Collaborative Lighting

Abstract

Examples described herein provide a computer-implemented method that includes receiving, by a processing device of a vehicle, first road data. The method also includes receiving, by the processing device of the vehicle, second road data from a sensor associated with the vehicle. The method also includes identifying, by the processing device of the vehicle, an object based at least in part on the first road data and the second road data. The method also includes causing, by the processing device of the vehicle, the object to be illuminated.

Description

Technical Field

[0001] The present disclosure relates to vehicles and, more particularly, to coordinated lighting. Background Art

[0002] Modern vehicles (e.g., cars, motorcycles, boats, or any other type of automobile) can be equipped with vehicle communication systems that facilitate different types of communications between the vehicle and other entities. For example, vehicle communication systems can provide vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), vehicle-to-pedestrian (V2P), and / or vehicle-to-grid (V2G) communications. Collectively, these can be referred to as vehicle-to-everything (V2X) communications, which enable the communication of information from a vehicle to any other suitable entity. Various applications (e.g., V2X applications) can use V2X communications to send and / or receive safety messages, maintenance messages, vehicle status messages, and the like.

[0003] Modern vehicles may also include one or more cameras that provide backup assistance, capture images of the vehicle's driver to determine driver drowsiness or inattention, provide images of the road as the vehicle is traveling to avoid collisions, provide structure recognition, such as road signs, etc. For example, a vehicle may be equipped with multiple cameras, and images from multiple cameras (referred to as "surround view cameras") may be used to create a "surround" or "bird's eye" view of the vehicle. Some cameras (referred to as "long-range cameras") may be used to capture long-range images (e.g., object detection for collision avoidance, structure recognition, etc.).

[0004] Such vehicles may also be equipped with sensors, such as radar devices, lidar devices, and / or similar devices for performing target tracking. Target tracking involves identifying a target object and tracking the target object over time as the target object moves relative to the vehicle observing the target object. Images from one or more cameras of the vehicle may also be used to perform target tracking. Summary of the Invention

[0005] In an exemplary embodiment, a computer-implemented method for coordinated lighting is provided. The method includes receiving, by a processing device of a vehicle, first road data. The method also includes receiving, by the processing device of the vehicle, second road data from a sensor associated with the vehicle. The method also includes identifying, by the processing device of the vehicle, an object based at least in part on the first road data and the second road data. The method also includes causing, by the processing device of the vehicle, illumination of the object.

[0006] Additionally or alternatively to one or more features described herein, further embodiments of the method may include selecting the object from the group consisting of a vulnerable road user and a road hazard.

[0007] Additionally or alternatively to one or more features described herein, further embodiments of the method may include receiving first road data from a device selected from the group consisting of an edge computing node and a cloud computing node of a cloud computing environment.

[0008] Additionally or alternatively to one or more features described herein, further embodiments of the method may include the vehicle being a first vehicle, and wherein the first road data is received from a second vehicle.

[0009] Additionally or alternatively to one or more features described herein, further embodiments of the method may include receiving the first road data from the second vehicle via a direct vehicle-to-vehicle communication protocol between the first vehicle and the second vehicle.

[0010] Additionally or alternatively to one or more features described herein, further embodiments of the method may include the first road data including local data and global data.

[0011] In addition or alternatively to one or more features described herein, further embodiments of the method may include selecting a sensor from the group consisting of a camera, a lidar device, and a radar device.

[0012] Additionally or alternatively to one or more features described herein, further embodiments of the method may include identifying the object including fusing, by a processing device of the vehicle, the first road data and the second road data.

[0013] Additionally or alternatively to one or more features described herein, further embodiments of the method may include causing the object to be illuminated including illuminating the object using a light assembly of the vehicle.

[0014] Additionally or alternatively to one or more features described herein, further embodiments of the method may include that the lighting includes increasing the brightness of light emitted by a lamp assembly of the vehicle.

[0015] Additionally or alternatively to one or more features described herein, further embodiments of the method may include illumination including reducing the brightness of light emitted by a lamp assembly of the vehicle.

[0016] Additionally or alternatively to one or more features described herein, further embodiments of the method may include causing the object to be illuminated including illuminating the object using a light assembly of another vehicle.

