Detection of anomalies in the interior of an autonomous vehicle

Abstract

An autonomous vehicle includes a microphone that senses sounds inside the vehicle. An output of the interior microphone is processed according to an unsupervised machine learning model such that an anomaly is indicated by the model. In response to detecting the anomaly, a remote dispatcher is notified, who can then resolve the anomaly or transmit instructions to the vehicle to change the trajectory of the vehicle. Outputs of an external microphone and an infotainment system can be removed from the output of the interior microphone prior to processing. In response to detecting that a keyword is spoken in the output of the interior microphone, an anomaly can be detected.

Description

background

[0001] Field of the Invention

[0002] The present invention relates to a sensor system and method for an autonomous vehicle. Background of the Invention

[0004] Recent announcements from various automotive companies (including Ford) predict that fully autonomous vehicles (SAE Level 4) will be commercially available within the next few years. The absence of a driver introduces several issues that could not be anticipated in non-autonomous vehicles or autonomous vehicles with safety drivers. This is especially true for ride-sharing and ride-hailing activities where the passengers do not own the vehicle.

[0005] The systems and methods disclosed herein provide an improved approach for facilitating the safety of autonomous vehicles without a safety driver. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] In order to facilitate an understanding of the advantages of the present invention, a more particular description of the invention briefly described above will be presented by reference to specific embodiments shown in the accompanying drawings. Understanding that these drawings depict only typical embodiments of the invention and are not therefore to be considered limiting of its scope, the invention will be described and explained with additional specificity and detail through the use of the accompanying drawings, in which:

[0007] Figure 1A is a schematic block diagram of a system for implementing an embodiment of the present invention;

[0008] Figure 1B is a schematic block diagram of a vehicle for implementing an embodiment of the present invention, the vehicle including an internal sensor;

[0009] Figure 2 is a schematic block diagram of an example computing device suitable for implementing the method according to an embodiment of the present invention;

[0010] Figure 3 is a process flow chart of a method for detecting anomalies in the interior of an autonomous vehicle according to an embodiment of the present invention; and

[0011] Figure 4 is a process flow chart of a method for training a machine learning model to handle abnormalities in the interior of a vehicle according to an embodiment of the present invention. DETAILED DESCRIPTION

[0012] refer to Figure 1A and Figure 1B , vehicle 100 (see Figure 1B) can house the controller 102. The vehicle 100 may include any vehicle known in the art. The vehicle 100 may have all the structures and features of any vehicle known in the art, including wheels, a transmission coupled to the wheels, an engine coupled to the transmission, a steering system, a braking system, and other systems included in a vehicle known in the art.

[0013] As discussed in more detail herein, the controller 102 can perform autonomous navigation and collision avoidance. The controller 102 can receive one or more outputs from one or more external sensors 104. For example, one or more cameras 106a can be mounted to the vehicle 100 and output the received image stream to the controller 102. The controller 102 can receive one or more audio streams from one or more microphones 106b mounted on the outside of the vehicle, or otherwise receive sound from the outside of the vehicle. For example, the external microphone can have an open air channel between the sound sensor and the outside of the vehicle, and is preferably not installed in the cabin of the vehicle 100. One or more microphones 106b or microphone arrays 106b can be mounted to the vehicle 100 and output the audio stream to the controller 102. The microphone 106b may include a directional microphone having a sensitivity that varies with angle. In such an embodiment, the direction sensed by the microphone can be directed outward from the vehicle 100.

[0014] The external sensors 104 may include sensors such as radar (radio detection and ranging) 106c, lidar (light detection and ranging) 106d, sonar (sound navigation and ranging) 106e, and the like.

[0015] The controller 102 may execute an autonomous operation module 108 that receives the output of the external sensor 104. The autonomous operation module 108 may include an obstacle recognition module 110a, a collision prediction module 110b, and a decision module 110c. The obstacle recognition module 110a analyzes the output of the external sensors and identifies potential obstacles, including people, animals, vehicles, buildings, curbs, and other objects and structures. In particular, the obstacle recognition module 110a may identify vehicle images in the sensor output.

