This disclosure relates generally to optical scanning and, more particularly, to using a light detection and ranging (LiDAR) system to perform far-distance road detection.
Light detection and ranging (LiDAR) systems use light pulses to create an image or point cloud of the external environment. Some typical LiDAR systems include a light source, a light transmitter, a light steering system, and a light detector. The light source generates a light beam that is directed by the light steering system in particular directions when being transmitted from the LiDAR system. When a transmitted light beam is scattered by an object, a portion of the scattered light returns to the LiDAR system as a return light pulse. The light detector detects the return light pulse. Using the difference between the time that the return light pulse is detected and the time that a corresponding light pulse in the light beam is transmitted, the LiDAR system can determine the distance to the object using the speed of light. The light steering system can direct light beams along different paths to allow the LiDAR system to scan the surrounding environment and produce images or point clouds. LiDAR systems can also use techniques other than time-of-flight and scanning to measure the surrounding environment.
A LiDAR system may be used to detect a road surface. When the road surface is located far away from the LiDAR system, the incident light transmitted from the LiDAR system may have a large incident angle with respect to the road surface. As a result, the light energy may spread over the pulse width of a return light pulse. The return light pulse thus becomes elongated in shape and its signal intensity becomes small. Under certain circumstances, the signal intensity of the return light pulse may be so small that it is below the threshold for distinguishing between a signal of a return light pulse and noise. As a result, the return light pulse may not be identified and in turn, this causes difficulty to detect a far-distance road surface.
In various embodiments of the present disclosure, a method for performing far-distance road surface detection is provided. The method uses a sliding time window to integrate data samples of return signals and determines whether the maximum signal intensity represents a return light pulse generated from a far-distance road surface. Using the disclosed method, a return light pulse generated from a far-distance road surface can be sufficiently distinguished from noise, even if the return light pulse has a small signal intensity that is close to that of noise.
Embodiments of the present disclosure provide methods and systems for far-distance road surface detection. In one embodiment of the present disclosure, a method for performing far-distance road surface detection is provided. The method comprises obtaining LiDAR detection data samples. The LiDAR detection data samples are associated with signal intensities below a threshold used for near-distance road surface detection. The method further comprises determining, based on a sliding time window, a maximum signal intensity associated with the LiDAR detection data samples. The method further comprises determining, based on the maximum signal intensity, whether the LiDAR detection data samples correspond to a far-distance road surface detection. And in accordance with a determination that the LiDAR detection data samples correspond to a far-distance road surface detection, the method further comprises providing far-distance road surface detection data to a vehicle for controlling movement of the vehicle.
An embodiment of a light detection and ranging (LiDAR) system configured for performing far-distance road surface detection is provided. The LiDAR system comprises one or more processors; memory; and one or more programs stored in the memory. The one or more programs include instructions for obtaining LiDAR detection data samples. The LiDAR detection data samples are associated with signal intensities below a threshold used for near-distance road surface detection. The one or more programs include further instructions for determining, based on a sliding time window, a maximum signal intensity associated with the LiDAR detection data samples. The one or more programs include further instructions for determining, based on the maximum signal intensity, whether the LiDAR detection data samples correspond to a far-distance road surface detection. In accordance with a determination that the LiDAR detection data samples correspond to a far-distance road surface detection, the one or more programs include further instructions for providing far-distance road surface detection data for controlling movement of a vehicle.
An embodiment of a method for performing far-distance road detection using a light detection and ranging (LiDAR) scanning system is provided. The method comprises obtaining LiDAR detection data samples. The LiDAR detection data samples are associated with signal intensities below a threshold used for near-distance road surface detection. The method further comprises determining, based on a sliding time window, a maximum signal intensity associated with the LiDAR detection data samples. The method further comprises determining, based on the maximum signal intensity, whether the LiDAR detection data samples correspond to a far-distance road surface detection. In accordance with a determination that the LiDAR detection data samples correspond to a far-distance road surface detection, the method further comprises providing far-distance road surface detection data for controlling movement of a vehicle.
An embodiment of non-transitory computer readable medium is provided. The computer readable medium storing one or more programs comprising instructions, which when executed by one or more processors of an electronic device, cause the electronic device to perform obtaining LiDAR detection data samples. The LiDAR detection data samples are associated with signal intensities below a threshold used for near-distance road surface detection. The one or more programs comprise further instructions, which cause the electronic device to perform determining, based on a sliding time window, a maximum signal intensity associated with the LiDAR detection data samples; and determining, based on the maximum signal intensity, whether the LiDAR detection data samples correspond to a far-distance road surface detection. In accordance with a determination that the LiDAR detection data samples correspond to a far-distance road surface detection, the one or more programs comprise further instructions, which cause the electronic device to perform providing far-distance road surface detection data for controlling movement of a vehicle.
The present application can be best understood by reference to the figures described below taken in conjunction with the accompanying drawing figures, in which like parts may be referred to by like numerals.
To provide a more thorough understanding of the present invention, the following description sets forth numerous specific details, such as specific configurations, parameters, examples, and the like. It should be recognized, however, that such description is not intended as a limitation on the scope of the present invention but is intended to provide a better description of the exemplary embodiments.
Throughout the specification and claims, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise:
The phrase “in one embodiment” as used herein does not necessarily refer to the same embodiment, though it may. Thus, as described below, various embodiments of the disclosure may be readily combined, without departing from the scope or spirit of the invention.
As used herein, the term “or” is an inclusive “or” operator and is equivalent to the term “and/or,” unless the context clearly dictates otherwise.
The term “based on” is not exclusive and allows for being based on additional factors not described unless the context clearly dictates otherwise.
As used herein, and unless the context dictates otherwise, the term “coupled to” is intended to include both direct coupling (in which two elements that are coupled to each other contact each other) and indirect coupling (in which at least one additional element is located between the two elements). Therefore, the terms “coupled to” and “coupled with” are used synonymously. Within the context of a networked environment where two or more components or devices are able to exchange data, the terms “coupled to” and “coupled with” are also used to mean “communicatively coupled with”, possibly via one or more intermediary devices.
Although the following description uses terms “first,” “second,” etc. to describe various elements, these elements should not be limited by the terms. These terms are only used to distinguish one element from another. For example, a first sensor could be termed a second sensor and, similarly, a second sensor could be termed a first sensor, without departing from the scope of the various described examples. The first sensor and the second sensor can both be sensors and, in some cases, can be separate and different sensors.
In addition, throughout the specification, the meaning of “a”, “an”, and “the” includes plural references, and the meaning of “in” includes “in” and “on”.
