At box 105, the identified artifacts are correlated with the object. This correlation can be performed, for example, using stored data or by classifying the artifacts using a machine learning model. Figure 7 In the example, the image (or a portion of the image including the red trail) is identified as a red traffic signal. In addition to detecting objects, the orientation or location of objects can also be determined based on the position of artifacts in the image.

[0078] At box 106, one or more actions can be performed based on object detection information. For example, where applicable, the operation of vehicle 10 (e.g., engine rotation speed, vehicle speed, braking, etc.) can be controlled manually or autonomously (e.g., by vehicle controller 30) to react to an object. Other actions include presenting instructions or notifications to the user. Figure 7 In the example, the action could be informing the driver that a red light is present, and / or notifying them when the traffic light 110 changes.

[0079] Figure 8 Examples of embodiments of a computer system 140 are shown, which can perform various aspects of the embodiments described herein. The computer system 140 includes at least one processing means 142, which generally includes one or more processors for performing various aspects of the image acquisition and analysis methods described herein.

[0080] The components of computer system 140 include processing device 142 (such as one or more processors or processing units), memory 144, and bus 146, which couples various system components, including system memory 144, to processing device 142. System memory 144 may be a non-transitory computer-readable medium and may include various computer system-readable media. Such media may be any available media accessible by processing device 142, and includes volatile and non-volatile media as well as removable and non-removable media.

[0081] For example, system memory 144 includes non-volatile memory 148, such as a hard disk drive, and may also include volatile memory 150, such as random access memory (RAM) and / or cache memory. Computer system 140 may also include other removable / non-removable, volatile / non-volatile computer system storage media.

[0082] System memory 144 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments described herein. For example, system memory 144 stores various program modules that generally perform the functions and / or methods of the embodiments described herein. Module 152 may be included to perform functions such as collecting images and data, and module 154 may be included to perform functions such as image analysis and object detection discussed herein. System 140 is not limited thereto, as it may include other modules. As used herein, the term "module" refers to processing circuitry that may include application-specific integrated circuits (ASICs), electronic circuitry, processor (shared, dedicated, or grouped) and memory executing one or more software or firmware programs, combinational logic circuitry, and / or other suitable components that provide the described functions.

[0083] The processing device 142 can also communicate with one or more external devices 156, which may be a keyboard, a pointing device, and / or any device that enables the processing device 142 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Communication with various devices may occur via input / output (I / O) interfaces 164 and 165.

[0084] Processing device 142 can also communicate via network adapter 168 with one or more networks 166, such as a local area network (LAN), a general wide area network (WAN), a bus network, and / or a public network (e.g., the Internet). It should be understood that, although not shown, other hardware and / or software components may be used in conjunction with computer system 140. Examples include, but are not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, and data archiving storage systems.

[0085] The terms “a” and “an” do not indicate a limitation of quantity, but rather that at least one of the referenced items is present. The term “or” means “and / or” unless the context clearly indicates otherwise. A reference to “an aspect” throughout the specification means that a particular element (e.g., feature, structure, step, or characteristic) described in connection with that aspect is included in at least one aspect described herein and may or may not be present in other aspects. Furthermore, it should be understood that the described elements may be combined in any suitable manner across the aspects.

[0086] When an element, such as a layer, film, region, or substrate, is referred to as being "on" another element, it can be directly on the other element, or there may be intermediate elements present. Conversely, when an element is referred to as being "directly on" another element, there are no intermediate elements present.

[0087] Unless otherwise stated herein, all test standards are the most recent valid standards up to the filing date of this application, or, if priority is claimed, the filing date of the earliest priority application in which the test standard appears.

[0088] Unless otherwise defined, the technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0089] While the foregoing disclosure has been described with reference to exemplary embodiments, those skilled in the art will understand that various changes can be made and equivalents can replace its elements without departing from its scope. Furthermore, many modifications can be made to adapt particular situations or materials to the teachings of this disclosure without departing from its essential scope. Therefore, it is intended that this disclosure be limited to the specific embodiments disclosed, but will include all embodiments falling within its scope.

Claims

1. A system for detecting objects in the environment surrounding a vehicle, comprising: Optical sensors with a basic field of view (FOV); An optical element, positioned close to an optical sensor, is configured to receive light from an area outside the basic field of view (FOV) and direct the received light to the optical sensor. as well as A processor configured to receive images from an optical sensor and detect objects in the region based on visual artifacts produced by the received light in the received images.

2. The system according to claim 1, wherein, The optical element is configured to allow light within the basic FOV to illuminate the optical sensor without being affected by the optical element.

3. The system according to claim 1, wherein, The optical element includes a diffraction grating.

4. The system according to claim 3, wherein, The diffraction grating is configured to be turned on to guide the received light, and configured to be turned off so that light from the region does not illuminate the optical sensor.

5. The system according to claim 3, wherein, The diffraction grating includes a body having a transparent central region, and the body is configured such that the transparent central region corresponds to the basic FOV of the optical sensor.

6. The system according to claim 4, wherein, The diffraction grating is a polarization grating, which has a first part and a second part. The first part is configured to diffract the received light in a first direction, and the second part is configured to diffract the received light in a second direction opposite to the first direction.

7. The system according to claim 1, wherein, The visual artifact is the color difference in the received image.

8. The system according to claim 1, wherein, The object in question is a traffic light.

9. A method for detecting objects in the environment surrounding a vehicle, comprising: The system monitors the environment and collects images generated by an optical sensing device, which includes an optical sensor with a basic field of view (FOV) and an optical element positioned close to the optical sensor. The optical element is configured to receive light from an area outside the basic FOV and guide the received light to the optical sensor. Images are generated by optical sensing devices; Analyze images to detect visual artifacts produced in the image by received light; as well as Objects in the region are detected based on visual artifacts.

10. The method of claim 9, further comprising activating the diffraction grating based on at least one of: user input and detection of environmental conditions.

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