[0017] Additionally or alternatively to one or more features described herein, further embodiments of the method may include the vehicle being a first vehicle, and causing the object to be illuminated includes illuminating the object using a light assembly of a second vehicle.

[0018] Additionally or alternatively to one or more of the features described herein, further embodiments of the method may include the second vehicle being a parked vehicle.

[0019] Additionally or alternatively to one or more features described herein, further embodiments of the method may include identifying the object further being based at least in part on a gaze of the vehicle operator.

[0020] Additionally or alternatively to one or more features described herein, further embodiments of the method may include identifying the object comprising generating an object map; generating a bounding box around the object on the object map; and determining a centroid of the bounding box.

[0021] In addition or alternatively to one or more features described herein, further embodiments of the method may include the object being a first object, the bounding box being a first bounding box, and the centroid being a first centroid, the method further comprising identifying a second object. The method further comprises generating a second bounding box around the second object on the object map. The method further comprises determining a second centroid of the second bounding box. The method further comprises generating a group bounding box around the first object and the second object on the object map. The method further comprises determining a group centroid of the group bounding box.

[0022] Additionally or alternatively to one or more features described herein, further embodiments of the method may include offsetting the group centroid based at least in part on the type of the first object and the type of the second object.

[0023] In another exemplary embodiment, a system is provided. The system includes a memory having computer-readable instructions. The system also includes a processing device for executing the computer-readable instructions, the computer-readable instructions controlling the processing device to perform operations for coordinated lighting. The operations include receiving, by a processing device of a vehicle, first road data. The method also includes receiving, by the processing device of the vehicle, second road data from a sensor associated with the vehicle. The method also includes identifying, by the processing device of the vehicle, an object based at least in part on the first road data and the second road data. The method also includes causing, by the processing device of the vehicle, at least one of the objects to be illuminated.

[0024] In yet another exemplary embodiment, a computer program product is provided. The computer program product includes a computer-readable storage medium having program instructions, wherein the computer-readable storage medium itself is not a transient signal, and the program instructions are executable by a processing device to cause the processing device to perform operations for coordinated illumination. The operations include receiving, by a processing device of a vehicle, first road data. The processing device of the vehicle also includes receiving, by the processing device of the vehicle, second road data from a sensor associated with the vehicle. The method also includes identifying, by the processing device of the vehicle, an object based at least in part on the first road data and the second road data. The method also includes causing, by the processing device of the vehicle, at least one of the objects to be illuminated.

[0025] The above features and advantages and other features and advantages of the present disclosure will become apparent from the following detailed description when taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Other features, advantages, and details appear, by way of example only, from the following detailed description, which refers to the accompanying drawings, in which:

[0027] Figure 1 depicts a vehicle including a sensor and processing system for coordinated lighting according to one or more embodiments described herein;

[0028] Figure 2 depicts an environment supporting collaborative lighting according to one or more embodiments described herein;

[0029] Figure 3 depicts a system for collaborative lighting according to one or more embodiments described herein;

[0030] Figure 4 depicts a flow chart of a method for collaborative lighting according to one or more embodiments described herein;

[0031] Figure 5 depicts an illustration of a vehicle illuminating road hazards and VRUs according to one or more embodiments described herein;

[0032] Figure 6 A flowchart depicting a method for coordinated lighting according to one or more embodiments described herein; and

[0033] Figure 7 Depicted is a block diagram of a processing system for implementing the techniques described herein, according to an illustrative embodiment. DETAILED DESCRIPTION

[0034] The following description is merely exemplary in nature and is not intended to limit the present disclosure, its application or use. It should be understood that throughout the drawings, corresponding reference numerals indicate identical or corresponding parts and features. As used herein, the term module refers to a processing circuit, which may include an application-specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that executes one or more software or firmware programs, combinational logic circuits, and / or other suitable components that provide the functionality described.

[0035] The technical solution described herein provides collaborative lighting. As a vehicle traverses a road, road hazards (e.g., potholes, oil on the road, obstacles on or near the road, fog, flooding, etc.) can be harmful to the vehicle. Vulnerable road users (VRUs), such as pedestrians, can be harmed by the vehicle, for example, if the vehicle strikes the VRU. In both cases, improved lighting of the road hazard and / or the VRU may be required. To this end, the present technology uses data from the vehicle and data from other sources (e.g., other vehicles, Internet of Things (IoT) devices, etc.) to collaboratively illuminate an area, road hazard, VRU, etc., to illuminate the scene.

