Method and device for segment-spanning detection in a lidar system

Abstract

Device comprising: - a laser emission device for emitting a laser in a first direction; - a first detector comprising a first detection section for detecting a first laser intensity and a second detection section for detecting a second laser intensity, wherein the first detection section and the second detection section are aligned in a first direction, wherein the first detector is still capable of generating a first control signal in response to the detection of the first laser intensity and the second laser intensity; - a second detector with a third detection section for detecting a third laser intensity and a fourth detection section for detecting a fourth laser intensity, wherein the third detection section and the fourth detection section are oriented in a second direction, the second direction being orthogonal to the first direction, wherein the second detector is furthermore capable of generating a second control signal in response to the detection of the third laser intensity and the fourth laser intensity; - a first lens for distributing a first laser reflection over a subsection of the first detection section and the second detection section; - a second lens for distributing a second laser reflection over a subsection of the third and fourth detection sections; and - a processor for determining an initial transmitter in response to the first control signal and the second control signal.

Description

BACKGROUND

[0001] This application generally relates to autonomous and semi-autonomous vehicles. More specifically, the application teaches an apparatus for enhanced target object detection in a vehicle equipped with laser detection and a LIDAR ranging system. Background information

[0002] The operation of modern vehicles is becoming increasingly automated, meaning vehicles assume control of the vehicle with less driver intervention. Vehicle automation has been categorized according to numerical levels from zero, corresponding to no automation with full human control, to five, corresponding to full automation without human control. Various automated driver assistance systems, such as cruise control, adaptive cruise control, and parking assistance systems, correspond to lower levels of automation, while true "driverless" vehicles correspond to higher levels of automation.

[0003] Vehicles are increasingly being equipped with onboard sensors to autonomously or semi-autonomously determine their surroundings. One valuable sensor for this task is LIDAR, a surveying technique that measures distances by illuminating a target with a laser light. However, fixed LiDAR systems typically require a laser for each point in a field of view, which in turn requires a large number of lasers to achieve a dense point cloud from a distance.

[0004] DE 10 2015 101 722 A1 describes a LiDAR sensor with a specific application for use on a vehicle. The LiDAR sensor comprises a plurality of spaced-apart VCSEL arrays, each comprising a single lens. The lens for each VCSEL array directs the beam of the respective laser in the array that illuminates in a desired direction, so that switching the lasers in each array on and off creates a scanning effect. The number and size of the VCSEL arrays are selected to provide the desired FOV for the sensor, and the VCSEL arrays can be positioned on a curved platform to provide this FOV. The sensor also includes one or more detectors for detecting reflections of the laser beams emitted by the lasers. Control electronics are described for scanning the laser beams and generating a three-dimensional point cloud of returned pixels.

[0005] DE 10 2016 108 437 A1 describes a multi-wavelength lidar field system and a method for designing a lidar field system. The system and method include arranging a plurality of lasers in an array to emit a plurality of respective beams, arranging a lens to scatter the plurality of beams at a plurality of respective angles, and arranging a bandpass filter to filter a plurality of reflections received at a plurality of respective angles of incidence and resulting from the plurality of beams emitted by the plurality of lasers at a plurality of respective emission angles. Selecting an emission wavelength for each of the plurality of beams is based on the plurality of respective emission angles to ensure that a received wavelength of each of the plurality of reflections lies in a narrower range than a range of the emission wavelengths.

[0006] WO 2012 / 024098 A2 describes methods and systems for adaptively controlling the illumination of a scene. In particular, a scene is illuminated, and the light reflected from the scene is detected. Information about the level of light intensity received by different pixels of a detector comprising multiple pixels corresponding to different areas within a scene and / or information about a distance to an area within a scene is received. This information is then used as a feedback signal to control the illuminance within the scene. In particular, different areas of the scene can be provided with different illuminances in response to the feedback signal.

[0007] The object of the invention is to achieve a higher density of the point cloud while simultaneously limiting the number of laser transmitters. SUMMARY

[0008] The invention is defined by the claims.

[0009] Embodiments according to the present disclosure provide several advantages. For example, embodiments according to the present disclosure may enable independent validation of autonomous vehicle control commands to facilitate diagnosis of software or hardware conditions in the primary control system.

[0010] Thus, embodiments according to the present disclosure may be more robust, thereby increasing customer satisfaction.

[0011] According to one aspect of the present invention, an apparatus comprises a laser emission device for emitting a laser in a first direction, a first detector having a first detection section for detecting a first laser intensity and a second detection section for detecting a second laser intensity, wherein the first detection section and the second detection section are aligned in a first direction, the first detector further operative to generate a first control signal in response to detecting the first and second laser intensities, a second detector having a third detection section for detecting a third laser intensity and a fourth detection section for detecting a fourth laser intensity, wherein the second direction is orthogonal to the first direction, the second detector further operative,to generate a second control signal in response to detecting the third laser intensity and the fourth laser intensity, a first lens for distributing a first laser reflection over a portion of the first detection portion and the second detection portion, a second lens for distributing a second laser reflection over a portion of the third detection portion and the fourth detection portion, and a processor for determining an original transmitter in response to the first control signal and the second control signal.

[0012] According to another aspect of the present invention, a LiDAR system includes a transmitter for transmitting a light pulse, a first detector having a first sensing portion and a second sensing portion, the first sensing portion and the second sensing portion having a first orientation, a second detector having a third sensing portion and a fourth sensing portion, the third sensing portion and the fourth sensing portion having a second orientation, wherein the second orientation is orthogonal to the first orientation, and a processor for determining a position of the transmitter in response to a first ratio of the light pulse detected by the first sensing portion and the second sensing portion and a second ratio of the light pulse detected by the third sensing portion and the fourth sensing portion.