[0016] The collision prediction module 110b predicts which obstacle images are likely to collide with the vehicle 100 based on the current trajectory or current expected path of the vehicle 100. The collision prediction module 110b can evaluate the possibility of collision with the object identified by the obstacle identification module 110a. The decision module 110c can make decisions such as stopping, accelerating, turning, etc. to avoid obstacles. The way in which the collision prediction module 110b predicts potential collisions and the way in which the decision module 110c takes actions to avoid potential collisions can be based on any method or system known in the field of autonomous vehicles.

[0017] The decision module 110c may control the trajectory of the vehicle by actuating one or more actuators 112 that control the direction and speed of the vehicle 100. For example, the actuators 112 may include a steering actuator 114a, an accelerator actuator 114b, and a brake actuator 114c. The configuration of the actuators 114a to 114c may be in accordance with any implementation of such actuators known in the field of autonomous vehicles.

[0018] In the embodiments disclosed herein, the autonomous operation module 108 may perform autonomous navigation to a specified location, autonomous parking, and other autonomous driving activities known in the art.

[0019] The autonomous operation module 108 may also include an abnormality detection module 110d. The abnormality detection module 110d detects abnormalities occurring inside the vehicle and takes one or more actions based thereon. The operation of the abnormality detection module 110d may refer to the following description. Figure 3 and Figure 4 Come to understand.

[0020] The anomaly detection module 110d may take as input the output of one or more internal sensors 116, such as one or more cameras 118a and one or more internal microphones 118b.

[0021] like Figure 1B As shown, one or more cameras 118a can be positioned and oriented in the vehicle to have all seating positions within the field of view of at least one of the cameras 118a. Other areas of the interior of the vehicle can also be within the field of view of at least one of the cameras 118a. Microphones 118b can also be distributed throughout the interior to detect sounds from occupants of the vehicle. External microphones 106b can be used as a reference to distinguish between external sounds and internal sounds, as described below.

[0022] The controller 102 may be in data communication with the server 120, such as via a network 122, which may include any wired or wireless network connection, including a cellular data network connection. The methods disclosed herein may be implemented by the server 120, the controller 102, or a combination of both.

[0023] Figure 2 2 is a block diagram illustrating an example computing device 200. Computing device 200 may be used to execute various programs, such as those discussed herein. Controller 102 and server system 120 may have some or all of the attributes of computing device 200.

[0024] Computing device 200 includes one or more processors 202, one or more memory devices 204, one or more interfaces 206, one or more mass storage devices 208, one or more input / output (I / O) devices 210, and a display device 230, all of which are coupled to a bus 212. Processor 202 includes one or more processors or controllers that execute instructions stored in memory device 204 and / or mass storage device 208. Processor 202 may also include various types of computer-readable media, such as cache memory.

[0025] Memory device 204 includes various computer-readable media such as volatile memory (e.g., random access memory (RAM) 214) and / or nonvolatile memory (e.g., read-only memory (ROM) 216). Memory device 204 may also include a rewritable ROM such as flash memory.

[0026] The mass storage device 208 includes various computer-readable media, such as magnetic tapes, magnetic disks, optical disks, solid-state memory (e.g., flash memory), etc. Figure 2 As shown in , the specific mass storage device is a hard disk drive 224. Various drives may also be included in the mass storage device 208 to enable reading from and / or writing to various computer-readable media. The mass storage device 208 includes removable media 226 and / or non-removable media.

[0027] I / O devices 210 include various devices that allow data and / or other information to be input into or retrieved from computing device 200. Example I / O devices 210 include cursor control devices, keyboards, keypads, microphones, monitors or other display devices, speakers, printers, network interface cards, modems, lenses, CCD or other image capture devices, and the like.

[0028] Display device 230 includes any type of device capable of displaying information to one or more users of computing device 200. Examples of display device 230 include a monitor, a display terminal, a video projection device, and the like.

[0029] Interfaces 206 include various interfaces that allow computing device 200 to interact with other systems, devices, or computing environments. Example interfaces 206 include any number of different network interfaces 220, such as interfaces for a local area network (LAN), a wide area network (WAN), a wireless network, and the Internet. Other interfaces include a user interface 218 and a peripheral device interface 222. Interfaces 206 may also include one or more peripheral interfaces, such as interfaces for a printer, a pointing device (mouse, trackpad, etc.), a keyboard, etc.