Although some of the various embodiments presented herein constitute a single combination of inventive elements, it should be appreciated that the inventive subject matter is considered to include all possible combinations of the disclosed elements. As such, if one embodiment comprises elements A, B, and C, and another embodiment comprises elements B and D, then the inventive subject matter is also considered to include other remaining combinations of A, B, C, or D, even if not explicitly discussed herein. Further, the transitional term “comprising” means to have as parts or members, or to be those parts or members. As used herein, the transitional term “comprising” is inclusive or open-ended and does not exclude additional, unrecited elements or method steps.
Throughout the following disclosure, numerous references may be made regarding servers, services, interfaces, engines, modules, clients, peers, portals, platforms, or other systems formed from computing devices. It should be appreciated that the use of such terms is deemed to represent one or more computing devices having at least one processor (e.g., ASIC, FPGA, PLD, DSP, x86, ARM, RISC-V, ColdFire, GPU, multi-core processors, etc.) configured to execute software instructions stored on a computer readable tangible, non-transitory medium (e.g., hard drive, solid state drive, RAM, flash, ROM, etc.). For example, a server can include one or more computers operating as a web server, database server, or other type of computer server in a manner to fulfill described roles, responsibilities, or functions. One should further appreciate the disclosed computer-based algorithms, processes, methods, or other types of instruction sets can be embodied as a computer program product comprising a non-transitory, tangible computer readable medium storing the instructions that cause a processor to execute the disclosed steps. The various servers, systems, databases, or interfaces can exchange data using standardized protocols or algorithms, possibly based on HTTP, HTTPS, AES, public-private key exchanges, web service APIs, known financial transaction protocols, or other electronic information exchanging methods. Data exchanges can be conducted over a packet-switched network, a circuit-switched network, the Internet, LAN, WAN, VPN, or other type of network.
As used in the description herein and throughout the claims that follow, when a system, engine, server, device, module, or other computing element is described as being configured to perform or execute functions on data in a memory, the meaning of “configured to” or “programmed to” is defined as one or more processors or cores of the computing element being programmed by a set of software instructions stored in the memory of the computing element to execute the set of functions on target data or data objects stored in the memory.
It should be noted that any language directed to a computer should be read to include any suitable combination of computing devices or network platforms, including servers, interfaces, systems, databases, agents, peers, engines, controllers, modules, or other types of computing devices operating individually or collectively. One should appreciate the computing devices comprise a processor configured to execute software instructions stored on a tangible, non-transitory computer readable storage medium (e.g., hard drive, FPGA, PLA, solid state drive, RAM, flash, ROM, etc.). The software instructions configure or program the computing device to provide the roles, responsibilities, or other functionality as discussed below with respect to the disclosed apparatus. Further, the disclosed technologies can be embodied as a computer program product that includes a non-transitory computer readable medium storing the software instructions that causes a processor to execute the disclosed steps associated with implementations of computer-based algorithms, processes, methods, or other instructions. In some embodiments, the various servers, systems, databases, or interfaces exchange data using standardized protocols or algorithms, possibly based on HTTP, HTTPS, AES, public-private key exchanges, web service APIs, known financial transaction protocols, or other electronic information exchanging methods. Data exchanges among devices can be conducted over a packet-switched network, the Internet, LAN, WAN, VPN, or other type of packet switched network; a circuit switched network; cell switched network; or other type of network.
A LiDAR system may need to scan objects (e.g., vehicles, bicycles, pedestrians, buildings, trees, etc.) located in a field-of-view (FOV). As described above, a LiDAR system transmits light pulses to the FOV and receives return light pulses. When the LiDAR system transmits a light pulse to an object, the return light pulse typically has its pulse energy concentrated in a small time interval (e.g., a few nanoseconds). When the pulse energy is concentrated in a small time interval, the return light pulse often has a signal shape and/or signal intensity that is easily distinguishable from a noise floor. For example, to distinguish between a return light pulse and the noise floor, an intensity threshold can be configured such that any pulse having a signal intensity above the threshold is identified as a signal, rather than noise.
A LiDAR system may also be used to detect a road surface. When the road surface is located near the LiDAR system, the detection of a return light pulse is similar to that for an object described above. When the road surface is located far away from the LiDAR system, the incident light may have a large incident angle. As a result, for a return light pulse generated from the far-distance road surface, the light energy may spread over its pulse width. The return light pulse thus becomes elongated in shape and its signal intensity becomes small. Under certain circumstances, the signal intensity of the return light pulse may become so small that it is below the intensity threshold that is normally used for distinguishing return light pulses from noise. As such, it becomes difficult to distinguish between a return light pulse and noise.
Embodiments of present disclosure are described below. In various embodiments of the present disclosure, a method for performing far-distance road surface detection is provided. The method uses a sliding time window to integrate data samples of return signals and determines whether the maximum signal intensity represents a return light pulse generated from a far-distance road surface. Using the disclosed method, a return light pulse generated from a far-distance road surface can be sufficiently distinguished from noise, even if the return light pulse has a small signal intensity that is close to that of noise. The effective signal-to-noise ratio is thus increased. As a result, the detection sensitivity of the LiDAR system is improved. The detection accuracy of the system is also enhanced such that signals generated from a far-distance road surface are less likely to be treated as noise, and vice versa. Additionally, using the far-distance road surface detection results, a vehicle can be controlled to more properly respond to the road conditions associated with a far-distance road surface, thereby improving the vehicle's operating safety. Furthermore, using the far-distance road detection data, the LiDAR system can dynamically adjust one or more components to increase the scanning density in a region-of-interest (ROI). Various embodiments of the present disclosure are described below in more detail.
In typical configurations, motor vehicle 100 comprises one or more LiDAR systems 110 and 120A-F. Each of LiDAR systems 110 and 120A-F can be a scanning-based LiDAR system and/or a non-scanning LiDAR system (e.g., a flash LiDAR). A scanning-based LiDAR system scans one or more light beams in one or more directions (e.g., horizontal and vertical directions) to detect objects in a field-of-view (FOV). A non-scanning based LiDAR system transmits laser light to illuminate an FOV without scanning. For example, a flash LiDAR is a type of non-scanning based LiDAR system. A flash LiDAR can transmit laser light to simultaneously illuminate an FOV using a single light pulse or light shot.