[0013] According to another aspect of the present invention, a VCSEL array comprises a first transmitter for transmitting a first light pulse and a second transmitter for transmitting a second light pulse, a first detector array having a first vertical detector for receiving a first portion of the first light pulse and a second vertical detector for receiving a first portion of the second light pulse, the first detector further serving to generate a first control signal in response to the first portion of the first light pulse and the first portion of the second light pulse, a second detector array having a first horizontal detector for receiving a second portion of the first light pulse and a second horizontal detector for receiving a second portion of the second light pulse, the second detector further being operative,to generate a second control signal in response to the second part of the first light pulse and the second part of the second light pulse, and a processor for determining a position of an object in response to the first control signal and the second control signal.

[0014] The foregoing advantages and other advantages and features of the present disclosure will become apparent from the following detailed description of the preferred embodiments taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The above and other features and advantages of this invention and the manner of achieving them will become more apparent and the invention will be better understood from the following description of embodiments of the invention taken in conjunction with the accompanying drawings, in which: Fig. 1 shows a schematic diagram of a communication system including a self-controlled vehicle according to one embodiment. Fig. 2 is a schematic block diagram of an automated drive system (ADS) for a vehicle according to an embodiment. Fig. 3 is a diagram of an exemplary environment for implementing the presently disclosed systems and methods. Fig. 4 is a block diagram of an exemplary implementation of a device for LIDAR implementation in a vehicle. Fig. 5 is a block diagram of an exemplary implementation of a method for LIDAR implementation in a vehicle. Fig. Figure 6 illustrates an exemplary embodiment of a proposed VCSEL array for enhanced LiDAR scanning. Fig. 7 illustrates a flowchart of a method for enhanced LiDAR sensing according to an exemplary embodiment.

[0016] The examples presented herein illustrate preferred embodiments of the invention, and such examples should not be construed in any way as limiting the scope of the invention. DETAILED DESCRIPTION

[0017] The following detailed description is merely exemplary in nature and is in no way intended to limit the disclosure or the application and uses thereof. Furthermore, there is no obligation to be limited to any of the theories presented in the foregoing background or the following detailed description. For example, the LiDAR sensor of the present invention has a particular application for use on a vehicle. However, as those skilled in the art will recognize, the LiDAR sensor of the invention may have other applications.

[0018] Modern vehicles sometimes incorporate various active safety and control systems, such as collision avoidance systems, adaptive cruise control systems, lane keeping systems, lane centering systems, etc., with vehicle technology moving toward semi-autonomous and fully autonomous vehicles. For example, collision avoidance systems are known in the field for providing automatic vehicle control, such as braking, when a potential collision with another vehicle or object is detected, and they can also issue a warning so the driver can take appropriate corrective action to avoid the collision.Adaptive cruise control systems are also known for being equipped with a forward-facing sensor that provides automatic cruise control and / or braking when the subject vehicle approaches another vehicle. The object detection sensors for these types of systems can use any of a number of technologies, such as short-range radar, long-range radar, image-processing cameras, laser or LiDAR, ultrasound, etc. The object detection sensors detect vehicles and other objects in the path of a subject vehicle, and the application software uses the object detection information to provide warnings or actions as appropriate.

[0019] LiDAR sensors are sometimes used in vehicles to detect objects, their orientation, and their distance from the vehicle. They provide reflections from the objects as multiple sample points. These multiple sample points form a map of a region of the point cloud (cluster), with a separate sample point provided for every ½° across the sensor's field of view. Therefore, if a target vehicle or other object is detected in front of the vehicle, multiple sample points can be returned, identifying the target vehicle's distance from the vehicle in question. By providing a sample return point cluster, objects of various shapes, such as trucks, trailers, bicycles, pedestrians, guardrails, and so on, can be better detected. The larger the objects are, the closer they are to the vehicle in question, the better the detection, as more sample points are provided.

[0020] Most known LiDAR sensors use a single laser and a rapidly rotating mirror to generate a three-dimensional point cloud of reflections, or return points, surrounding the vehicle. As the mirror rotates, the laser emits pulses of light, and the sensor measures the time it takes for the light pulse to be reflected and re-emitted by objects in its field of view to determine the distance to the objects, known in the field as time-of-flight measurements. By pulsing the laser very rapidly, a three-dimensional image of objects in the sensor's field of view can be created. Multiple sensors can be deployed, and their images can be correlated to create a three-dimensional image of the objects surrounding the vehicle.

[0021] A disadvantage of most known LiDAR sensors is their finite angular resolution. LiDAR is capable of pulsing the laser at discrete angles around the vehicle. For example, if the laser is pulsed with an angular resolution of 0.5 degrees at 50 meters, the field of view distance is approximately 0.5 meters. For a LiDAR in an autonomous vehicle application, a target vehicle may only reflect one or two of the transmitted laser pulses. A few hits on a target object at a long distance may provide insufficient information about the object's boundaries. It would be desirable to estimate the surface length and angular orientation of each hit point and obtain additional object information.

[0022] Fig. 1 schematically illustrates an operating environment including a mobile vehicle communication and control system 10 for a motor vehicle 12. The communication and control system 10 for the vehicle 12 generally includes one or more wireless carrier systems 60, a landline network 62, a computer 64, a networked wireless device 57, including, but not limited to, a smartphone, tablet, or wearable device such as a watch, and a remote access center 78.

[0023] Vehicle 12, which is in Fig. 1, includes a propulsion system 13, which in various embodiments may include an internal combustion engine, an electric machine, such as a traction motor, and / or a fuel cell propulsion system. The vehicle 12 is depicted as a passenger car in the illustrated embodiment, but it should be understood that any other vehicle, including motorcycles, trucks, off-road vehicles (SUVs), recreational vehicles (RVs), watercraft, aircraft, etc., may also be used.

[0024] The vehicle 12 also includes a transmission 14 configured to transfer power from the drive system 13 to a plurality of vehicle wheels 15 according to selectable speed ratios. According to various embodiments, the transmission 14 may include a stepped-ratio automatic transmission, a continuously variable transmission, or other suitable transmission. The vehicle 12 additionally includes wheel brakes 17 configured to provide braking torque to the vehicle wheels 15. The wheel brakes 17, in various embodiments, may include friction brakes, a regenerative braking system such as an electric machine, and / or other suitable braking systems.