[0036] Conventional adaptive front lighting systems for vehicles only consider the host vehicle's trajectory and the forward geometry from sensing or mapping to generate lighting patterns. One or more embodiments described herein utilize connected vehicle telemetry and perception data from multiple vehicles and / or other devices to collaboratively illuminate a scene. For example, one or more embodiments utilize collaboration between multiple road-aware entities (e.g., vehicles, IoT devices, etc.) to illuminate VRUs and / or road hazards.

[0037] Figure 1 A vehicle 100 is depicted including a sensor and processing system 110 for coordinated lighting according to one or more embodiments described herein. Figure 1 In the example of FIG, vehicle 100 includes a processing system 110, cameras 120, 121, 122, 123, cameras 130, 131, 132, 133, a radar sensor 140, and a lidar sensor 141. Vehicle 100 can be a car, truck, van, bus, motorcycle, boat, airplane, or other suitable vehicle 100.

[0038] Cameras 120-123 are panoramic cameras that capture images of the exterior and vicinity of vehicle 100. The images captured by cameras 120-123 together form a surround view (sometimes referred to as a "top view" or "bird's eye view") of vehicle 100. These images are useful for operating the vehicle (e.g., parking, reversing, etc.). Cameras 130-133 are long-range cameras that capture images of the exterior of the vehicle and are further away from vehicle 100 than cameras 120-123. For example, these images can be used for object detection and avoidance. It should be understood that although eight cameras 120-123 and 130-133 are shown, more or fewer cameras may be implemented in various embodiments.

[0039] The captured images can be displayed on a display (not shown) to provide the driver / operator of vehicle 100 with a view of the exterior of vehicle 100. The captured images can be displayed as moving images, still images, or some combination thereof. In some examples, the images can be combined to form a composite view, such as a surround view.

[0040] The radar sensor 140 measures the distance to a target object by emitting electromagnetic waves and measuring the reflected waves with the sensor. This information is useful for determining the distance / position of the target object relative to the vehicle 100.

[0041] The LiDAR (Light Detection and Ranging) sensor 141 measures the distance to a target object by illuminating the target with pulsed laser light and measuring the reflected pulses with the sensor. This information is useful for determining the distance / position of the target object relative to the vehicle 100.

[0042] The vehicle 100 may also include light assemblies 142, 143 that emit light. For example, one or more light assemblies 142, 143 may include one or more parking lights, headlights, etc. The type and number of light assemblies may vary and are not limited to Figure 1 Quantities and locations shown.

[0043] The data generated from the cameras 120-123, 130-133, the radar sensor 140, and / or the lidar sensor 141 may be used to track target objects relative to the vehicle 100. Examples of target objects include other vehicles, vulnerable road users (VRUs) such as pedestrians, bicycles, animals, potholes, oil on the road surface, debris on the road surface, fog, flooding, etc.

[0044] The processing system 110 includes a data / communication engine 112, a recognition engine 114, and a lighting engine 116. Although not shown, the processing system 110 may include other components, engines, modules, etc., such as a processor (e.g., a central processing unit, a graphics processing unit, a microprocessor, etc.), a memory (e.g., a random access memory, a read-only memory, etc.), a data storage (e.g., a solid-state drive, a hard disk drive, etc.), etc.

[0045] The processing system 110 may be communicatively coupled to a remote processing system 150, which may be an edge processing node as part of an edge processing environment (see, e.g., Figure 3 ), cloud processing nodes as part of a cloud processing environment (see e.g. Figure 3 ) etc. For example, the processing system 110 may include a network adapter (not shown) (see, for example Figure 7 The network adapter 726 is a network adapter that enables processing system 110 to send data to and / or receive data from other sources, such as other processing systems, data repositories, etc., including remote processing system 150. For example, processing system 110 can send data to and / or receive data from remote processing system 150 directly and / or via network 152.