A LiDAR system is often an essential sensor of a vehicle that is at least partially automated. In one embodiment, as shown in
LiDAR system(s) 210 can include one or more of short-range LiDAR sensors, medium-range LiDAR sensors, and long-range LiDAR sensors. A short-range LiDAR sensor measures objects located up to about 20-40 meters from the LiDAR sensor. Short-range LiDAR sensors can be used for, e.g., monitoring nearby moving objects (e.g., pedestrians crossing street in a school zone), parking assistance applications, or the like. A medium-range LiDAR sensor measures objects located up to about 100-150 meters from the LiDAR sensor. Medium-range LiDAR sensors can be used for, e.g., monitoring road intersections, assistance for merging onto or leaving a freeway, or the like. A long-range LiDAR sensor measures objects located up to about 150-300 meters. Long-range LiDAR sensors are typically used when a vehicle is travelling at high speed (e.g., on a freeway), such that the vehicle's control systems may only have a few seconds (e.g., 6-8 seconds) to respond to any situations detected by the LiDAR sensor. As shown in
With reference still to
Other vehicle onboard sensor(s) 230 can also include radar sensor(s) 234. Radar sensor(s) 234 use radio waves to determine the range, angle, and velocity of objects. Radar sensor(s) 234 produce electromagnetic waves in the radio or microwave spectrum. The electromagnetic waves reflect off an object and some of the reflected waves return to the radar sensor, thereby providing information about the object's position and velocity. Radar sensor(s) 234 can include one or more of short-range radar(s), medium-range radar(s), and long-range radar(s). A short-range radar measures objects located at about 0.1-30 meters from the radar. A short-range radar is useful in detecting objects located nearby the vehicle, such as other vehicles, buildings, walls, pedestrians, bicyclists, etc. A short-range radar can be used to detect a blind spot, assist in lane changing, provide rear-end collision warning, assist in parking, provide emergency braking, or the like. A medium-range radar measures objects located at about 30-80 meters from the radar. A long-range radar measures objects located at about 80-200 meters. Medium- and/or long-range radars can be useful in, for example, traffic following, adaptive cruise control, and/or highway automatic braking. Sensor data generated by radar sensor(s) 234 can also be provided to vehicle perception and planning system 220 via communication path 233 for further processing and controlling the vehicle operations.
Other vehicle onboard sensor(s) 230 can also include ultrasonic sensor(s) 236. Ultrasonic sensor(s) 236 use acoustic waves or pulses to measure object located external to a vehicle. The acoustic waves generated by ultrasonic sensor(s) 236 are transmitted to the surrounding environment. At least some of the transmitted waves are reflected off an object and return to the ultrasonic sensor(s) 236. Based on the return signals, a distance of the object can be calculated. Ultrasonic sensor(s) 236 can be useful in, for example, check blind spot, identify parking spots, provide lane changing assistance into traffic, or the like. Sensor data generated by ultrasonic sensor(s) 236 can also be provided to vehicle perception and planning system 220 via communication path 233 for further processing and controlling the vehicle operations.
In some embodiments, one or more other sensor(s) 238 may be attached in a vehicle and may also generate sensor data. Other sensor(s) 238 may include, for example, global positioning systems (GPS), inertial measurement units (IMU), or the like. Sensor data generated by other sensor(s) 238 can also be provided to vehicle perception and planning system 220 via communication path 233 for further processing and controlling the vehicle operations. It is understood that communication path 233 may include one or more communication links to transfer data between the various sensor(s) 230 and vehicle perception and planning system 220.
In some embodiments, as shown in
With reference still to
Sharing sensor data facilitates a better perception of the environment external to the vehicles. For instance, a first vehicle may not sense a pedestrian that is a behind a second vehicle but is approaching the first vehicle. The second vehicle may share the sensor data related to this pedestrian with the first vehicle such that the first vehicle can have additional reaction time to avoid collision with the pedestrian. In some embodiments, similar to data generated by sensor(s) 230, data generated by sensors onboard other vehicle(s) 250 may be correlated or fused with sensor data generated by LiDAR system(s) 210, thereby at least partially offloading the sensor fusion process performed by vehicle perception and planning system 220.
In some embodiments, intelligent infrastructure system(s) 240 are used to provide sensor data separately or together with LiDAR system(s) 210. Certain infrastructures may be configured to communicate with a vehicle to convey information and vice versa. Communications between a vehicle and infrastructures are generally referred to as V2I (vehicle to infrastructure) communications. For example, intelligent infrastructure system(s) 240 may include an intelligent traffic light that can convey its status to an approaching vehicle in a message such as “changing to yellow in 5 seconds.” Intelligent infrastructure system(s) 240 may also include its own LiDAR system mounted near an intersection such that it can convey traffic monitoring information to a vehicle. For example, a left-turning vehicle at an intersection may not have sufficient sensing capabilities because some of its own sensors may be blocked by traffics in the opposite direction. In such a situation, sensors of intelligent infrastructure system(s) 240 can provide useful, and sometimes vital, data to the left-turning vehicle. Such data may include, for example, traffic conditions, information of objects in the direction the vehicle is turning to, traffic light status and predictions, or the like. These sensor data generated by intelligent infrastructure system(s) 240 can be provided to vehicle perception and planning system 220 and/or vehicle onboard LiDAR system(s) 210, via communication paths 243 and/or 241, respectively. Communication paths 243 and/or 241 can include any wired or wireless communication links that can transfer data. For example, sensor data from intelligent infrastructure system(s) 240 may be transmitted to LiDAR system(s) 210 and correlated or fused with sensor data generated by LiDAR system(s) 210, thereby at least partially offloading the sensor fusion process performed by vehicle perception and planning system 220. V2V and V2I communications described above are examples of vehicle-to-X (V2X) communications, where the “X” represents any other devices, systems, sensors, infrastructure, or the like that can share data with a vehicle.
With reference still to
In other examples, sensor data generated by other vehicle onboard sensor(s) 230 may have a lower resolution (e.g., radar sensor data) and thus may need to be correlated and confirmed by LiDAR system(s) 210, which usually has a higher resolution. For example, a sewage cover (also referred to as a manhole cover) may be detected by radar sensor 234 as an object towards which a vehicle is approaching. Due to the low-resolution nature of radar sensor 234, vehicle perception and planning system 220 may not be able to determine whether the object is an obstacle that the vehicle needs to avoid. High-resolution sensor data generated by LiDAR system(s) 210 thus can be used to correlated and confirm that the object is a sewage cover and causes no harm to the vehicle.