[0025] The vehicle 12 also includes a steering system 16. While depicted as a steering wheel for illustrative purposes in some embodiments within the scope of the present disclosure, the steering system 16 may not include a steering wheel.

[0026] The vehicle 12 includes a wireless communication system 28 configured to communicate wirelessly with other vehicles ("V2V") and / or infrastructure ("V2I"). In an exemplary embodiment, the wireless communication system 28 is configured to communicate over a wireless local area network (WLAN) using the IEEE 802.11 standard or via mobile data communication. However, additional or alternative communication methods, such as a dedicated short-range communication (DSRC) channel, are also contemplated within the scope of the present disclosure. DSRC channels refer to one-way or two-way short- to mid-range radio communication channels specifically designed for automotive applications and a corresponding set of protocols and standards.

[0027] The drive system 13, the transmission 14, the steering system 16, and the wheel brakes 17 are in communication with or under the control of at least one control unit 22. Although depicted as a single unit for illustrative purposes, the control unit 22 may additionally include one or more other "control units." The controller 22 may include a microprocessor, such as a central processing unit (CPU) or a graphics processing unit (GPU), in communication with various types of computer-readable storage devices or media. Computer-readable storage devices or media may include volatile and non-volatile memory in read-only memory (ROM), random access memory (RAM), and keep-alive memory (KAM).KAM is persistent or non-volatile memory that can be used to store various operating variables while the CPU is off. Computer-readable storage devices or media can be implemented using any number of known storage devices, such as PROMs (programmable read-only memory), EPROMs (electrical PROMs), EEPROMs (electrically erasable PROMs), flash memory, or any other electrical, magnetic, optical, or combined storage devices capable of storing data, some of which represent executable instructions used by control unit 22 in controlling the vehicle.

[0028] The controller 22 includes an automated drive system (ADS) 24 for automatically controlling various actuators in the vehicle. In an exemplary embodiment, the ADS 24 is a so-called level four or level five automation system. In various embodiments, the autonomous driving system generates an emergency alert signal and transmits the emergency alert signal to a communication system after the vehicle has been brought into the predetermined state and if the operator incapacitation trigger signal is still present for a predetermined period of time or if the operator does not respond within the predetermined period of time. A level five system indicates "full automation" and refers to the full-time performance of an automated driving system of all aspects of the dynamic driving task under all road and environmental conditions manageable by a human driver.In an exemplary embodiment, the ADS 24 is configured to control the drive system 13, the transmission 14, the steering system 16, and the wheel brakes 17 to control vehicle acceleration, steering, and braking without human intervention via a plurality of actuators 30 in response to inputs from a plurality of sensors 26, such as GPS, RADAR, LIDAR, optical cameras, thermal cameras, ultrasonic sensors, and / or additional sensors.

[0029] Fig. 1 illustrates several networked devices that can communicate with the wireless communication system 28 of the vehicle 12. One of the networked devices that can communicate with the vehicle 12 via the wireless communication system 28 is the wireless networked device 57. The wireless networked device 57 can include computer processing capability, a transceiver that can communicate using a short-range wireless protocol, and a visual display 59. The computer processing capability includes a microprocessor in the form of a programmable device that includes one or more instructions stored in an internal memory structure and is operated to receive binary inputs and generate binary outputs. In some embodiments, the wireless networked device 57 includes a GPS module that can receive GPS satellite signals and generate GPS coordinates based on those signals.In further embodiments, the wireless networked device 57 includes cellular communication functionality, whereby the wireless networked device 57, as explained herein, conducts voice and / or data communications via the wireless carrier system 60 using one or more cellular communication protocols. The visual display 59 may also include a touchscreen as a graphical user interface.

[0030] The wireless carrier system 60 is preferably a cellular system that includes a plurality of cellular towers 70 (only one shown), one or more mobile switching centers (MSCs) 72, and any other network components necessary to connect the wireless carrier system 60 to the fixed network 62. Each cellular tower 70 includes transmit and receive antennas and a base station, with the base stations of different cellular towers connecting to the MSC 72 either directly or through intermediary devices such as a base station controller. The wireless carrier system 60 may implement any suitable communications technology, for example, digital technologies such as CDMA (e.g., CDMA2000), LTE (e.g., 4G LTE or 5G LTE), GSM / GPRS, or other current or emerging wireless technologies. Other cellular tower / base station / MSC arrangements are possible and could be used with the wireless carrier system 60.For example, the base station and cell tower could be co-located or remote from each other, each base station could serve a single cell tower, a single base station could serve multiple cell towers, or multiple base stations could be linked to a single MSC, to name just a few of the possible arrangements.

[0031] In addition to using the cellular provider system 60, a different cellular provider system in the form of satellite communications may be used to provide unidirectional or bidirectional communications with the vehicle 12. This may be accomplished using one or more communications satellites 66 and an uplink transmitting station 67. Unidirectional communications may, for example, be satellite radio services, wherein the programming content (news, music, etc.) is received by the transmitting station 67, packaged for upload, and then transmitted to the satellite 66, which broadcasts the programming to subscribers. Bidirectional communications may, for example, be satellite telephone services, which use the satellite 66 to relay telephone communications between the vehicle 12 and the station 67.Satellite telephony can be used either in addition to or instead of the mobile operator system 60.

[0032] The fixed network 62 may be a conventional land-based telecommunications network connected to one or more landline telephones and connecting the mobile service provider system 60 to the remote access center 78. For example, the fixed network 62 may include a public switched telecommunications network (PSTN), such as is used to provide wired telephony, packet-switched data communications, and the Internet infrastructure. One or more segments of the fixed network 62 could be implemented using a standard wired network, a fiber optic or other optical network, a cable network, power lines, other wireless networks, such as wireless local area networks (WLANs) or networks providing wireless broadband access (BWA), or a combination thereof.Furthermore, the remote access center 78 need not be connected via the landline network 62, but could include radio telephone equipment so that it can communicate directly with a wireless network, such as the cellular provider system 60.

