DEVICE FOR INCREASING THE FIELD OF VISION FOR LIDAR DETECTORS AND ILLUMINATORS

Abstract

Device comprising: - a convex lens (510, 615) with a first and a second side; - a first detector (520) mounted on the second side to receive a light pulse having a first angle of incidence on the first side; wherein the first detector (520) is operated to generate a first control signal in response to the first light pulse; - a second detector (520) mounted on the second side to receive a second light pulse having a second angle of incidence on the second side, the second detector (520) being operated to generate a second control signal in response to the second light pulse; and - a processor (440) for generating a range map in response to the first control signal and the second control signal.

Description

BACKGROUND

[0001] The present invention relates generally to autonomous and semi-autonomous vehicles. More specifically, the invention relates, among other things, to a device for improved target object recognition in a vehicle equipped with laser detection and a LIDAR distance measurement system.

[0002] For example, DE 10 2008 025 772 A1 describes a device with a convex lens having a first and a second side; a first detector mounted on the second side to receive a light pulse having a first angle of incidence on the first side; wherein the first detector is operated to generate a first control signal in response to the first light pulse; a second detector that receives a second light pulse, wherein the second detector is operated to generate a second control signal in response to the second light pulse; and a processor for generating a range map in response to the first control signal and the second control signal. Background information

[0003] The operation of modern vehicles is becoming increasingly automated, meaning vehicles take over driving control with less driver intervention. Vehicle automation has been categorized into numerical levels from zero, corresponding to no automation with full human control, to five, corresponding to full automation without any 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 truly "driverless" vehicles correspond to higher levels of automation.

[0004] Vehicles are increasingly being equipped with onboard sensors to autonomously or semi-autonomously perceive their surroundings. A valuable sensor for this task is LiDAR, a surveying technique that measures distances by illuminating a target with laser light. However, stationary LiDAR systems typically require a large number of laser transmitters and detectors to achieve an acceptable point cloud density. It would be desirable to achieve a higher point cloud density while simultaneously limiting the number of laser transmitters. SUMMARY

[0005] Embodiments according to the present disclosure offer a number of advantages. For example, embodiments according to the present disclosure can enable independent validation of control commands of autonomous vehicles to facilitate the diagnosis of software or hardware states in the primary control system. Thus, embodiments according to the present disclosure can be more robust, thereby increasing customer satisfaction.

[0006] According to one aspect of the present invention, a device comprises a convex lens having a first side and a second side, a first detector mounted on the second side to receive a first light pulse with a first angle of incidence on the first side, wherein the first detector is operated to generate a first control signal in response to the first light pulse, a second detector mounted on the second side to receive a second light pulse with a second angle of incidence on the second side, wherein the second detector is operated to generate a second control signal in response to the second light pulse, and a processor for generating a range map in response to the first control signal and the second control signal.

[0007] According to a further aspect of the present invention, a LiDAR system comprising a detector with a convex lens having a first and a second side, a first sensor mounted on the second side for receiving a first light pulse, a second sensor on the second side for receiving a second light pulse, and a processor for generating an area map in response to the first light pulse and the second light pulse.

[0008] According to a further aspect of the present invention, an active detection system with a laser transmission arrangement comprising a first transmitter for transmitting a first light pulse and a second transmitter for transmitting a second light pulse, and a first lens for aligning the first and second light pulses, a detection arrangement comprising a second lens, wherein the second lens has a first convex side and a second convex side, a first sensor mounted on the second convex side for receiving the first light pulse, a second sensor mounted on the second convex side for receiving the second light pulse, and a processor for controlling the laser transmission arrangement and generating an area map in response to the first and second light pulses.

[0009] The aforementioned advantages and other advantages and features of the present disclosure will become apparent from the following detailed description of the preferred embodiments in conjunction with the associated drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The aforementioned features and advantages of this invention, as well as the manner in which they are achieved, become more apparent, and the invention is better understood with reference to the following description of embodiments of the invention in conjunction with the associated drawings, wherein: Fig. Figure 1 shows a schematic diagram of a communication system that includes an autonomously controlled vehicle according to one embodiment. Fig. Figure 2 is a schematic block diagram of an automated drive system (ADS) for a vehicle according to one embodiment. Fig. Figure 3 is a diagram of an exemplary environment for the implementation of the systems and methods disclosed herein. Fig. Figure 4 is a block diagram of an exemplary implementation of a device for LIDAR implementation in a vehicle. Fig. Figure 5 shows a diagram of an exemplary lens with laser detectors. Fig. Figure 6 shows a diagram of an exemplary detector arrangement.