Vehicle perception and planning system 220 further comprises an object classifier 223. Using raw sensor data and/or correlated/fused data provided by sensor fusion sub-system 222, object classifier 223 can detect and classify the objects and estimate the positions of the objects. In some embodiments, object classifier 233 can use machine-learning based techniques to detect and classify objects. Examples of the machine-learning based techniques include utilizing algorithms such as region-based convolutional neural networks (R-CNN), Fast R-CNN, Faster R-CNN, histogram of oriented gradients (HOG), region-based fully convolutional network (R-FCN), single shot detector (SSD), spatial pyramid pooling (SPP-net), and/or You Only Look Once (Yolo).
Vehicle perception and planning system 220 further comprises a road detection sub-system 224. Road detection sub-system 224 localizes the road and identifies objects and/or markings on the road. For example, based on raw or fused sensor data provided by radar sensor(s) 234, camera(s) 232, and/or LiDAR system(s) 210, road detection sub-system 224 can build a 3D model of the road based on machine-learning techniques (e.g., pattern recognition algorithms for identifying lanes). Using the 3D model of the road, road detection sub-system 224 can identify objects (e.g., obstacles or debris on the road) and/or markings on the road (e.g., lane lines, turning marks, crosswalk marks, or the like).
Vehicle perception and planning system 220 further comprises a localization and vehicle posture sub-system 225. Based on raw or fused sensor data, localization and vehicle posture sub-system 225 can determine position of the vehicle and the vehicle's posture. For example, using sensor data from LiDAR system(s) 210, camera(s) 232, and/or GPS data, localization and vehicle posture sub-system 225 can determine an accurate position of the vehicle on the road and the vehicle's six degrees of freedom (e.g., whether the vehicle is moving forward or backward, up or down, and left or right). In some embodiments, high-definition (HD) maps are used for vehicle localization. HD maps can provide highly detailed, three-dimensional, computerized maps that pinpoint a vehicle's location. For instance, using the HD maps, localization and vehicle posture sub-system 225 can determine precisely the vehicle's current position (e.g., which lane of the road the vehicle is currently in, how close it is to a curb or a sidewalk) and predict vehicle's future positions.
Vehicle perception and planning system 220 further comprises obstacle predictor 226. Objects identified by object classifier 223 can be stationary (e.g., a light pole, a road sign) or dynamic (e.g., a moving pedestrian, bicycle, another car). For moving objects, predicting their moving path or future positions can be important to avoid collision. Obstacle predictor 226 can predict an obstacle trajectory and/or warn the driver or the vehicle planning sub-system 228 about a potential collision. For example, if there is a high likelihood that the obstacle's trajectory intersects with the vehicle's current moving path, obstacle predictor 226 can generate such a warning. Obstacle predictor 226 can use a variety of techniques for making such a prediction. Such techniques include, for example, constant velocity or acceleration models, constant turn rate and velocity/acceleration models, Kalman Filter and Extended Kalman Filter based models, recurrent neural network (RNN) based models, long short-term memory (LSTM) neural network based models, encoder-decoder RNN models, or the like.
With reference still to
Vehicle control system 280 controls the vehicle's steering mechanism, throttle, brake, etc., to operate the vehicle according to the planned route and movement. Vehicle perception and planning system 220 may further comprise a user interface 260, which provides a user (e.g., a driver) access to vehicle control system 280 to, for example, override or take over control of the vehicle when necessary. User interface 260 can communicate with vehicle perception and planning system 220, for example, to obtain and display raw or fused sensor data, identified objects, vehicle's location/posture, etc. These displayed data can help a user to better operate the vehicle. User interface 260 can communicate with vehicle perception and planning system 220 and/or vehicle control system 280 via communication paths 221 and 261 respectively, which include any wired or wireless communication links that can transfer data. It is understood that the various systems, sensors, communication links, and interfaces in
LiDAR system 300 can also include other components not depicted in
Laser source 310 outputs laser light for illuminating objects in a field of view (FOV). Laser source 310 can be, for example, a semiconductor-based laser (e.g., a diode laser) and/or a fiber-based laser. A semiconductor-based laser can be, for example, an edge emitting laser (EEL), a vertical cavity surface emitting laser (VCSEL), or the like. A fiber-based laser is a laser in which the active gain medium is an optical fiber doped with rare-earth elements such as erbium, ytterbium, neodymium, dysprosium, praseodymium, thulium and/or holmium. In some embodiments, a fiber laser is based on double-clad fibers, in which the gain medium forms the core of the fiber surrounded by two layers of cladding. The double-clad fiber allows the core to be pumped with a high-power beam, thereby enabling the laser source to be a high power fiber laser source.
In some embodiments, laser source 310 comprises a master oscillator (also referred to as a seed laser) and power amplifier (MOPA). The power amplifier amplifies the output power of the seed laser. The power amplifier can be a fiber amplifier, a bulk amplifier, or a semiconductor optical amplifier. The seed laser can be a solid-state bulk laser or a tunable external-cavity diode laser. In some embodiments, laser source 310 can be an optically pumped microchip laser. Microchip lasers are alignment-free monolithic solid-state lasers where the laser crystal is directly contacted with the end mirrors of the laser resonator. A microchip laser is typically pumped with a laser diode (directly or using a fiber) to obtain the desired output power. A microchip laser can be based on neodymium-doped yttrium aluminum garnet (Y3Al5O12) laser crystals (i.e., Nd:YAG), or neodymium-doped vanadate (i.e., ND:YVO4) laser crystals.
In some variations, fiber-based laser source 400 can be controlled (e.g., by control circuitry 350) to produce pulses of different signal intensities based on the fiber gain profile of the fiber used in fiber-based laser source 400. Communication path 312 couples fiber-based laser source 400 to control circuitry 350 (shown in
Referencing
It is understood that the above descriptions provide non-limiting examples of a laser source 310. Laser source 310 can be configured to include many other types of light sources (e.g., laser diodes, short-cavity fiber lasers, solid-state lasers, and/or tunable external cavity diode lasers) that are configured to generate one or more light signals at various wavelengths. In some examples, light source 310 comprises amplifiers (e.g., pre-amplifiers and/or booster amplifiers), which can be a doped optical fiber amplifier, a solid-state bulk amplifier, and/or a semiconductor optical amplifier. The amplifiers are configured to receive and amplify light signals with desired gains.