[0033] Although in Fig. 1 as a single device, the computer 64 may include a number of computers accessible via a private or public network, such as the Internet. Each computer 64 may be used for one or more purposes. In one exemplary embodiment, the computer 64 may be configured as a web server accessible by the vehicle 12 via the wireless communication system 28 and the cellular provider 60. Other such accessible computers 64 may include, for example, a computer at a repair shop to which diagnostic information and other vehicle data may be uploaded from the vehicle via the wireless communication system 28 or a third-party storage location, or from which vehicle data or other information may be provided, either through communication with the vehicle 12, the remote access center 78, the wireless networked device 57, or a combination thereof.Computer 64 may maintain a searchable database and a database management system that allows for the entry, deletion, and modification of data, as well as the receipt of queries to locate data within the database. Computer 64 may also be used to provide internet connections, such as DNS services, or as a network address server using DHCP or another suitable protocol to assign an IP address to vehicle 12.

[0034] The remote access center 78 is designed to provide the wireless communication system 28 of the vehicle 12 with a variety of different system functions and includes, according to the Fig. 1 generally includes one or more switches 80, servers 82, databases 84, live advisors 86, and an automated voice response system (VRS) 88. These various components of the remote access center are preferably coupled to one another via a wired or wireless local area network 90. ​​The switch 80, which may be used as a private branch exchange (PBX) switch, routes incoming signals so that voice transmissions are usually sent either to the live advisor 86 via the regular telephone or automatically to the voice response system 88 using VoIP. The live advisor telephone may also use VoIP, as indicated by the dashed line in Fig. 1. VoIP and other data communications through switch 80 are implemented via a modem (not shown) connected between switch 80 and network 90. ​​Data transmissions are passed via the modem to server 82 and / or database 84. Database 84 may store account information such as subscriber authentication information, vehicle identifiers, profile records, behavior patterns, and other related subscriber information. Data transmissions may also occur through wireless systems such as 802.11x, GPRS, and the like. Although the illustrated embodiment has been described as being used in conjunction with a manned remote access center 78 employing live advisor 86, it is understood that the remote access center may instead utilize VRS 88 as an automated advisor, or a combination of VRS 88 and live advisor 86 may be used.

[0035] As in Fig. 2, the ADS 24 includes several different control systems, including at least one perception system 32 for determining the presence, position, classification, and trajectory of the detected features or objects in the vicinity of the vehicle. The perception system 32 is configured to receive inputs such as Fig. 1, receives from a plurality of sensors 26 and synthesizes and processes sensor inputs to generate parameters used as inputs to other control algorithms of the ADS 24.

[0036] The perception system 32 includes a sensor fusion and preprocessing module 34 that processes and synthesizes the sensor data 27 from the plurality of sensors 26. The sensor fusion and preprocessing module 34 performs calibration of the sensor data 27, including, but not limited to, LIDAR-to-LIDAR calibration, camera-to-LIDAR calibration, LIDAR-to-chassis calibration, and LIDAR beam intensity calibration. The sensor fusion and preprocessing module 34 outputs preprocessed sensor outputs 35.

[0037] A classification and segmentation module 36 receives the preprocessed sensor output 35 and performs object classification, image classification, traffic light classification, object segmentation, ground segmentation, and object tracking processes. Object classification includes, but is not limited to, the identification and classification of objects in the environment, including identification and classification of traffic signals and signs, radar fusion and tracking to account for sensor placement and field of view, and false positive rejection via lidar fusion to eliminate the many false positives that exist in an urban environment, such as manhole covers, bridges, trees or light poles overhanging the roadway, and other obstacles with a high radar cross-section that do not affect the vehicle's ability to proceed along its course.Additional object classification and tracking processes performed by the classification and segmentation model 36 include, but are not limited to, freespace detection and high-level tracking, which fuse data from radar tracks, lidar segmentation, lidar classification, image classification, object shape matching models, semantic information, motion prediction, raster maps, static obstacle maps, and other sources to produce high-quality object tracks.

[0038] The classification and segmentation module 36 additionally performs traffic control classification and traffic control device fusion with lane association and traffic control device behavior models. The classification and segmentation module 36 generates an object classification and segmentation output 37 containing object identification information.

[0039] A localization and mapping module 40 uses the object classification and segmentation output 37 to calculate parameters, including, but not limited to, estimates of the position and orientation of the vehicle 12 in both typical and challenging driving scenarios. These challenging driving scenarios include, but are not limited to, dynamic environments with many cars (e.g., heavy traffic), environments with large-scale obstructions (e.g., roadwork or construction sites), hills, multi-lane roads, single-lane roads, a variety of road markings and buildings or the lack thereof (e.g., residential and commercial districts), and bridges and overpasses (both above and below a current road segment of the vehicle).

[0040] The localization and mapping module 40 also includes new data captured as a result of expanded map areas obtained through onboard mapping functions performed by the vehicle 12 during operation and mapping data "pushed" to the vehicle 12 via the wireless communication system 28. The localization and mapping module 40 updates the previous map data with the new information (e.g., new lane markings, new building structures, addition or removal of construction zones, etc.), while leaving unaffected map areas unchanged. Examples of map data that may be generated or updated include, but are not limited to, alternate lane categorization, lane boundary generation, lane connection, secondary and primary road classification, left and right turn classification, and intersection lane generation.

[0041] In some embodiments, the localization and mapping module 40 uses simultaneous localization and mapping ("SLAM") techniques to develop maps of the environment. SLAM stands for simultaneous fault localization and mapping. SLAM techniques construct a map of an environment and track the position of an object within the environment. GraphSLAM, a variant of SLAM, uses sparse matrices to create a graph with observation dependencies.