[0011] The examples shown herein illustrate preferred embodiments of the invention. DETAILED DESCRIPTION

[0012] The following detailed description is merely exemplary. Furthermore, there is no obligation to limit the scope to any one of the theories presented in the preceding background or in the following detailed description. For example, the LiDAR sensor of the present invention has a specific application for use on a vehicle. However, as those skilled in the field will recognize, the LiDAR sensor of the invention may also have other applications.

[0013] 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 towards semi-autonomous and fully autonomous driving. For example, collision avoidance systems are known to provide 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 that the driver can take appropriate corrective action to avoid the collision.Adaptive cruise control systems are also known to be equipped with a forward-facing sensor that provides automatic speed control and / or braking when the vehicle in question 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, cameras with image processing, lasers 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 take appropriate action.

[0014] LiDAR sensors are sometimes used in vehicles to detect objects and their orientation and distance from the vehicle. They provide reflections from the objects in the form of multiple scan points, which together form a map of a point cloud area (cluster). A separate scan point is provided for every 1 / 2° across the sensor's entire field of view. Therefore, if a target vehicle or other object is detected in front of the vehicle, multiple scan points can be returned, identifying the distance of the target vehicle from the vehicle in question. By providing a scan point return cluster, objects of various and arbitrary shapes, such as trucks, trailers, bicycles, pedestrians, guardrails, etc., can be detected more effectively. The larger or closer the objects are to the vehicle, the better the detection, as more scan points are provided.

[0015] 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 each pulse to be reflected and returned by objects in its field of view to determine their distance. This measurement is known as time-of-flight measurement. By pulsing the laser very rapidly, a three-dimensional image of objects can be generated within the sensor's field of view. Multiple sensors can be used, and their images can be correlated to create a three-dimensional image of the objects surrounding the vehicle.

[0016] A disadvantage of most known LiDAR sensors is their finite angular resolution. LiDAR is capable of pulsed 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 distance of the field of view is approximately 0.5 meters. 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 great distance can provide insufficient information about the object's boundaries. It would be desirable to estimate the surface length and angular orientation of each hit point to obtain additional object information.

[0017] Fig. Figure 1 schematically illustrates an operating environment comprising 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 fixed 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.

[0018] Vehicle 12, which is in Fig. Figure 1, shown schematically, includes a drive system 13, which in various embodiments can include an internal combustion engine, an electric machine such as a traction motor, and / or a fuel cell drive system. The vehicle 12 is shown as a passenger car in the illustrated embodiment; however, it should be noted that any other vehicle, including motorcycles, trucks, SUVs, motorhomes (RVs), watercraft, aircraft, etc., can also be used.

[0019] The vehicle 12 also includes a transmission 14 configured to transmit power from the drive system 13 to a plurality of vehicle wheels 15 according to selectable speed ratios. Depending on the embodiment, the transmission 14 may be a gear-ratio automatic transmission, a continuously variable transmission, or another suitable transmission. The vehicle 12 also includes wheel brakes 17 configured to deliver a braking torque to the vehicle wheels 15. Depending on the embodiment, the wheel brakes 17 may include friction brakes, a regenerative braking system such as an electric motor, and / or other suitable braking systems.

[0020] The vehicle 12 also includes a steering system 16. While in some embodiments it is shown as a steering wheel for illustrative purposes, the steering system 16 may not include a steering wheel.

[0021] The vehicle 12 includes a wireless communication system 28 configured to communicate wirelessly with other vehicles (“V2V”) and / or infrastructure (“V2I”). In one exemplary embodiment, the wireless communication system 28 is configured to communicate via a wireless local area network (WLAN) using the IEEE 802.11 standard or via mobile data communication. However, the scope of this disclosure also includes additional or alternative communication methods, such as a dedicated short-range radio communication (DSRC) channel. DSRC channels refer to one-way or two-way short-range to medium-range radio communication channels specifically designed for the automotive industry and a corresponding set of protocols and standards.

[0022] The drive system 13, the transmission 14, the steering system 16, and the wheel brakes 17 are connected to or under the control of at least one control unit 22. Although shown as a single unit for illustrative purposes, the control unit 22 may additionally include one or more other “control units.” The control unit 22 may include a microprocessor, such as a central processing unit (CPU) or a graphics processing unit (GPU), which communicates with various types of computer-readable storage devices or media. Computer-readable storage devices or media may include volatile and non-volatile memory in the form of read-only memory (ROM), direct-access memory (RAM), and keep-alive memory (KAM).KAM is a persistent or non-volatile memory that can be used to store various operating variables while the CPU is powered 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 the control unit 22 in controlling the vehicle.