[0030] The bus 212 allows the processor 202, memory device 204, interface 206, mass storage device 208, I / O device 210, and display device 230 to communicate with each other and with other devices or components coupled to the bus 212. The bus 212 represents one or more of several types of bus structures, such as a system bus, a PCI bus, an IEEE 1394 bus, a USB bus, and the like.

[0031] For purposes of illustration, programs and other executable program components are shown herein as discrete blocks, but it should be understood that these programs and components may reside in different storage components of the computing device 200 at different times and be executed by the processor 202. Alternatively, the systems and programs described herein may be implemented in hardware or a combination of hardware, software, and / or firmware. For example, one or more application specific integrated circuits (ASICs) may be programmed to perform one or more of the systems and programs described herein.

[0032] refer to Figure 3 The illustrated method 300 may be performed by the controller 102 or by the controller 102 in cooperation with the server system 120. For example, the sensor output may be processed locally according to the method 300 or transmitted to the server system 120 for processing.

[0033] The method 300 may include capturing 302 the output of one or more interior microphones 118b and one or more exterior microphones 106b. The output of the one or more exterior microphones 106b may be subtracted 304 from the output of the one or more interior microphones 118b. The output of one or both of the microphones 106b, 118b may be scaled prior to the subtraction to account for differences in sensitivity and damping of sound transmitted from the exterior to the interior of the vehicle.

[0034] Where multiple external microphones are present, the output of each microphone 106b may be subtracted from the output of the microphone 118b closest to that microphone 106b. Alternatively, the average of the outputs of the microphones 106b may be subtracted from the outputs of all the microphones 118b.

[0035] The result of step 304 is a first difference signal in which the contribution of sounds outside the vehicle is reduced relative to the original output of the one or more interior microphones 118b. The infotainment audio signal may then be subtracted 306 from the first difference signal to obtain a second difference signal. The infotainment audio signal may be a signal coupled to or derived from a speaker inside the vehicle. This reduces the impact of sounds emitted by the interior speakers of the vehicle. The amplitude of the infotainment audio signal may be scaled according to a predetermined value prior to subtraction in order to more closely cancel the infotainment audio signal from the second difference signal.

[0036] The second difference signal can be input 308 to an unsupervised anomaly detection model. In particular, the second difference signal can be input to an unsupervised machine learning algorithm that trains a model based on the signal over time. The unsupervised machine learning algorithm can be any unsupervised machine learning algorithm known in the art. The result of step 308 is a model that outputs whether a given signal is abnormal. In particular, the unsupervised anomaly detection model can detect audible anomalies that indicate distress (e.g., shouting, screaming, banging, etc.).

[0037] The method 300 may also include inputting 310 the second difference signal to a keyword detection algorithm. In some applications, a passenger in the vehicle may specify that a particular keyword should be considered to indicate an anomaly. The keyword may be any arbitrary word or phrase selected by a user to be used as a keyword, such as "apple", "how the flowers grow", etc. The word may be input to the controller 102 as speech or text, such as before starting the ride in the vehicle. Thus, step 310 may include identifying the word in the second difference signal using any speech recognition method known in the art.

[0038] The method 300 may include evaluating 312 whether an anomaly is detected in the second difference signal. If any of the following occurs: (a) the unsupervised machine learning model indicates an anomaly in the second difference signal, and (b) a predefined keyword is detected in the second difference signal, it may be determined 312 that an anomaly is detected. If no anomaly is found, the method 300 may end, i.e., a subsequent output of the microphone 118b may be evaluated according to the method 300.

[0039] If it is determined 312 that an anomaly is detected, various actions may be taken. For example, an alert may be transmitted 314 to a human operator, such as a human operator in data communication with server system 120. The alert may be in the form of an email, text, or application-specific alert output on a computing device used by a human dispatcher, such as a computing device having some or all of the attributes of computing device 200.

[0040] The method 300 may also include streaming 316 the output of one or both of the camera 118a and the one or more microphones 118b to a human operator, e.g., the same computing device or a different computing device to which the alarm was transmitted 312. Streaming 316 may include streaming the portion of the output of the one or more microphones 118b that was evaluated at steps 302 to 312 and found to indicate an abnormality at step 312. The output of the one or more microphones 118b that is streamed at step 316 may include a second difference signal derived from the output of the one or more microphones 118b.