With reference back to
Laser beams provided by laser source 310 may diverge as they travel to transmitter 320. Therefore, transmitter 320 often comprises a collimating lens configured to collect the diverging laser beams and produce parallel optical beams with reduced or minimum divergence. The parallel optical beams can then be further directed through various optics such as mirrors and lens. A collimating lens may be, for example, a plano-convex lens. The collimating lens can be configured to have any desired properties such as the beam diameter, divergence, numerical aperture, focal length, or the like. A beam propagation ratio or beam quality factor (also referred to as the M2 factor) is used for measurement of laser beam quality. In many LiDAR applications, it is important to control good laser beam quality in generated a transmitting laser beam. The M2 factor represents a degree of variation of a beam from an ideal Gaussian beam. Thus, the M2 factor reflects how well a collimated laser beam can be focused on a small spot, or how well a divergent laser beam can be collimated. The smaller the M2 factor, the tighter the focus of the laser beam and the more intense a beam spot can be obtained. Therefore, laser source 310 and/or transmitter 320 can be configured to obtained desired M2 factor according to, for example, a scan resolution requirement.
One or more of the light beams provided by transmitter 320 are scanned by steering mechanism 340 to a FOV. Steering mechanism 340 scans light beams in multiple dimensions (e.g., in both the horizontal and vertical dimension) to facilitate LiDAR system 300 to map the environment by generating a 3D point cloud. Steering mechanism 340 will be described in more detail below. The laser light scanned to an FOV may be scattered or reflected by an object in the FOV. At least a portion of the scattered or reflected light returns to LiDAR system 300.
A light detector detects the return light focused by the optical receiver and generates current and/or voltage signals proportional to the incident intensity of the return light. Based on such current and/or voltage signals, the depth information of the object in the FOV can be derived. One exemplary method for deriving such depth information is based on the direct TOF (time of flight), which is described in more detail below. A light detector may be characterized by its detection sensitivity, quantum efficiency, detector bandwidth, linearity, signal to noise ratio (SNR), overload resistance, interference immunity, etc. Based on the applications, the light detector can be configured or customized to have any desired characteristics. For example, optical receiver and light detector 330 can be configured such that the light detector has a large dynamic range while having a good linearity. The light detector linearity indicates the detector's capability of maintaining linear relationship between input optical signal power and the detector's output. A detector having good linearity can maintain a linear relationship over a large dynamic input optical signal range.
To achieve desired detector characteristics, configurations or customizations can be made to the light detector's structure and/or the detector's material system. Various detector structure can be used for a light detector. For example, a light detector structure can be a PIN based structure, which has a undoped intrinsic semiconductor region (i.e., an “i” region) between a p-type semiconductor and an n-type semiconductor region. Other light detector structures comprise, for example, a APD (avalanche photodiode) based structure, a PMT (photomultiplier tube) based structure, a SiPM (Silicon photomultiplier) based structure, a SPAD (single-photon avalanche diode) base structure, and/or quantum wires. For material systems used in a light detector, Si, InGaAs, and/or Si/Ge based materials can be used. It is understood that many other detector structures and/or material systems can be used in optical receiver and light detector 330.
A light detector (e.g., an APD based detector) may have an internal gain such that the input signal is amplified when generating an output signal. However, noise may also be amplified due to the light detector's internal gain. Common types of noise include signal shot noise, dark current shot noise, thermal noise, and amplifier noise (TIA). In some embodiments, optical receiver and light detector 330 may include a pre-amplifier that is a low noise amplifier (LNA). In some embodiments, the pre-amplifier may also include a TIA-transimpedance amplifier, which converts a current signal to a voltage signal. For a linear detector system, input equivalent noise or noise equivalent power (NEP) measures how sensitive the light detector is to weak signals. Therefore, they can be used as indicators of the overall system performance. For example, the NEP of a light detector specifies the power of the weakest signal that can be detected and therefore it in turn specifies the maximum range of a LiDAR system. It is understood that various light detector optimization techniques can be used to meet the requirement of LiDAR system 300. Such optimization techniques may include selecting different detector structures, materials, and/or implement signal processing techniques (e.g., filtering, noise reduction, amplification, or the like). For example, in addition to or instead of using direct detection of return signals (e.g., by using TOF), coherent detection can also be used for a light detector. Coherent detection allows for detecting amplitude and phase information of the received light by interfering the received light with a local oscillator. Coherent detection can improve detection sensitivity and noise immunity.
Steering mechanism 340 can be used with the transceiver (e.g., transmitter 320 and optical receiver and light detector 330) to scan the FOV for generating an image or a 3D point cloud. As an example, to implement steering mechanism 340, a two-dimensional mechanical scanner can be used with a single-point or several single-point transceivers. A single-point transceiver transmits a single light beam or a small number of light beams (e.g., 2-8 beams) to the steering mechanism. A two-dimensional mechanical steering mechanism comprises, for example, polygon mirror(s), oscillating mirror(s), rotating prism(s), rotating tilt mirror surface(s), or a combination thereof. In some embodiments, steering mechanism 340 may include non-mechanical steering mechanism(s) such as solid-state steering mechanism(s). For example, steering mechanism 340 can be based on tuning wavelength of the laser light combined with refraction effect, and/or based on reconfigurable grating/phase array. In some embodiments, steering mechanism 340 can use a single scanning device to achieve two-dimensional scanning or two devices combined to realize two-dimensional scanning.
As another example, to implement steering mechanism 340, a one-dimensional mechanical scanner can be used with an array or a large number of single-point transceivers. Specifically, the transceiver array can be mounted on a rotating platform to achieve 360-degree horizontal field of view. Alternatively, a static transceiver array can be combined with the one-dimensional mechanical scanner. A one-dimensional mechanical scanner comprises polygon mirror(s), oscillating mirror(s), rotating prism(s), rotating tilt mirror surface(s) for obtaining a forward-looking horizontal field of view. Steering mechanisms using mechanical scanners can provide robustness and reliability in high volume production for automotive applications.
As another example, to implement steering mechanism 340, a two-dimensional transceiver can be used to generate a scan image or a 3D point cloud directly. In some embodiments, a stitching or micro shift method can be used to improve the resolution of the scan image or the field of view being scanned. For example, using a two-dimensional transceiver, signals generated at one direction (e.g., the horizontal direction) and signals generated at the other direction (e.g., the vertical direction) may be integrated, interleaved, and/or matched to generate a higher or full resolution image or 3D point cloud representing the scanned FOV.
Some implementations of steering mechanism 340 comprise one or more optical redirection elements (e.g., mirrors or lens) that steer return light signals (e.g., by rotating, vibrating, or directing) along a receive path to direct the return light signals to optical receiver and light detector 330. The optical redirection elements that direct light signals along the transmitting and receiving paths may be the same components (e.g., shared), separate components (e.g., dedicated), and/or a combination of shared and separate components. This means that in some cases the transmitting and receiving paths are different although they may partially overlap (or in some cases, substantially overlap).