[0023] The control unit 22 includes an automated driving 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. A Level Four system indicates a "high level of automation" with reference to the driving mode-specific performance of an automated driving system in all aspects of the dynamic driving task, even if a human driver does not respond appropriately to a request to intervene. A Level Five system indicates "full automation" and refers to the full-time performance of an automated driving system in all aspects of the dynamic driving task under all road and environmental conditions that can be managed 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 variety of actuators 30 in response to inputs from a variety of sensors 26, such as GPS, RADAR, LIDAR, optical cameras, thermal cameras, ultrasonic sensors and / or additional sensors.

[0024] Fig. Figure 1 illustrates several networked devices that can communicate with the vehicle 12's wireless communication system 28. 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 a computing capability, a transmitter-receiver capable of communicating using a short-range wireless protocol, and a visual display 59. The computing capability includes a microprocessor in the form of a programmable device that contains one or more instructions stored in an internal memory structure and is used to receive binary inputs and produce 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 these signals.In further embodiments, the wireless networked device 57 includes cellular communication functionality, enabling the wireless networked device 57, as described herein, to conduct voice and / or data communications via the mobile network operator system 60 using one or more cellular communication protocols. The visual display 59 may also include a touchscreen as a graphical user interface.

[0025] The mobile network operator system 60 is preferably a mobile phone system comprising a plurality of mobile towers 70 (only one shown), one or more mobile switching centers (MSCs) 72, and all other network components necessary to connect the mobile network operator system 60 to the fixed network 62. Each mobile tower 70 includes transmit and receive antennas and a base station, with the base stations of different mobile towers being connected to the MSC 72 either directly or via intermediate devices, such as a base station control unit. The wireless carrier system 60 can implement any suitable communication 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 mobile tower / base station / MSC arrangements are possible and could be used with the mobile network operator system 60.For example, the base station and the mobile phone tower could be located in the same place or far apart, each base station could be responsible for a single mobile phone tower, or a single base station could serve several mobile phone towers, or several base stations could be coupled to a single MSC, to name just a few of the possible arrangements.

[0026] Apart from using the mobile network operator system 60, a different mobile network operator system in the form of satellite communication can be used to provide unidirectional or bidirectional communication with the vehicle 12. This can be done using one or more communication satellites 66 and an uplink transmitting station 67. Unidirectional communication could, for example, involve satellite radio services, where the programming content (news, music, etc.) is received by the transmitting station 67, packaged for uploading, and then sent to the satellite 66, which broadcasts the programming to the subscribers. Bidirectional communication could, for example, involve 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 network operator system 60.

[0027] The fixed network 62 can be a conventional land-based telecommunications network connected to one or more landline telephones and linking the mobile network operator system 60 to the remote access center 78. For example, the fixed network 62 can include a public switched telephone network (PSTN), such as those used to provide wired telephony, packet-switched data communications, and 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 does not need to be connected via the fixed network 62, but could include radio telephone equipment so that it can communicate directly with a wireless network, such as the mobile network operator system 60.

[0028] Although in Fig. While represented as a single device, Computer 64 can comprise a number of computers accessible via a private or public network, such as the Internet. Each Computer 64 can be used for one or more purposes. In an exemplary embodiment, Computer 64 can be configured as a web server accessible by the vehicle 12 via the wireless communication system 28 and the mobile network operator 60. Other such accessible Computers 64 may include, for example, a computer in a repair shop to which diagnostic information and other vehicle data can be uploaded by the vehicle via the wireless communication system 28 or a third-party storage location, or from which vehicle data or other information can be provided, either by communication with the vehicle 12, the remote access center 78, the wireless networked device 57, or a combination thereof.Computer 64 can maintain a searchable database and a database management system that allows the input, deletion, and modification of data, as well as the receipt of queries to locate data within the database. Computer 64 can 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.

[0029] The remote access center 78 is designed to provide the vehicle 12's wireless communication system 28 with a variety of different system functions, and includes, according to the in Fig. In the exemplary embodiment shown in Figure 1, the remote access center generally comprises one or more switches 80, servers 82, databases 84, live advisors 86, and an automated speech-to-text system (VRS) 88. These various components of the remote access center are preferably interconnected via a wired or wireless local area network 90. ​​The switch 80, which can be used as a private branch exchange (PBX) switch, forwards incoming signals so that voice transmissions are usually sent either to the live advisor 86 via the regular telephone or automatically to the speech-to-text system 88 using VoIP. The live advisor telephone can also use VoIP, as indicated by the dashed line in Figure 1. Fig. Figure 1 shows VoIP and other data communication through the switch 80 are implemented via a modem (not shown) connected between the switch 80 and network 90. ​​Data transmissions are passed through the modem to the server 82 and / or the database 84. The database 84 can store account information, such as subscriber authentication information, vehicle identifiers, profile records, behavioral patterns, and other relevant subscriber information. Data transmissions can 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 control center 78 employing the live advisor 86, it is evident that the remote access control center can instead use VRS 88 as an automated advisor, or a combination of VRS 88 and the live advisor 86 can be used.