[0041] If it is found 318 that input is received from a human operator indicating that the anomaly is resolved, then the method 300 may end. The method 300 may then be repeated for subsequent outputs of one or more microphones 118b. The input may be received from the same computing device to which the alarm was transmitted or from a different computing device.

[0042] Otherwise, the method 300 may include receiving and executing 320 an instruction from a human operator, such as from the same computing device or a different computing device to which the alert was transmitted 314. Examples of instructions may include instructions to stop, turn, slow, travel to an alternate destination, travel to a hospital or other emergency service provider, or any other change to the operation and trajectory of the vehicle. The controller 102 then executes the instruction by stopping, turning, slowing, or otherwise autonomously traveling to the destination specified in the instruction.

[0043] refer to Figure 4 , given sufficient data about detected anomalies and the operator instructions provided in response to them (release or modification of vehicle operation or destination), a machine learning model can be trained to autonomously invoke actions based on detected anomalies.

[0044] For example, the illustrated method 400 may include storing 402 sensor data within a time range of when the anomaly was detected (e.g., 30 seconds, 1 minute, or some other interval before and after the anomaly was detected). The sensor data may include outputs of the internal sensors 116 and the external sensors 104. The method 400 may also include storing 404 a human operator's response to each anomaly (dismiss, stop, turn, slow, reroute, reroute destination, etc.).

[0045] The data stored at steps 402 and 404 may then be used to train 406 a machine learning model, where the sensor data of the anomaly is the input and the dispatcher's response to the anomaly is the desired output of the sensor data. For example, thousands or tens of thousands of anomalies and their corresponding operator responses may be processed in order to train a machine learning model to replicate the responses of a human operator. The machine learning model may be any machine learning model known in the art, such as a deep neural network, decision tree, clustering, Bayesian network, genetic, or other type of machine learning model.

[0046] Then, the method 400 may include processing 408 subsequent anomalies according to the machine learning model. For example, in the method 300, if an anomaly is detected, the sensor data (internal sensor 116 and external sensor 104) may be input to the machine learning model. The controller 102 may invoke any action indicated by the machine learning model.

[0047] In the above disclosure, reference has been made to the accompanying drawings, which form a part of the present disclosure, and in which specific implementations in which the present disclosure may be practiced are shown by way of illustration. It should be understood that other implementations may be utilized and structural changes may be made without departing from the scope of the present disclosure. References to "one embodiment," "embodiment," "example embodiment," etc. in the specification indicate that the described embodiment may include specific features, structures, or characteristics, but each embodiment may not necessarily include specific features, structures, or characteristics. In addition, such phrases do not necessarily refer to the same embodiment. In addition, when specific features, structures, or characteristics are described in conjunction with an embodiment, whether or not explicitly described, it is considered to be within the knowledge of a technician in the field to affect such features, structures, or characteristics in conjunction with other embodiments.

[0048] Implementations of the systems, devices, and methods disclosed herein may include or utilize a dedicated or general-purpose computer including computer hardware (e.g., one or more processors and system memory as discussed herein). Implementations within the scope of the present disclosure may also include physical and other computer-readable media for carrying or storing computer-executable instructions and / or data structures. Such computer-readable media may be any available media that can be accessed by a general-purpose or special-purpose computer system. A computer-readable medium that stores computer-executable instructions is a computer storage medium (device). A computer-readable medium that carries computer-executable instructions is a transmission medium. Therefore, by way of example and not limitation, implementations of the present disclosure may include at least two distinct computer-readable media: a computer storage medium (device) and a transmission medium.

[0049] Computer storage media (devices) include RAM, ROM, EEPROM, CD-ROM, solid-state drives ("SSD") (e.g., RAM-based), flash memory, phase-change memory ("PCM"), other types of memory, other optical disk storage devices, magnetic disk storage devices or other magnetic storage devices, or any other medium that can be used to store desired program code means in the form of computer-executable instructions or data structures and that can be accessed by a general or special purpose computer.

[0050] The implementation of the device, system and method disclosed herein can communicate through a computer network. "Network" is defined as one or more data links that enable the transmission of electronic data between computer systems and / or modules and / or other electronic devices. When information is transmitted or provided to a computer through a network or another communication connection (hardwired, wireless, or a combination of hardwired or wireless), the computer appropriately regards the connection as a transmission medium. The transmission medium may include a network and / or a data link, which may be used to carry a desired program code means in the form of a computer executable instruction or data structure and may be accessed by a general or special purpose computer. The above combination should also be included in the scope of computer-readable media.