[0046] The network 152 represents any one or combination of different types of suitable communication networks, such as a wired network, a public network (e.g., the Internet), a private network, a wireless network, a cellular network, or any other suitable private and / or public network. In addition, the network 152 can have any suitable communication range associated with it and can include, for example, a global network (e.g., the Internet), a metropolitan area network (MAN), a wide area network (WAN), a local area network (LAN), or a personal area network (PAN). In addition, the network 152 can include any type of medium that can carry network traffic, including but not limited to coaxial cable, twisted pair, optical fiber, hybrid fiber coaxial (HFC) medium, microwave terrestrial transceiver, radio frequency communication medium, satellite communication medium, or any combination thereof. According to one or more embodiments described herein, the remote processing system 150 and the processing system 110 communicate via vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), vehicle-to-pedestrian (V2P), and / or vehicle-to-grid (V2G) communications.

[0047] The features and functions of the components of the processing system 110 are further described herein. The processing system 110 of the vehicle 100 facilitates coordinated lighting. The process is further described with reference to the remaining figures.

[0048] Specifically, Figure 2 An environment 200 supporting collaborative lighting according to one or more embodiments described herein is depicted. In this example, environment 200 includes vehicles 100, 210, and 212, configured and arranged as shown, with vehicles 100 and 210 moving along arrows 201 and 211, respectively, and vehicle 212 stationary (e.g., parked). Environment 200 also includes two objects: a road hazard 220 (e.g., a pothole) and a VRU 222 (e.g., a pedestrian). As can be seen, road hazard 220 is in the path of vehicle 100. Similarly, the pedestrian's anticipated path may cross the path of vehicle 100. In either case, it is desirable to illuminate road hazard 220 and VRU 222. To this end, the present technology identifies road hazard 220 and VRU 222 and illuminates them using light assemblies of vehicle 100, vehicle 210, and / or vehicle 212. For example, processing system 110 of vehicle 100 receives global data in the form of a cloud-based dynamic map 230 displaying road hazard 220. The processing system 110 also receives local data (e.g., via V2I communication, V2V communication, edge communication, etc.) from two sources: data 232 generated by a camera associated with a lamppost 233 and data 234 generated by sensors associated with vehicles 210 and 212. The data 232 and 234 may include local data, which may be in the form of a local awareness map showing the location of the VRU 222. It should be understood that, for example, a camera associated with the vehicle 210 and 212 and / or the lamppost 233 may have a field of view 223.

[0049] Using data 230, 232, 234, road hazard 220 and / or VRU 222 can be illuminated, such as by vehicle 100, vehicle 210, vehicle 212, and / or lamppost 233. This allows road hazard 220 and / or VRU 222 to be easily observed by the driver of one of vehicles 100, 210. For example, vehicle 210 can highlight pothole 220 221. Similarly, vehicle 201 can highlight pedestrian 224.

[0050] As another example, when vehicle 100 and / or vehicle 210 approaches vehicle 212, vehicle 212 may be illuminated by turning on its parking lights. This makes it easier for the driver of one of vehicles 100, 210 to observe vehicle 212.

[0051] Figure 3 A system 300 for collaborative lighting according to one or more embodiments described herein is depicted. In this example, three computing / processing environments are depicted: the processing system 110 of the vehicle 100, a cloud computing environment 310, and an edge computing environment 320. The processing system 110 can communicate with one or both of the environments 310, 320.

[0052] Cloud computing environment 310 may include one or more cloud computing nodes, such as cloud computing node 312. Cloud computing may supplement, support, or replace some or all of the functionality of the elements of processing system 110. Figure 3 In the example of FIG. 1 , cloud computing node 312 may be a data store that stores global data. Global data is data that is not limited to the immediate vicinity of vehicle 100. In the example, global data may include road conditions, speed information, suspension data, etc. Cloud computing node 312 may be communicatively coupled to vehicle processing system 110, such as directly via cellular communication (e.g., 5G) and / or via edge computing environment 320.

[0053] Edge computing environment 320 may include one or more edge computing nodes, such as edge computing node 322. Compared to cloud computing environment 310, edge computing environment 320 provides computing and data storage resources closer to where they are needed (e.g., closer to vehicle 100). In some examples, cloud computing environment 310 may provide more data than edge computing environment 320, but with higher latency. In contrast, edge computing environment 320 provides lower latency but does not support as much data as cloud computing environment 320. In this example, edge computing node 322 may be a data store that stores local data. Local data refers to data about the area immediately adjacent to vehicle 100 (e.g., within 100 feet, within 200 yards, within 0.5 miles, etc.). Edge computing node 322 may be communicatively coupled to the vehicle's processing system 110, such as via cellular (e.g., 5G), V2I, V2V, V2P, and / or V2G communication.