With reference still to
Control circuitry 350 can also be configured and/or programmed to perform signal processing to the raw data generated by optical receiver and light detector 330 to derive distance and reflectance information, and perform data packaging and communication to vehicle perception and planning system 220 (shown in
LiDAR system 300 can be disposed in a vehicle, which may operate in many different environments including hot or cold weather, rough road conditions that may cause intense vibration, high or low humidifies, dusty areas, etc. Therefore, in some embodiments, optical and/or electronic components of LiDAR system 300 (e.g., optics in transmitter 320, optical receiver and light detector 330, and steering mechanism 340) are disposed or configured in such a manner to maintain long term mechanical and optical stability. For example, components in LiDAR system 300 may be secured and sealed such that they can operate under all conditions a vehicle may encounter. As an example, an anti-moisture coating and/or hermetic sealing may be applied to optical components of transmitter 320, optical receiver and light detector 330, and steering mechanism 340 (and other components that are susceptible to moisture). As another example, housing(s), enclosure(s), and/or window can be used in LiDAR system 300 for providing desired characteristics such as hardness, ingress protection (IP) rating, self-cleaning capability, resistance to chemical and resistance to impact, or the like. In addition, efficient and economical methodologies for assembling LiDAR system 300 may be used to meet the LiDAR operating requirements while keeping the cost low.
It is understood by a person of ordinary skill in the art that
These components shown in
As described above, some LiDAR systems use the time-of-flight (TOF) of light signals (e.g., light pulses) to determine the distance to objects in a light path. For example, with reference to
Referring back to
By directing many light pulses, as depicted in
If a corresponding light pulse is not received for a particular transmitted light pulse, then it may be determined that there are no objects within a detectable range of LiDAR system 500 (e.g., an object is beyond the maximum scanning distance of LiDAR system 500). For example, in
In
The density of a point cloud refers to the number of measurements (data points) per area performed by the LiDAR system. A point cloud density relates to the LiDAR scanning resolution. Typically, a larger point cloud density, and therefore a higher resolution, is desired at least for the region of interest (ROI). The density of points in a point cloud or image generated by a LiDAR system is equal to the number of pulses divided by the field of view. In some embodiments, the field of view can be fixed. Therefore, to increase the density of points generated by one set of transmission-receiving optics (or transceiver optics), the LiDAR system may need to generate a pulse more frequently. In other words, a light source with a higher pulse repetition rate (PRR) is needed. On the other hand, by generating and transmitting pulses more frequently, the farthest distance that the LiDAR system can detect may be limited. For example, if a return signal from a distant object is received after the system transmits the next pulse, the return signals may be detected in a different order than the order in which the corresponding signals are transmitted, thereby causing ambiguity if the system cannot correctly correlate the return signals with the transmitted signals.
To illustrate, consider an exemplary LiDAR system that can transmit laser pulses with a repetition rate between 500 kHz and 1 MHz. Based on the time it takes for a pulse to return to the LiDAR system and to avoid mix-up of return pulses from consecutive pulses in a conventional LiDAR design, the farthest distance the LiDAR system can detect may be 300 meters and 150 meters for 500 kHz and 1 MHz, respectively. The density of points of a LiDAR system with 500 kHz repetition rate is half of that with 1 MHz. Thus, this example demonstrates that, if the system cannot correctly correlate return signals that arrive out of order, increasing the repetition rate from 500 kHz to 1 MHz (and thus improving the density of points of the system) may reduce the detection range of the system. Various techniques are used to mitigate the tradeoff between higher PRR and limited detection range. For example, multiple wavelengths can be used for detecting objects in different ranges. Optical and/or signal processing techniques are also used to correlate between transmitted and return light signals.
Various systems, apparatus, and methods described herein may be implemented using digital circuitry, or using one or more computers using well-known computer processors, memory units, storage devices, computer software, and other components. Typically, a computer includes a processor for executing instructions and one or more memories for storing instructions and data. A computer may also include, or be coupled to, one or more mass storage devices, such as one or more magnetic disks, internal hard disks and removable disks, magneto-optical disks, optical disks, etc.
Various systems, apparatus, and methods described herein may be implemented using computers operating in a client-server relationship. Typically, in such a system, the client computers are located remotely from the server computers and interact via a network. The client-server relationship may be defined and controlled by computer programs running on the respective client and server computers. Examples of client computers can include desktop computers, workstations, portable computers, cellular smartphones, tablets, or other types of computing devices.
Various systems, apparatus, and methods described herein may be implemented using a computer program product tangibly embodied in an information carrier, e.g., in a non-transitory machine-readable storage device, for execution by a programmable processor; and the method processes and steps described herein, including one or more of the steps of
A high-level block diagram of an exemplary apparatus that may be used to implement systems, apparatus and methods described herein is illustrated in
Processor 610 may include both general and special purpose microprocessors and may be the sole processor or one of multiple processors of apparatus 600. Processor 610 may comprise one or more central processing units (CPUs), and one or more graphics processing units (GPUs), which, for example, may work separately from and/or multi-task with one or more CPUs to accelerate processing, e.g., for various image processing applications described herein. Processor 610, persistent storage device 620, and/or main memory device 630 may include, be supplemented by, or incorporated in, one or more application-specific integrated circuits (ASICs) and/or one or more field programmable gate arrays (FPGAs).
Persistent storage device 620 and main memory device 630 each comprise a tangible non-transitory computer readable storage medium. Persistent storage device 620, and main memory device 630, may each include high-speed random access memory, such as dynamic random access memory (DRAM), static random access memory (SRAM), double data rate synchronous dynamic random access memory (DDR RAM), or other random access solid state memory devices, and may include non-volatile memory, such as one or more magnetic disk storage devices such as internal hard disks and removable disks, magneto-optical disk storage devices, optical disk storage devices, flash memory devices, semiconductor memory devices, such as erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM), digital versatile disc read-only memory (DVD-ROM) disks, or other non-volatile solid state storage devices.
Input/output devices 690 may include peripherals, such as a printer, scanner, display screen, etc. For example, input/output devices 690 may include a display device such as a cathode ray tube (CRT), plasma or liquid crystal display (LCD) monitor for displaying information to a user, a keyboard, and a pointing device such as a mouse or a trackball by which the user can provide input to apparatus 600.