[0030] As in Fig. As shown in Figure 2, the ADS 24 incorporates 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 near the vehicle. The perception system 32 is configured to receive inputs such as, for example, Fig. Figure 1 illustrates how the ADS 24 receives input from a variety of sensors 26 and synthesizes and processes sensor inputs to generate parameters that are used as inputs for other control algorithms.

[0031] The perception system 32 includes a sensor fusion and preprocessing module 34 that processes and synthesizes the sensor data 27 from the multitude of sensors 26. The sensor fusion and preprocessing module 34 performs a 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.

[0032] 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 the identification and classification of traffic signals and signs, radar fusion and tracking to account for the sensor's placement and field of view (FOV), 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 encroaching on the roadway, and other obstacles with a high radar cross-section that do not affect the vehicle's ability to travel 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 merge data from RADAR tracks, LIDAR segmentation, LIDAR classification, image classification, object shape pass models, semantic information, motion prediction, raster maps, static obstacle maps, and other sources to produce high-quality object tracks.

[0033] The classification and segmentation module 36 additionally performs a traffic control classification and traffic control device merging with lane association and traffic control device behavior models. The classification and segmentation module 36 generates an object classification and segmentation output 37 that contains object identification information.

[0034] A localization and mapping module 40 uses the object classification and segmentation output 37 to compute parameters, including but not limited to estimates of the vehicle's position and orientation 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., roadworks or construction sites), hills, multi-lane roads, single-lane roads, a variety of road markings and buildings or their absence (e.g., residential and commercial districts), and bridges and overpasses (both above and below a current road segment of the vehicle).

[0035] The Localization and Mapping Module 40 also contains new data resulting from expanded map areas obtained through vehicle-specific mapping functions performed by Vehicle 12 during operation, and mapping data "pushed" to Vehicle 12 via the Wireless Communication System 28. The Localization and Mapping Module 40 updates previous map data with the new information (e.g., new lane markings, new building structures, adding or removing construction zones, etc.), while unaffected map areas remain unchanged. Examples of map data that can be generated or updated include, but are not limited to, diversion lane categorization, lane boundary generation, lane linking, secondary and main road classification, left and right turn classification, and intersection lane creation.

[0036] 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 that environment. GraphS-LAM, a variant of SLAM, uses parsimony matrices to create a graph of observation dependencies.

[0037] The object's position within a map is represented by a Gaussian probability distribution centered around the predicted path of the object. In its simplest form, SLAM 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 to a landmark.

[0038] The initial motion constraint is the vehicle's starting position (e.g., position and orientation), which is comprised of the vehicle's position in two-dimensional or three-dimensional space, including pitch, rotation, and yaw data. The relative motion constraint is the object's displacement, which includes some flexibility to accommodate map consistency. The relative measurement constraint involves 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 location methods within a sensor-generated map typically employ Kalman filters, various statistical correlation methods such as the Pearson product-moment correlation, and / or particle filters.

[0039] In some embodiments, real-time vehicle localization is achieved via a particle filter after a map has been created. 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 representing the accuracy of its position relative to the predicted position. The sensor data is taken into account, and the particle weights are adjusted accordingly. The closer the particle is to the set sensor position, the higher the numerical value of the particle weight.

[0040] As soon as an action command is triggered, 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 position relative to the predicted position and the sensor data. The particles are then resampled, selecting the weights with the largest numerical values, which increases the accuracy of the predicted and sensor-corrected object position. Typically, the probability of a new object position is derived from the mean, variance, and standard deviation of the resample data.

[0041] 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-1 is the previous object position, A tis the action, which is typically a motor command, and Dt is the observable data.

[0042] 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 known as recursive Bayesian estimation, essentially replace the posterior of an estimate into the previous position to compute 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 through the hypothesis in the previous iteration H t-1 and the data D t is evaluated at the current time t.

[0043] A Kalman filter adds an action variable At, where t is a time iteration, resulting in: P(Ht|Ht−1,At,Dt) where the probability of a hypothesis H t based on the previous hypothesis H t-1 , an action At, and the data D t based on the current time t.