[0042] The object position within a map is represented by a Gaussian probability distribution centered around the object's predicted path. SLAM, in its simplest form, uses three constraints: an initial location constraint; a relative motion constraint, which is the object's path; and a relative measurement constraint, which is one or more measurements of an object relative to a landmark.

[0043] The initial motion constraint is the initial position (e.g., position and orientation) of the vehicle, which consists of the vehicle's position in two-dimensional or three-dimensional space, including pitch, roll, and yaw data. The relative motion constraint is the object's translation, which includes some flexibility to adapt to map consistency. The relative measurement constraint includes one or more measurements from the object sensors to a landmark. The initial position constraint, the relative motion constraint, and the relative measurement constraint are typically Gaussian probability distributions. Object localization methods within a sensor-generated map typically use Kalman filters, various statistical correlation methods such as the Pearson product-moment correlation, and / or particle filters.

[0044] In some embodiments, after a map is created, vehicle localization is achieved in real time using a particle filter. Unlike Bayesian or Kalman filters, particle filters are suitable for nonlinear systems. To locate a vehicle, particles are generated around an expected mean value using a Gaussian probability distribution. Each particle is assigned a numerical weight that represents the accuracy of the particle position to the predicted position. The sensor data is taken into account, and the particle weights are adjusted to the sensor data. The closer the particle approaches the set sensor position, the larger the numerical value of the particle weights.

[0045] As soon as an action command occurs, each particle is updated to a new predicted position. The sensor data is observed at the new predicted position, and each particle is assigned a new weight that indicates the accuracy of the particle's position relative to the predicted position and the sensor data. The particles are resampled, selecting the weights with the largest numerical magnitude, which increases the accuracy of the predicted and sensor-corrected object position. Typically, the mean, variance, and standard deviation of the resampled data yield the probability of a new object position.

[0046] The processing of the particulate filter is expressed as: P(Ht|Ht−1,At,Dt) where H t the current hypothesis is what the object position is. H t-L is the previous object position, A tis the action, which is typically a motor command, and D t are the observable data.

[0047] In some embodiments, the localization and mapping module 40 maintains an estimate of the vehicle's global position by incorporating data from multiple sources, as previously explained in an extended Kalman filter (EKF) framework. Kalman filters are linear filters based on recursive Bayesian filters. Recursive Bayesian filters, also referred to as recursive Bayesian estimation, essentially replace the posterior of an estimate with the previous position to calculate a new posterior on a new iteration of the estimate. This effectively yields: P(Ht|Ht−1,Dt) where the probability of a hypothesis H t by the hypothesis in the previous iteration H t-1 and the data D t is evaluated at the current time t.

[0048] A Kalman filter adds an action variable A t where t is a time iteration, resulting in: P(Ht|Ht−1,At,Dt) where the probability of a hypothesis Ht is based on the previous hypothesis H t-1 , an action At, and the data D t at the present time t.

[0049] A Kalman filter, widely used in robotics, estimates a current position using a joint probability distribution and predicts a new position based on an action command, which is also a joint probability distribution, also known as a state prediction. Sensor data is collected and a separate joint probability distribution is calculated, known as the sensor prediction.

[0050] The state prediction is expressed as: X′t=AXt−1+Bμ+εt where X' tis a new state based on the previous state AX t-1 , Bµ and ξt. The constants A and B are determined by the physics of interest, where µ can be the command of the robot motor and ξ t is a Gaussian state error prediction.

[0051] The sensor prediction is expressed as: Z′t=CXt+εz where Z' t the new sensor estimate, C a function and ξ z is a Gaussian sensor error prediction.

[0052] A new estimate of the predicted state is expressed as: XEST=X′t+K(Zt−Z′t) where the product K(Zt - Z' t ) is called the Kalman gain factor. If the difference between the sensor prediction Z' t and the actual sensor data Z t (that is, if Z t - Z' t ) is relatively close to zero, then X' t as the new state estimate. If Zt - Z' t is relatively larger than zero, the K(Z t - Z' t ) factor is added to obtain a new state estimate.

[0053] Once the vehicle motion information is received, the EKF updates the vehicle position estimate while simultaneously extending the estimated covariance. Once the sensor covariance is incorporated into the EKF, the localization and mapping module 40 generates a localization and mapping output 41 that includes the position and orientation of the vehicle 12 with respect to detected obstacles and road features.

[0054] A vehicle odometry module 46 receives data 27 from the vehicle sensors 26 and generates a vehicle odometry output 47, which includes, for example, vehicle heading, speed, and distance information. An absolute positioning module 42 receives the localization and mapping output 41 and the vehicle odometry information 47 and generates a vehicle position output 43, which is used in separate calculations, as discussed below.

[0055] An object prediction module 38 uses the object classification and segmentation output 37 to generate parameters, including, but not limited to, a position of a detected obstacle relative to the vehicle, a predicted path of the detected obstacle relative to the vehicle, and a position and orientation of the roadways relative to the vehicle. Bayesian models can be used in some embodiments to predict the intent of a driver or pedestrian based on semantic information, previous trajectories, and immediate pose, where pose is the combination of an object's position and orientation.

[0056] Bayes' theorem, widely used in robotics and also known as the Bayesian filter, is a form of conditional probability. Bayes' theorem, shown below in Equation 7, states that the probability of a hypothesis H with data D is equal to the probability of a hypothesis H times the probability of data D with hypothesis H divided by the probability of data P(D). P(H|D)=P(H)P(D|H)P(D)

[0057] P(H / D) is called the posterior, and P(H) is called the prior. Bayes' theorem measures a degree of probability of belief in a proposition before (the prior) and after (the subsequent), given the evidence in the data. Bayes' theorem can be used recursively in iteration. At each new iteration, the previous posterior becomes the one before it to produce a new posterior until the iteration is complete. Data about the predicted path of objects (including pedestrians, surrounding vehicles, and other moving objects) is output as object prediction output 39 and used in separate calculations, as discussed below.