[0054] Processing system 110 includes a telematics unit 302, sensors 304, a data fusion unit 306, and a light control unit 308. Telematics unit 302 facilitates communication between processing system 110 and cloud computing nodes 312 and / or edge computing nodes 322. Sensors 304 may include one or more cameras 120-123, 130-133, a driver monitor sensor 305, a radar sensor 140, a lidar sensor 141, and the like. Data fusion unit 306 takes information from cloud computing nodes 312 (i.e., global data), information from edge computing nodes 322 (i.e., local data), and information from sensors 304 and constructs a map (object map) indicating detected road hazards and / or VRUs. For example, data fusion unit 306 takes a conventional map and adds markers indicating the presence of detected road hazards and / or VRUs. Position data is used to appropriately place detected road hazards and / or VRUs on the map. The light control unit 308 can illuminate the detected road hazards and / or VRUs, such as by changing the brightness of the headlight beam, turning the headlights on / off, adjusting the direction of the light beam emitted by the headlights, turning on / off parking lights or other similar lights, etc., including combinations thereof.

[0055] Figure 4 Depicted is a flow diagram of a method 400 for coordinated lighting, according to one or more embodiments described herein.

[0056] A sensing cycle begins at block 402. The sensing cycle may be approximately every 100 milliseconds, although other durations / periods are possible and within the scope of the present technology.

[0057] At block 404, a determination is made as to whether the target vehicle (e.g., vehicle 100) is parked. This may be based on, for example, vehicle kinematic data received from the target vehicle. If the target vehicle is determined to be parked, a determination is made at block 406 as to whether there is sufficient battery for lighting. This determination may be made by comparing the available battery level to a threshold, such that if the threshold is met, it is determined that there is sufficient battery for lighting. If it is determined at block 406 that the battery is insufficient, the vehicle and / or its lighting system is shut down at block 408.

[0058] If the battery is determined to be sufficient at block 406, or if the target vehicle is determined to be unparked at block 404, a target map is acquired at block 410. The target map indicates detected road hazards and / or VRUs. For example, a conventional map may be supplemented by adding markers indicating the presence of detected road hazards and / or VRUs to create the target map. The location data is used to appropriately position the detected road hazards and / or VRUs on the map. The object map is received from the cloud computing environment 310 and / or the edge computing environment 320.

[0059] A determination is then made at block 412 as to whether there is an object of interest (e.g., a road hazard and / or a VRU) on the object map. This may include determining whether the object is along the intended path of the vehicle 100, in the field of view of the operator of the vehicle 100, etc. If no object of interest is detected at block 414, then at block 414 the perception loop sleeps for the remainder of the perception loop. If an object of interest is detected at block 412, the method 400 continues at block 416 by creating a bounding box around the target, determining a centroid from the bounding box based on the weight of the object and / or the driver's gaze at block 418, and generating a light pattern to highlight the centroid region at block 420. Figure 5 Blocks 416, 418, and 420 are described in more detail. Next, a determination is made at block 422 as to whether a collision risk exists. That is, a determination is made as to whether the vehicle 100 is likely to collide with a VRU and / or a road obstacle. This can be determined, for example, by comparing the kinematic data of the vehicle 100 with the location of the VRU and / or road hazard. If a collision risk does not exist (or a threshold is not met) at block 422, the perception loop can restart at block 402. However, a collision risk exists, and the method 400 continues to provide an alert at block 424. This can include providing an external notification via a human-machine interface (HMI), which can include sounding a horn, turning on or illuminating a light pattern, and the like. The method 400 then returns to block 402, and the perception loop restarts.

[0060] Figure 5 Illustration 500 of vehicle 100 illustrating road hazards 510 and VRU 520 according to one or more embodiments described herein. Figure 5 Involved Figure 4 4. Blocks 416, 418, and 420 of the embodiment include creating a bounding box around the target at block 416, determining a centroid from the bounding box based on the object's weight and / or the driver's gaze at block 418, and generating a light pattern to highlight the centroid region at block 420.