[0051] Computer executable instructions include instructions and data that, when executed on a processor, cause a general-purpose computer, a special-purpose computer, or a special-purpose processing device to perform a specific function or group of functions. Computer executable instructions can be, for example, binary files, intermediate format instructions such as assembly language, or even source code. Although the subject matter is described in language specific to structural features and / or method actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the above-mentioned features or actions. Instead, the described features and actions are disclosed as example forms of implementing the claims.

[0052] Those skilled in the art will appreciate that the present disclosure can be practiced in a network computing environment with many types of computer system configurations, including built-in vehicle computers, personal computers, desktop computers, laptop computers, message processors, handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, mobile phones, PDAs, tablet computers, pagers, routers, switches, various storage devices, etc. The present disclosure can also be practiced in a distributed system environment, where both local and remote computer systems linked by a network (by a hardwired data link, a wireless data link, or a combination of hardwired and wireless data links) perform tasks. In a distributed system environment, program modules can be located in both local and remote memory storage devices.

[0053] In addition, where appropriate, the functions described herein may be performed in one or more of the following: hardware, software, firmware, digital components, or analog components. For example, one or more application specific integrated circuits (ASICs) may be programmed to perform one or more of the systems and procedures described herein. Certain terms are used throughout the specification and claims to refer to specific system components. As will be appreciated by those skilled in the art, components may be referred to by different names. This document is not intended to distinguish between components that have different names but the same function.

[0054] It should be noted that the sensor embodiments discussed above may include computer hardware, software, firmware, or any combination thereof to perform at least a portion of their functionality. For example, the sensor may include computer code configured to be executed in one or more processors, and may include hardware logic / circuitry controlled by the computer code. These example devices are provided herein for purposes of illustration and are not intended to be limiting. The embodiments of the present disclosure may be implemented in other types of devices as known to those skilled in the relevant art.

[0055] At least some embodiments of the present disclosure have been directed to a computer program product including such logic (e.g., in the form of software) stored on any computer usable medium. Such software, when executed in one or more data processing devices, causes the devices to operate as described herein.

[0056] Although various embodiments of the present disclosure have been described above, it should be understood that they are presented by way of example only and are not intended to be limiting. It will be apparent to those skilled in the relevant art that various changes in form and detail may be made therein without departing from the spirit and scope of the present disclosure. Therefore, the breadth and scope of the present disclosure should not be limited by any of the above-described exemplary embodiments, but should only be defined in accordance with the appended claims and their equivalents. The foregoing description has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the present disclosure to the precise form disclosed. In view of the above teachings, many modifications and variations are possible. In addition, it should be noted that any or all of the aforementioned alternative implementations may be used in any desired combination to form another hybrid implementation of the present disclosure.

Claims

1. A system for a vehicle, the system comprising: an interior microphone that senses an interior of the vehicle; External microphone; as well as a controller coupled to the internal microphone and programmed to: autonomously causing the vehicle to traverse the trajectory; receiving an output of the internal microphone during autonomous traversal of the trajectory; alerting a remote human dispatcher if the output of said internal microphone indicates an internal anomaly; subtracting an output of the external microphone from the output of the internal microphone to obtain a first difference signal; subtracting an output of an infotainment audio system from the first difference signal to obtain a second difference signal; as well as If the second difference signal indicates the internal anomaly, the remote human dispatcher is alerted.

2. The system of claim 1, wherein the controller is further programmed to: training an unsupervised machine learning model based on the output of the internal microphone over time; and If the unsupervised machine learning model indicates that the output of the internal microphone indicates the anomaly, the remote human dispatcher is alerted.

3. The system of claim 1, wherein the controller is further programmed to: performing speech recognition relative to the output of the internal microphone; and If the results of speech recognition relative to the output of the internal microphone include a predetermined keyword, the remote human dispatcher is alerted.

4. The system of claim 1, wherein the controller is further programmed to: If instructions are received from the human dispatcher, the trajectory of the vehicle is altered according to the instructions.