[0058] The ADS 24 also includes an observation module 44 and an interpretation module 48. The observation module 44 generates an observation output 45, which is received by the interpretation module 48. The observation module 44 and the interpretation module 48 allow access through the remote access center 78. A live expert or consultant, e.g. the one in Fig. The advisor 86 shown in Figure 1 can optionally review the object prediction output 39 and provide additional input and / or override automatic driving actions and assume operation of the vehicle if desired or required by a vehicle situation. The observation and interpretation module 48 generates an interpreted output 49 that includes additional input from the live expert, if any.

[0059] A path planning module 50 processes and synthesizes the object prediction output 39, the interpreted output 49, and additional heading information 79 received from an online database or the remote access center 78 to determine a vehicle path to follow to maintain the vehicle on the desired course while obeying traffic laws and avoiding detected obstacles. The path planning module 50 uses algorithms configured to avoid any detected obstacles near the vehicle, maintain the vehicle in a current travel lane, and maintain the vehicle on the desired course. The path planning module 50 utilizes position graph optimization techniques, including nonlinear least-squares position graph optimization, to optimize the map of vehicle trajectories in six degrees of freedom and reduce path errors.The path planning module 50 outputs the vehicle path information as a path planning output 51. The path planning output 51 includes a predetermined vehicle route based on the route, a vehicle position relative to the route, the position and orientation of the lanes, and the presence and path of detected obstacles.

[0060] A first control module 52 processes and synthesizes the path planning output 51 and the vehicle position output 43 to generate a first control output 53. The first control module 52 also contains the heading information 79 provided by the remote access center 78 in the case of a remote takeover mode of the vehicle.

[0061] A vehicle control module 54 receives the first control output 53 as well as the speed and heading information 47 received from the vehicle odometry 46 and generates a vehicle control output 55. The vehicle control output 55 includes a set of actuator commands to achieve the commanded path from the vehicle control module 54, including, but not limited to, a steering command, a shift command, a throttle command, and a braking command.

[0062] The vehicle control output 55 is transmitted to the actuators 30. In an exemplary embodiment, the actuators 30 include a steering control, a shift control, a throttle control, and a brake control. The steering control may, for example, control a steering system 16, as shown in Fig. 1. The gearshift control may, for example, control a transmission 14 as shown in Fig. 1. The throttle control may, for example, control a drive system 13 as shown in Fig. 1. The brake control can, for example, control the wheel brakes 17, as shown in Fig. 1 illustrates.

[0063] It should be understood that the disclosed method may be used with any number of different systems and is not specifically limited to the operating environment illustrated herein. The architecture, design, configuration, and operation of system 10 and its individual components are generally known. Furthermore, other systems not illustrated herein may also utilize the disclosed methods.

[0064] Now shows Fig. 3 illustrates an exemplary environment 300 for implementing the presently disclosed systems and methods. In the illustrative embodiment, a vehicle 310 with an operational LIDAR system is traveling. The system has a transmitter that is operational and transmits pulsed light or laser 330 away from the vehicle 310. A portion of the pulsed light impinges on objects 320 around the vehicle, and a reflected signal is returned to a receiver on the vehicle. The vehicle is also equipped with a processor that processes the returned signal to measure the amplitude, time of flight, and phase shift, among other characteristics, to determine the distance to objects 320, as well as the size and speed of objects 320.

[0065] Now shows Fig. 4 shows a functional block diagram of a LIDAR system 400 according to an exemplary method and system. The LIDAR receiver 410 is operable to generate a laser beam, transmit it, and detect the laser energy scattered / reflected by an object within the field of view. The scanner 420 moves the laser beam over the target areas, the position-attitude measurement (POS) system 430 measures the sensor position and attitude, the system processor 440 controls all of the above actions, the vehicle control system and user interface 450, and the data storage 460.

[0066] The LIDAR receiver 410 is operable to generate a laser beam, transmit it to the FOV, and detect energy reflected from a target. LIDAR sensors use time-of-flight measurements to determine the distance of objects from which the pulsed laser beams are reflected. The oscillating light signal is reflected from the target and detected by the detector within the LIDAR receiver 410 with a phase shift that depends on the object's distance from the sensor. An electronic phase-locked loop (PLL) can be used to extract the phase shift from the signal, and this phase shift is translated into a distance using known techniques. The detector can also employ peak detection.

[0067] The scanner 420 is used to move the laser beam across the field of view. In one exemplary application, a rotating mirror is used to reflect a stationary laser across the field of view. In another exemplary application, a number of fixed lasers are pulsed in different directions to generate a field-of-view object model.

[0068] A POS 430 is used to determine the time, position, and orientation of the scanner 420 when a laser is pulsed. The system may include a GPS sensor, an inertial measurement system, and other sensors. The POS may further be operable to determine the range measurement, scan angle, sensor position, sensor orientation, and signal amplitude. The data generated by the POS 430 may be combined with the data generated by the LIDAR receiver 410 to generate a field-of-view object model.

[0069] The system processor 440 is operable to send control signals to the LIDAR receiver 410, the POS 430, and the scanner 420, and to receive data from these devices. The system processor 440 receives the data and determines the location of objects within the field of view, and it may determine other information such as the speed of objects, the composition of objects, signal filtering, etc. The memory 460 is operable to store digital representations of returned signal pulses and / or to store digital data calculated by the system processor 440. The vehicle control system / user interface 450 is operable to receive input from a user, to display results as needed, and optionally to generate vehicle control signals in response to the data generated by the system processor 440. Vehicle control signals may be used to control an autonomous vehicle, including:to avoid collisions or as a driver warning system.