[0061] In this example, vehicle 100 is shown traveling along a path / track 502. Also shown is an object map 504, which demarcates an area surrounding and extending around vehicle 100. In this example, two objects 510 and 520 are detected. Road hazard 510 represents a road hazard in the form of another vehicle. Object 520 represents a VRU.

[0062] Bounding boxes 512 and 522 are created around objects 510 and 520, respectively. According to one or more embodiments described herein, the sizes of the bounding boxes 512 and 522 are set so that they are approximately the same size as the corresponding objects 510 and 520. A center point (or "center of mass") is generated for each bounding box 512 and 522. For example, bounding box 512 has center point 514 and bounding box 522 has center point 524. According to one or more embodiments described herein, center points 514 and 524 are unweighted so that they are located at the center of the corresponding bounding box 512 and 522.

[0063] A grouping bounding box 532 may be created based on the bounding boxes 512, 522. According to one or more embodiments described herein, the grouping bounding box 532 includes each of the bounding boxes 512, 522. A center point 534 (or "center of mass") of the grouping bounding box 532 may be determined. According to one or more embodiments described herein, the center point 534 is not at the center of the bounding box 532; rather, it is offset based on the importance of the objects 510, 520. In this example, as shown, the center point 534 is biased toward the VRU (i.e., object 520). According to one or more embodiments described herein, the weighting of object types may be based on the type of object (e.g., VRU vs. road hazard) and the risk associated with each object. For example, a pothole with a larger magnitude may have a higher weight than a pothole with a smaller magnitude. Similarly, a VRU may have a higher weight than a non-VRU, such as Figure 5 shown.

[0064] In this example, the headlights (not shown) of vehicle 100 can be configured to make the weighted average center (e.g., center point 534) of grouping bounding box 532 more prominent. In some examples, center point 534 can also be based on the projected gaze of the driver of vehicle 100.

[0065] The following equation can be used, based on the object position x i ,y i and object threat level w i , determine the coordinate-based position of the center point 534 for the target object lighting position X, Y, where X is the x-axis coordinate and Y is the x-axis coordinate of a Cartesian coordinate system originating from the vehicle 100 or another suitable origin:

[0066]

[0067] Figure 6 A flow chart of a method 600 for collaborative lighting according to one or more embodiments described herein is depicted. The method 600 may be performed by any suitable system or device, such as Figure 1 processing system 110, Figure 7The processing system 700 or any other suitable processing system and / or processing device (eg, processor) of FIG. Figure 1 The elements of method 600 are described, but are not limited thereto.

[0068] At block 602, the processing system 110 receives first road data. According to one or more embodiments described herein, from a cloud computing environment (see, e.g., Figure 3 According to one or more embodiments described herein, the cloud computing node of the edge computing node (see, for example, Figure 3 According to one or more embodiments described herein, the vehicle is a first vehicle and receives the first road data from a second vehicle, such as from a remote processing system 150, which may be associated with the second vehicle. In such an example, the first road data may be received from the second vehicle via a direct vehicle-to-vehicle communication protocol between the first vehicle and the second vehicle, as described herein. According to one or more embodiments described herein, the first road data includes local data and global data. Local data is data about an area in close proximity to the vehicle 100 (e.g., within 100 feet, within 200 yards, within 0.5 miles, etc.). Local data may be provided by, for example, an edge computing environment. Global data is data that is not limited to being in close proximity to the vehicle 100. In an example, global data may include road conditions, speed information, suspension data, and the like.

[0069] At block 604 , the processing system 110 receives second road data from sensors associated with the vehicle. The sensors may be one or more of the cameras 120 - 123 , 130 - 133 , the radar sensor 140 , the lidar sensor 141 , and / or any other suitable sensors.

[0070] At block 606, the processing system 110 identifies at least one of a vulnerable road user or a road hazard based at least in part on the first road data and the second road data. According to one or more embodiments described herein, identifying at least one of the vulnerable road user or the road hazard includes fusing the first road data and the second road data via a processing device of the vehicle (e.g., using the data fusion unit 306). According to one or more embodiments described herein, identifying at least one of the vulnerable road user or the road hazard is also based at least in part on a gaze of the vehicle operator.