5. The system of claim 1, wherein the controller is further programmed to alert the remote human dispatcher if the first difference signal indicates the internal anomaly.

6. The system of claim 1, wherein the controller is further programmed to transmit the output of the internal microphone to the remote human dispatcher if the output of the internal microphone indicates the internal anomaly.

7. The system of claim 1, further comprising a camera, the camera's field of view including the interior of the vehicle; Wherein the controller is further programmed to transmit the output of the internal microphone and the output of the camera to the remote human dispatcher if the output of the internal microphone indicates the internal anomaly.

8. The system of claim 1, further comprising: Steering actuator; Brake actuator; as well as accelerator actuator; Wherein the controller is further programmed to autonomously cause the vehicle to traverse the trajectory by activating the steering actuator, the brake actuator, and the accelerator actuator.

9. The system of claim 1, further comprising: Radio detection and ranging (radar) sensors; as well as Light detection and ranging (lidar) sensors; Wherein the controller is further programmed to autonomously cause the vehicle to traverse the trajectory based on outputs of the radio detection and ranging (radar) sensor and the light detection and ranging (lidar) sensor.

10. A method for a vehicle, the method comprising: receiving, by a controller of the vehicle, an output of an interior microphone sensing an interior of the vehicle; autonomously causing, by the controller, the vehicle to traverse a trajectory; determining, by the controller, that the output of the internal microphone indicates an internal abnormality; in response to determining that the output of the internal microphone indicates an internal anomaly, transmitting, by the controller, an alarm to a remote human dispatcher; receiving, by the controller, an output from an external microphone; subtracting, by the controller, the output of the external microphone from the output of the internal microphone to obtain a first difference signal; subtracting, by the controller, an output of an infotainment audio system from the first difference signal to obtain a second difference signal; as well as The alarm is transmitted by the controller to the remote human dispatcher if the second difference signal indicates the internal anomaly.

11. The method of claim 10, further comprising: training, by the controller over time, an unsupervised machine learning model based on the output of the internal microphone; determining, by the controller, that the unsupervised machine learning model indicates that the output of the internal microphone indicates the anomaly; as well as In response to determining that the unsupervised machine learning model indicates that the output of the internal microphone indicates the anomaly, transmitting, by the controller, the alarm to the remote human dispatcher.

12. The method of claim 10, further comprising: performing, by the controller, speech recognition relative to the output of the internal microphone; determining, by the controller, that a result of speech recognition relative to the output of the internal microphone includes a predetermined keyword; as well as In response to determining that the results of speech recognition relative to the output of the internal microphone include the predetermined keyword, the alert is transmitted to the human dispatcher.

13. The method of claim 10, further comprising: receiving, by the controller, instructions from the human dispatcher; as well as In response to the instruction from the human dispatcher, the trajectory of the vehicle is changed by the controller according to the instruction.

14. The method of claim 10, further comprising: determining, by the controller according to an anomaly detection algorithm, that the first difference signal indicates the internal anomaly; as well as In response to determining according to the anomaly detection algorithm that the first difference signal is indicative of the internal anomaly, transmitting, by the controller, the alarm to the remote human dispatcher.

15. The method of claim 10, further comprising: processing the second difference signal according to an anomaly detection algorithm; determining that an output of the anomaly detection algorithm indicates an internal anomaly; as well as In response to determining that the output of the anomaly detection algorithm indicates the internal anomaly, transmitting, by the controller, the alarm to the remote human dispatcher.

16. The method of claim 15, further comprising transmitting the second difference signal to the remote human dispatcher in response to determining that the output of the anomaly detection algorithm indicates an internal anomaly.

17. The method of claim 10, further comprising: receiving, by the controller, an output of a camera, the camera having a field of view including the interior of the vehicle; as well as In response to determining that the output of the internal microphone indicates an internal anomaly, the output of the internal microphone and the output of the camera are transmitted to the remote human dispatcher.

18. The method of claim 10, further comprising autonomously causing the vehicle to traverse the trajectory by activating a steering actuator, a brake actuator, and an accelerator actuator by the controller.

19. The method of claim 10 further comprising autonomously causing the vehicle to traverse the trajectory based on outputs of a radio detection and ranging (radar) sensor and a light detection and ranging (lidar) sensor by the controller.

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