[0070] Fig. 5 is a block diagram of an array LiDAR system 500 according to one embodiment. An array LiDAR 510 includes an array of lasers 511. Each laser 511 may be a vertical cavity surface-emitting laser (VCSEL). A VCSEL is a semiconductor-based laser diode that emits an optical beam perpendicular to its surface, as shown. The laser beam 512 emitted by each laser 511 (e.g., VCSEL) forms a field of view. Each object 515 in the field of view of the array of LiDAR 510 results in reflections 516 that are received at a bandpass filter (BPF) 520. The reflections 516 in the field of view of the receiving side of the array of the LiDAR system 500 are filtered by the BPF 520 and focused by a lens 525 onto an avalanche photodiode (APD) 535, which converts the received and filtered light into an electrical signal. This electrical signal is fed to a processing system 530.The processing system 530 may generate the signal ultimately emitted as a laser beam 512. The generated signal may pass through an amplitude modulator 505 before reaching the LiDAR 510 array.

[0071] The exemplary LiDAR system 500 is capable of transmitting light pulses with a known pulse duration and a known pulse repetition rate, and receiving light pulses resulting from the transmitted pulse reflection from an object with the beam path of the transmitted laser pulse. A typical pulse duration can be 10 ns, with a typical pulse repetition rate of 140 kHz. A longer pulse duration can result in a lower SNR.

[0072] The transmitter 510 is capable of transmitting a sequence of laser pulses in a known direction. The transmitter may include a laser diode for generating a laser, a wavelength modulator, a pulse width modulator, a frequency modulator, and / or an amplifier. The transmitter is capable of scanning the laser pulses over a desired field of view for a specific period of time.

[0073] Receiver 530 is operable to receive the pulsed laser signals after they are reflected from objects within the field of view. The receiver may include amplifiers, mixers, circulators, and the like to convert the received pulsed laser signal into an intermediate frequency (IF) signal that can be manipulated by processor 540. Receiver 530 may also be further operable to convert the received pulsed laser signals into digital representations. These digital representations may represent the received pulsed laser signals or the converted IF signal.

[0074] The processor 540 is operable to generate control signals that control the receiver 530 and the transmitter 510. These control signals may be operable to control the pulse rate of the laser pulse and the pulse width of the pulse. Additionally, the control signals may control the receiver 530 so that the receiver 530 is operable to receive reflected pulsed laser signals at different pulse rates and pulse widths. In an exemplary embodiment, the processor generates a control signal such that the transmitter 510 transmits a laser pulse with a known pulse duration. Thus, the laser is transmitted for a known duration of time per pulse. The processor 540 also generates a control signal such that the receiver 530 receives a reflected representation of the laser pulse and determines or records the pulse duration of the received laser pulse.

[0075] Once the data is received from transmitter 510 and / or receiver 530, processor 540 determines the distance to an object in response to the data representing the reflected representation of the laser. Furthermore, processor 540 compares the duration of the transmitted laser pulse with the duration of the corresponding received data pulse. If the received laser pulse has a longer duration than the transmitted laser pulse, then it can be assumed that the light pulse was incident on an inclined surface, since a LiDAR pulse expands when reflected from an inclined surface due to the increased travel time of a portion of the light pulse. For example, a 0.5° beam at 60 m produces a distance of λ = 0.5 m for a 45° surface. If g(t) is the shape of a pulse reflected from a perpendicular surface, then an inclined surface produces the following waveform: s(t)=1Δ∫−Δ / 2Δ / 2g(t−2×tanαc)dx=1Δ∫−Δ / 2Δ / 2∑kg^kexp[2πjk(t−2×tanαc)]dx

[0076] Now to Fig. 6, an exemplary embodiment of a proposed cross-segment detection system 600 is shown in a VCSEL array 610 for enhanced LiDAR scanning. An array LiDAR 610 uses an array of lasers to illuminate a scene. The array LiDAR 610 is shown with the single VCSEL in a rectangular array. Each of the VCSELs has the same orientation on the substrate, and a scattering lens 615 is used to spread the laser beams across the field of view. Switching the individual lasers creates a scan of the scene.

[0077] The VCSEL array 610 is divided into MxN blocks to enable parallel acquisition and reduce acquisition times. In this exemplary embodiment, the VCSEL array is divided into 3x2 segments. Each of the corresponding VCSELs in each segment is programmed to transmit simultaneously. In this example, the 1.1 VCSELs in each segment transmit simultaneously, followed by the 2.1 VCSELs in each segment, and so on. Thus, all VCSELs in the array transmit at least every six transmission cycles. The proposed solution makes it possible to accelerate point cloud acquisition by operating multiple VCSELs in parallel. The considered scheme complicates the Rx circuit and thus results in faster acquisition / higher frame rates. The circuit complication is linear in the number of horizontal and vertical segments (M+N), while the acceleration is quadratic (M×N).In contrast to TOF cameras, which require M×N amplification / sampling circuits to simultaneously capture M×N points, the additional functionality of peak sorting must be implemented in the DSP domain.

[0078] One of the disadvantages of operating multiple VCSELs in parallel is the creation of angular ambiguities, since a simple detector does not provide information about the source of the detected signal. To overcome this disadvantage, the proposed system uses at least two segmented receivers 612, 614. A first receiver 612 has M vertical segments, and a second receiver 614 has N horizontal segments. Transmit and receive optics ensure that pulses transmitted from block #(i,j) are received in the i-th vertical segment and j-th horizontal segment. The signals received in each segment contain pulses resulting from different blocks. Pulse sorting is performed by cross-comparing digitized Rx traces from different segments. When a pulse falls across the edge of two segments, a partial power signal is received from each segment.This ratio can be used to more accurately determine the transmitting VCSEL. Thus, by determining the ratio of the pulse power in each of two or more horizontal segments and two or more vertical segments, the receiver processor can accurately determine the source of the pulse. Each of the segments is equipped with an amplifier 620 and an analog-to-digital converter 622. The amplified and digitized signal from each segment is then coupled to a processor, such as a digital signal processor.