[0071] According to one or more embodiments described herein (see e.g. Figure 5), identifying at least one of the vulnerable road users or road hazards includes generating an object map, generating (on the object map) a bounding box around the at least one of the vulnerable road users or road hazards, and determining a centroid of the bounding box. In such an example, the at least one of the vulnerable road users or road hazards is a first at least one of the vulnerable road users or road hazards, the bounding box is a first bounding box, and the centroid is the first centroid. In such an example, the method further includes identifying a second at least one of the vulnerable road users or road hazards; generating a second bounding box around the second at least one of the vulnerable road users or road hazards on the object map; determining a second centroid of the second bounding box; generating a group bounding box around the first at least one of the vulnerable road users or road hazards and the second at least one of the vulnerable road users or road hazards on the object map; and determining a group centroid of the group bounding box. In an example, the group centroid is offset based at least in part on a type of the first at least one of the vulnerable road users or road hazards and a type of the second at least one of the vulnerable road users or road hazards.

[0072] At block 608, processing system 110 causes illumination of at least one of a vulnerable road user or a road hazard. For example, causing illumination of at least one of a vulnerable road user or a road hazard includes illuminating at least one of the vulnerable road user or the road hazard using a light assembly of the vehicle. As another example, illumination includes increasing (and / or decreasing) the brightness of light emitted by a light assembly (e.g., headlights, parking lights, etc.) of vehicle 100. According to one or more embodiments described herein, causing illumination of at least one of the vulnerable road user or the road hazard includes illuminating at least one of the vulnerable road user or the road hazard using a light assembly of another vehicle (e.g., vehicle 210, vehicle 212). In examples, the other vehicle can be a parked or moving vehicle.

[0073] Additional processes may also be included, and it should be understood that Figure 6 The processes depicted in the drawings represent illustrations, and other processes may be added or existing processes may be removed, modified, or rearranged without departing from the scope and spirit of the present disclosure.

[0074] It should be understood that the present disclosure can be implemented in conjunction with any type of computing environment now known or later developed. Figure 7A block diagram of a processing system 700 for implementing the techniques described herein is depicted. In an example, the processing system 700 has one or more central processing units (processors) 721a, 721b, 721c, etc. (collectively or generally referred to as processors 721 and / or processing devices). In aspects of the present disclosure, each processor 721 may include a reduced instruction set computer (RISC) microprocessor. The processor 721 is coupled to system memory (e.g., random access memory (RAM) 724) and various other components via a system bus 733. A read-only memory (ROM) 722 is coupled to the system bus 733 and may include a basic input / output system (BIOS) that controls certain basic functions of the processing system 700.

[0075] Also depicted are an input / output (I / O) adapter 727 and a network adapter 726 coupled to the system bus 733. The I / O adapter 727 may be a Small Computer System Interface (SCSI) adapter that communicates with the hard disk 723 and / or storage device 725, or any other similar component. The I / O adapter 727, hard disk 723, and storage device 725 are collectively referred to herein as mass storage 734. An operating system 740 for execution on the processing system 700 may be stored in the mass storage 734. The network adapter 726 interconnects the system bus 733 with an external network 736, enabling the processing system 700 to communicate with other such systems.

[0076] A display (e.g., a display monitor) 735 is connected to the system bus 733 via a display adapter 732, which may include a graphics adapter to improve the performance of graphics-intensive applications and a video controller. In one aspect of the present disclosure, adapters 726, 727, and / or 732 may be connected to one or more I / O buses, which are connected to the system bus 733 via an intermediate bus bridge (not shown). Suitable I / O buses for connecting peripheral devices such as hard disk controllers, network adapters, and graphics adapters typically include common protocols such as the Peripheral Component Interconnect (PCI). Additional input / output devices are shown as being connected to the system bus 733 via a user interface adapter 728 and a display adapter 732. A keyboard 729, a mouse 730, and a speaker 731 (or other suitable input and / or output, such as a touchscreen for an infotainment system) may be interconnected to the system bus 733 via the user interface adapter 728, which may include, for example, a super I / O chip that integrates multiple device adapters into a single integrated circuit. One or more cameras 120-123, 130-133 are also connected to the system bus 733.