Any or all of the functions of the systems and apparatuses discussed herein may be performed by processor 610, and/or incorporated in, an apparatus or a system such as LiDAR system 300. Further, LiDAR system 300 and/or apparatus 600 may utilize one or more neural networks or other deep-learning techniques performed by processor 610 or other systems or apparatuses discussed herein.
One skilled in the art will recognize that an implementation of an actual computer or computer system may have other structures and may contain other components as well, and that
In some embodiments, LiDAR system 704 is also configured to detect a near-distance road surface. A near-distance road surface is located close to LiDAR system 704 within a predetermined threshold distance (e.g., 60 meters).
Referencing
In contrast, if the incident light has a large incident angle (e.g., close to 90°), a corresponding return light pulse may have a signal intensity that is below an intensity threshold. As a result, the return light pulse may not be easily distinguishable from a noise floor.
Detecting far-distance road surfaces can be important and sometimes essential for operation of a vehicle. For example, when a motor vehicle is moving at a high speed on a freeway, it is essential to detect the condition of the far-distance road surface (e.g., the road surface located at about 60-150 meters from the vehicle) and control the vehicle accordingly. Typically, a vehicle travelling at a high speed only has a few seconds to respond to the condition of a far-distance road surface. For example, at about 100 meters, the road may have a sharp curve and therefore, the LiDAR system needs to detect that curve and provide the detection data to the vehicle planning and control system so that the vehicle can be controlled to slow down to safely pass the curve. As another example, the road surface located at about 120 meters from the vehicle may have a pit or may be bumpy, the LiDAR system thus needs to detect such road conditions and provide the detection data to the vehicle planning and control system so that the vehicle can respond properly.
Referencing
At or beyond the threshold distance D1 from LiDAR system 704, the incident angle of the incident light becomes large. As such, the corresponding return light pulse may not be sufficiently distinguished from the noise floor. Thus, to detect such return light pulses, LiDAR system 704 determines that far-distance road detection should be used. The threshold distance D1 is also referred to as the first threshold distance. The threshold distance D1 can be determined based on previously-received return light pulses and an intensity threshold. For example, LiDAR system 704 may transmits a plurality of light pulses at different directions or incident angles to different portions of road surfaces 720. LiDAR system 704 receives the corresponding return light pulses for these transmitted light pulses. The signal intensities of these return light pulses are compared to an intensity threshold used to distinguish from the noise floor. When one or more return light pulses cannot be sufficiently distinguished from the noise floor, the corresponding one or more transmitted light pulses can be used to determine the threshold distance D1. The threshold distance D1 can also be similarly determined using computer simulations. In one example, the threshold distance D1 is determined to be about 60 meters.
Referencing still to
In some embodiments, LiDAR system 704 can determine if far-distance road detection should be used based on both the threshold distance D1 and the threshold distance D2. For example, if any transmitted light pulses are for detecting road surfaces located between the threshold distance D1 and the threshold distance D2, LiDAR system 704 determines that far-distance road detection should be used. The threshold distance D1 and threshold distance D2 can be used to compute the corresponding LiDAR system parameters such as transmitting light angles, rotation/oscillation speeds of optical components (e.g., the polygon mirror and/or the Galvanometer mirror), or the like. In turn, such LiDAR system parameters can be used by the LiDAR system's control circuitry to determine if far-distance road detection should be used for any particular return light pulse. For example, when processing a return light pulse corresponding to a road surface located within the threshold distance D1 or a road surface located beyond the threshold distance D2, the LiDAR system may disable or simply not use the far-distance road detection. Otherwise, the LiDAR system may enable or use the far-distance road detection for processing a return light pulse.
Referencing back to
In some embodiments, as shown in
In some embodiments, a LiDAR system comprises one or more analog-to-digital converters (ADC) configured to sample a return signal corresponding to a current transmitted light pulse within a starting time position T1 and an ending time position T2 to obtain the LiDAR detection data samples. An ADC can be included in, for example, control circuitry 350 shown in
Referencing
Referencing
Using the sliding time window 1102, subsets of the LiDAR detection data samples associated with return light pulse 1116 can be analyzed between the starting time position T1 and the ending time position T2. In one embodiment, the analysis of return light pulse 1116 is performed by iteratively integrating, from the starting time position T1 to the ending time position T2, the LiDAR detection data samples that have corresponding time positions within the selected time width of sliding time window 1102. As illustrated in
Next, method 1000 proceeds to step 1022, which moves sliding timing window 1102 to the next time position. The next time position may be denoted as T1+Δt, where Δt is a timing step used in the data sampling of the return signals. Step 1024 then determines whether the next time position causes sliding time window 1102 to exceed the ending time position T2. For example, step 1024 determines if the right edge of sliding timing window 1102 exceeds the ending time position T2. If not, method 1000 goes back to step 1014 to integrate a second subset of the LiDAR detection data samples having corresponding time positions within sliding time window 1102 at the time position T1+Δt. The result of the integration is represented as the signal intensity of the second subset of the LiDAR detection data samples. In some embodiments, the second subset and the first subset have overlapping data samples.
Next, method 1000 repeats step 1016 to determine if the signal intensity of the second subset is greater than the current maximum signal intensity I_max. In this case, the current maximum signal intensity I_max is the signal intensity of the first subset of the LiDAR detection data samples. If the determination of step 1016 is “yes” (i.e., the signal intensity of the second subset is greater than the signal intensity of the first subset), method 1000 proceeds to step 1020. In step 1020, this signal intensity of the second subset of the LiDAR detection samples is stored as the new maximum signal intensity I_max. The corresponding time position of slide timing window 1102 (e.g., T1+Δt) is also stored. If the determination of step 1016 is “no” (i.e., the signal intensity of the second subset is less than or equal to the signal intensity of the first subset), method 1000 proceeds to step 1018. Step 1018 keeps the current maximum signal intensity I_max.
Next, method 1000 proceeds to step 1022, which moves sliding time window 1102 to the next time position. The next time position may be at T1+2Δt. Step 1024 then determines whether the next time position T1+2Δt causes sliding time window 1102 to exceed the ending time position T2. If not, method 1000 then again proceeds to step 1014 to integrate the third subset of the LiDAR detection data sample. Steps 1014, 1016, 1018, 1020, 1022, and 1024 are then repeated iteratively to integrate a fourth subset of the LiDAR detection data sample, a fifth subset, a sixth subset, and so forth. In each iteration, if the signal intensity of a particular subset is greater than the stored current maximum signal intensity I_max, the signal intensity of the particular subset is stored as the new maximum signal intensity. The corresponding time position of slide time window 1102 is also stored. If the signal intensity of a particular subset is equal to or less than the stored current maximum signal intensity I_max, the current maximum signal intensity and the current time position of sliding time window 1102 are both unchanged. Accordingly, at the end of the iteration, the stored current maximum signal intensity represents the highest signal intensity among all the subsets of the LiDAR detection data samples. And the stored time position of sliding time window 1102 is the time position of the particular subset that generates the highest signal intensity.