[0079] Looking now at Fig.Figure 7 illustrates a flowchart 700 of a method for enhanced LiDAR sensing according to an exemplary embodiment. The method is adapted to receive a first signal from a first detector representing a first light pulse 710. In this exemplary embodiment, the first detector may include a plurality of vertical segments, with the light pulse being spread across one or more of the vertical segments by a dispersive lens. The first signal may be a power signal received from one or more of the vertical segments, or the first signal may be a digital signal from an analog-to-digital converter, where the first signal is a digital representation of the amplitude or other parameter of the light pulse. The digital representation may further include a ratio dependent on the light pulse being detected by more than one of the vertical segments and an identification of the segments.

[0080] The method is then capable of receiving a second signal from a second detector representing a second light pulse 720. In this exemplary embodiment, the second detector may have a plurality of horizontal segments, with the light pulse being spread across one or more of the horizontal segments by a scattering lens. The second signal may be a power signal received from one or more of the horizontal segments, or the second signal may be a digital signal from an analog-to-digital converter, with the second signal being a digital representation of the amplitude or other parameter of the light pulse. The digital representation may further include a ratio dependent on the light pulse being detected by more than one of the horizontal segments and an identification of the segments.

[0081] The method is then capable of determining a segment of the first detector and a segment of the second detector 730. A data signal is received from each of the first and second receivers. The data signal may be received by an A / D converter associated with a particular segment of a receiver, or the data signal may be received by a multiplexer or the like with an indication of the output segment. The data signal may further indicate more than one segment and / or a ratio of power or amplitude at each segment.

[0082] The method is then able to determine the origin of the light pulse in response to the segment of the first detector and the segment of the second detector 740. An algorithm is used to determine the original VCSEL from the transmitter in response to determining the segment in the first detector and the segment in the second detector. Knowing the segment and the segment's location relative to the diverging lens facilitates, for example, calculating the angle of incidence of the incident light pulse. Alternatively, a lookup table can be used to determine the original VCSEL from the determined segments.

[0083] The method is then capable of generating control data in response to the first light pulse, the second light pulse, and the determination of the origin 750. The control data may include the VCSEL of the origin, the angle of incidence, and the time of flight, or may be an obstacle map generated in part from the determined and calculated data.

[0084] Although this exemplary embodiment is described in the context of a fully functioning computer system, it will be understood that those skilled in the art will recognize that the mechanisms of the present disclosure may be distributed as a program product with one or more types of non-transitory computer-readable signal carrying media used to store and execute the distribution of the program and associated instructions, such as a non-transitory computer-readable medium containing the program and computer instructions stored therein for causing a computer processor to execute the program. Such a program product may take many forms, with the present disclosure applying equally regardless of the specific type of computer-readable signal carrying medium used for distribution.Examples of signal-bearing media include: recordable media such as floppy disks, hard disks, memory cards, and optical disks, and transmission media such as digital and analog communications links.

Claims

[1] Device comprising: - a laser emitting device for emitting a laser in a first direction; - a first detector having a first sensing portion for detecting a first laser intensity and a second sensing portion for detecting a second laser intensity, the first sensing portion and the second sensing portion being aligned in a first direction, the first detector further being operable to generate a first control signal in response to detecting the first laser intensity and the second laser intensity; - a second detector having a third sensing section for detecting a third laser intensity and a fourth sensing section for detecting a fourth laser intensity, the third sensing section and the fourth sensing section being aligned in a second direction, the second direction being orthogonal to the first direction, the second detector further being operable to generate a second control signal in response to detecting the third laser intensity and the fourth laser intensity; - a first lens for distributing a first laser reflection over a portion of the first detection portion and the second detection portion; - a second lens for distributing a second laser reflection over a portion of the third detection section and the fourth detection section; and - a processor for determining an original transmitter in response to the first control signal and the second control signal. [2] The apparatus of claim 1, wherein the first laser emitting device is a vertical cavity surface emitting laser. [3] The device of claim 1, wherein the device is part of a LIDAR system. [4] The apparatus of claim 1, wherein the laser reflection is a reflection of the laser from a target within a field of view. [5] The apparatus of claim 1, wherein the second detector has a fifth detecting portion for detecting a fifth laser intensity, and wherein the processor is further operable to determine an original transmitter by determining a ratio of the fourth laser intensity and the fifth laser intensity. [6] The apparatus of claim 1, wherein the first lens defocuses the first laser reflection to distribute the first laser reflection over a larger portion of the first sensing portion and the second sensing portion. [7] The apparatus of claim 1, wherein the laser emitting device, the first detector, and the second detector have fixed orientations. [8] LiDAR system, comprising: - a transmitter for transmitting a light pulse; - a first detector having a first detection section and a second detection section, wherein the first detection section and the second detection section have a first orientation; - a second detector having a third detection section and a fourth detection section, wherein the third detection section and the fourth detection section have a second orientation, the second orientation being orthogonal to the first orientation; and - a processor for determining a position of the transmitter in response to a first ratio of the light pulse detected by the first detection section and the second detection section and a second ratio of the light pulse detected by the third detection section and the fourth detection section. [9] The LiDAR system of claim 8, further comprising a first lens for distributing the light pulse to the first sensing section and the second sensing section. [10] Vehicle sensor system, comprising: - a VCSEL array having a first transmitter for transmitting a first light pulse and a second transmitter for transmitting a second light pulse; - a first detector array having a first vertical detector for receiving a first portion of the first light pulse and a second vertical detector for receiving a first portion of the second light pulse, the first detector further operable to generate a first control signal in response to the first portion of the first light pulse and the first portion of the second light pulse; - a second detector array having a first horizontal detector for receiving a second portion of the first light pulse and a second horizontal detector for receiving a second portion of the second light pulse, the second detector further operable to generate a second control signal in response to the second portion of the first light pulse and the second portion of the second light pulse; and - a processor for determining a position of an object in response to the first control signal and the second control signal.

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