[0077] In some aspects of the present disclosure, the processing system 700 includes a graphics processing unit 737. The graphics processing unit 737 is a specialized electronic circuit designed to manipulate and modify memory to accelerate the creation of images in a frame buffer for output to a display. Generally speaking, the graphics processing unit 737 is very efficient at manipulating computer graphics and image processing, and has a highly parallel structure, which makes it more efficient than a general-purpose CPU for algorithms that process large blocks of data in parallel.

[0078] Thus, as configured herein, processing system 700 includes processing capability in the form of processor 721, storage capability including system memory (e.g., RAM 724) and mass storage 734, input devices such as keyboard 729 and mouse 730, and output capability including speakers 731 and display 735. In some aspects of the present disclosure, a portion of system memory (e.g., RAM 724) and mass storage 734 together store an operating system 740 to coordinate the functionality of the various components shown in processing system 700.

[0079] For the purpose of illustration, descriptions of various examples of the present disclosure have been given, but these descriptions are not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the technology. The terminology used herein is selected to best explain the principles of the technology, practical applications, or technical improvements over technologies found on the market, or to enable others skilled in the art to understand the technology disclosed herein.

[0080] Although the above disclosure has been described with reference to exemplary embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope thereof. Furthermore, many modifications may be made to adapt a particular situation or material to the teachings of the present disclosure without departing from the essential scope of the present disclosure. Therefore, it is intended that the present technology is not limited to the specific embodiments disclosed, but rather encompasses all embodiments falling within the scope of the present application.

Claims

1. A computer-implemented method for collaborative lighting, the method comprising: Receiving first road data by a processing device of the vehicle; receiving, by a processing device of the vehicle, second road data from a sensor associated with the vehicle; identifying, by a processing device of the vehicle, an object based at least in part on the first road data and the second road data and by generating an object map, generating a bounding box around at least one of the vulnerable road user or the road hazard on the object map, and determining a centroid of the bounding box; as well as After determining the centroid of the bounding box, a processing device of the vehicle illuminates the at least one of the vulnerable road user or the road hazard by causing an illumination source external to the vehicle to generate a light pattern to highlight the centroid.

2. The computer-implemented method of claim 1 , wherein: The first road data is received from a device selected from the group consisting of an edge computing node and a cloud computing node of a cloud computing environment.

3. The computer-implemented method of claim 1 , wherein: The vehicle is a first vehicle, and wherein the first road data is received from a second vehicle.

4. The computer-implemented method of claim 3, wherein: The first road data is received from a second vehicle via a direct vehicle-to-vehicle communication protocol between the first vehicle and the second vehicle.

5. The computer-implemented method of claim 1 , wherein: The first road data includes local data and global data.

6. The computer-implemented method of claim 1 , wherein: Identifying the at least one of a vulnerable road user or a road hazard includes fusing, by a processing device of the vehicle, the first road data and the second road data.

7. The computer-implemented method of claim 1 , wherein: Causing the at least one of a vulnerable road user or a road hazard to be illuminated comprises: The at least one of a vulnerable road user or a road hazard is illuminated using a light assembly of the vehicle.

8. The computer-implemented method of claim 1 , wherein: Causing the at least one of a vulnerable road user or a road hazard to be illuminated comprises: The at least one of a vulnerable road user or a road hazard is illuminated using a light assembly of another vehicle.

9. The computer-implemented method of claim 1 , wherein: The at least one of a vulnerable road user or a road hazard is a first at least one of a vulnerable road user or a road hazard, wherein the bounding box is a first bounding box, and wherein the centroid is a first centroid, the method further comprising: identifying at least one of a vulnerable road user or a road hazard; generating a second bounding box on the object map around the second at least one of the vulnerable road user or the road hazard; determining a second centroid of the second bounding box; generating a group bounding box on the object map around a first at least one of the vulnerable road users or the road hazard and a second at least one of the vulnerable road users or the road hazard; and A group centroid of the group bounding box is determined, wherein the group centroid is offset based at least in part on a type of a first at least one of a vulnerable road user or a road hazard and a type of a second at least one of a vulnerable road user or a road hazard.

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