In the above description, the sliding time window is a rectangle window. It is understood that other types of windows may also be used, including Bartlett window, Blackman window, Dolph-Shebyshev window, Hamming window, Hanning window, Kaiser window, etc. Further, the data samples described above are the in the time domain. It is understood that the data samples may also be represented in frequency domain and processed accordingly.
Referencing
In
If the answer in step 1026 is “yes” (i.e., the current maximum signal intensity I_max is greater than the first intensity threshold I1), the LiDAR detection data samples under analysis correspond to a possible return light pulse generated from a far-distance road surface. Method 1000 thus proceeds to step 1028 to make further determinations. Specifically, step 1028 determines if there are any additional LiDAR detection data samples associated with signal intensities above a second intensity threshold 12. The additional LiDAR detection data samples correspond to other possible return signals (not shown) that are also received during the time interval T between the two consecutive transmitted light pulses 1106 and 1107. For example, if there is a non-road surface object (e.g., a debris, a construction zone warning cone, a vehicle that is positioned in front of the LiDAR system, etc.) located in the light path of a transmitted light pulse for detecting far-distance road surface, the object may also generate a return light pulse. The LiDAR system receiver receives the return light pulse from the non-road surface object. The return light pulse generated by the non-road surface object may have an intensity that is larger than the second intensity threshold 12, which indicates that the return light pulse isn't generated from a far-distance road surface. In this case, the far-distance road surface is not detected. In some embodiments, the second intensity threshold 12 is also determined based on simulation and/or experimental data for a non-road surface object. Integrations of data samples correspond to return light pulses of known non-road surface objects can be computed and/or simulated. The second intensity threshold 12 can then be determined based on a minimum intensity of return light pulses of known objects and optionally a multiplier. In some embodiments, the second intensity threshold 12 can be configured to be any desired value using the product of the multiplier and the minimum intensity of return light pulses of known non-road surface objects. The second intensity threshold 12 is greater than the first intensity threshold I1.
Referencing
In some embodiments, after the LiDAR system determines that there is likely a far-distance road detection, the detection results can be used to compute the position of the far-distance road surface and/or provided for controlling movement of the vehicle.
Referencing back to
Next, in step 1306 of method 1300, the LiDAR system and/or vehicle perception and planning system causes at least a part of perception of an environment associated with the vehicle to be generated based on the far-distance road surface detection results. As described above, the perception of the environment may be generated based on the point cloud provided by the LiDAR system, which includes the far-distance road surface detection results (e.g., the depth or distance information of the road surface). In some embodiments, the perception of the environment comprises at least one of a road shape detection or a road surface condition perception. The perceptions can be derived by the vehicle perception and planning system using the point cloud provided by the LiDAR system. The road shape perception comprises a perception of at least one of: an uphill road shape, a downhill road shape, a slope-varying road shape, a left winding road shape, and a right winding road shape. The road surface condition perception comprises a perception of at least one of: a dry road surface, a wet road surface, a flooded road surface, an icy road surface, an oily road surface, an obstructed road surface, and a changing of a road surface condition.
Next, in step 1308 of method 1300, the LiDAR system and/or the vehicle perception and planning system causes the vehicle control system to actuate a vehicle control mechanism based on the perception of the environment associated with the vehicle. For instance, based on the perception, the vehicle planning system plans the next movement of the vehicle. According to the planned movement, the vehicle control system then controls the vehicle to perform at least one of: speeding up, slowing down, turning left, turning right, turning at a pre-determined degree of angle, signaling, pulling to a side of the road, or gradually stopping the vehicle based on the perception of the environment associated with the vehicle. As an example, if a vehicle is moving at a high speed, it is essential to detect the condition of the far-distance road surface (e.g., at 60-150 meters) and control the vehicle accordingly. Typically, a vehicle travelling at a high speed only has a few seconds to react to the condition of the far-distance road surface. For example, at about 100 meters, the road may have a sharp curve and therefore, the LiDAR system needs to detect that curved road surface at that distance. Using the methods described above, the LiDAR system can detect the far-distance curved road surface and provide the detection data to the vehicle planning and control systems so that the vehicle is controlled to safely pass the curve (e.g., slow down, turn left/right, or the like).
In some embodiments, based on the far-distance road surface detection data, the LiDAR system and/or the vehicle perception and planning system cause the vehicle control system to dynamically adjust a region-of-interest (ROI) of the LiDAR system. Typically, the LiDAR system is configured to scan the region-of-interest (ROI) in a denser manner than other regions. For example, there may be more scan lines of an ROI than those of other regions. The region of interest may be, for example, the center front region of a vehicle or the direction of which the vehicle is heading towards. When the vehicle is driving at a high speed (e.g., on a freeway), the center front region located at about 60-150 meters away from the vehicle is important because the vehicle will approach the region in a few seconds. Thus, it may be beneficial to scan the region more densely than other regions. Based on the far-distance road detection data, the LiDAR system can dynamically adjust one or more components to increase the scanning density in such an ROI. For instance, the LiDAR system can increase the number of laser beams that are being directed to the ROI, reduce the scanning speed such that the ROI region has more scanning lines, increase the laser light power, or the like.
Various exemplary embodiments are described herein. Reference is made to these examples in a non-limiting sense. They are provided to illustrate more broadly applicable aspects of the disclosed technology. Various changes may be made, and equivalents may be substituted without departing from the true spirit and scope of the various embodiments. In addition, many modifications may be made to adapt a particular situation, material, composition of matter, process, process act(s) or step(s) to the objective(s), spirit or scope of the various embodiments. Further, as will be appreciated by those with skill in the art, each of the individual variations described and illustrated herein has discrete components and features which may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the various embodiments.
This application claims priority to U.S. Provisional Patent Application Ser. No. 63/155,666, filed Mar. 2, 2021, entitled “ENHANCEMENT OF LIDAR ROAD DETECTION,” the content of which is hereby incorporated by reference for all purposes.
Number | Date | Country | |
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63155666 | Mar 2021 | US |