[0044] A Kalman filter, widely used in robotics, estimates a current position using a joint probability distribution and, based on an action command, predicts a new position, also using a joint probability distribution. This is also known as state prediction. Sensor data is acquired, and a separate joint probability distribution is calculated, referred to as sensor prediction.

[0045] The state prediction is expressed as: X′t=AXt−1+Bμ+εt where X′t a new state that builds upon the previous state AX t-1 , Bµ and ξ t 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 a Gaussian state error prediction.

[0046] The sensor prediction is expressed as: Z′t=CXt+εz where Z't is the new sensor estimate, C is a function and ξ z a Gaussian sensor error prediction.

[0047] A new estimate of the predicted state is expressed as: XEST=X′t+K(Zt−Z′t) where the product K(Z t - Z' t ) is referred to as the Kalman gain factor. When the difference between the sensor prediction Z't and the actual sensor data Z t is. (that is, if Z t - Z' t ) is relatively close to zero, then X' t than the new state estimate. If Zt - Z' t if relatively larger than zero, the K(Zt - Z' t A factor was added to obtain a new state estimate.

[0048] Once the vehicle motion information is received, the EKF updates the vehicle position estimate and simultaneously expands the estimated covariance. Once the sensor covariance is integrated 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.

[0049] 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.

[0050] An object prediction module 38 uses the object classification and segmentation output 37 to generate parameters, including, but not limited to, the position of a detected obstacle relative to the vehicle, a predicted path of the detected obstacle relative to the vehicle, and the position and orientation of the roadway relative to the vehicle. Bayesian models can be used in some embodiments to predict the intention 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.

[0051] Bayes' theorem, widely used in robotics and also known as the Bayesian filter, is a form of conditional probability. The theorem, represented below in Equation 7, states that the probability of a hypothesis H with data D is equal to the probability of 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)

[0052] P(H / D) is called the posterior and P(H) is called the prior. Bayes' theorem measures a probability degree of conviction in a set before (the preceding) and after (the following) the theorem, where, considering the evidence contained in the data, Bayes' theorem can be used recursively during iteration. With each new iteration, the previous posterior becomes the preceding one to generate a new posterior, until the iteration is complete. Data on the predicted path of objects (including pedestrians, surrounding vehicles, and other moving objects) are output as object prediction output 39 and used in separate calculations, as discussed below.

[0053] The ADS 24 also contains 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 by the remote access center 78. A live expert or consultant, e.g., the one in Fig. The advisor 86 shown can optionally check the object prediction output 39 and provide additional input and / or override automatic driving operations and assume vehicle operation if desired or required by a vehicle situation. The observation and interpretation module 48 generates an interpreted output 49, which includes additional input from the live expert, if available.

[0054] A path planning module 50 processes and synthesizes the object prediction output 39, the interpreted output 49, and additional course information 79 received from an online database or the remote access center 78 to determine a vehicle path to follow in order to keep 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, keep the vehicle in its current lane, and maintain the vehicle on the desired course. The path planning module 50 uses position graph optimization techniques, including nonlinear least-squared position graph optimization, to optimize the vehicle trajectory map in six degrees of freedom and reduce path errors.The route planning module 50 outputs the vehicle route information as route planning output 51. The route planning output value 51 includes a predefined 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.

[0055] A first control module 52 processes and synthesizes the route planning output 51 and the vehicle position output 43 to generate a first control output 53. The first control module 52 also contains the course information 79, which is provided by the remote access control center 78 in the case of a remote takeover operating mode of the vehicle.

[0056] 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 brake command.

[0057] 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 can, for example, control a steering system 16, as shown in Fig. Figure 1 illustrates this. The gearshift control can, for example, control a transmission 14, as shown in Fig. Figure 1 illustrates this. The throttle valve control can, for example, control a drive system 13, as shown in Fig. Figure 1 illustrates this. The brake control can, for example, control the wheel brakes 17, as shown in Fig. 1 illustrates.

[0058] It is understood that the disclosed method can be used with any number of different systems and is not specifically limited to the operating environment described herein. The architecture, structure, configuration, and operation of System 10 and its individual components are generally known. Furthermore, other systems not described here can also use the disclosed method.

[0059] Now shows Fig. 3 An exemplary environment 300 for implementing the systems and methods disclosed herein. In the illustrated embodiment, a vehicle 310 is equipped with an operational LIDAR system. The system has a transmitter that is operational and sends pulsed light or laser 330 away from the vehicle 310. A portion of the pulsed light reaches the objects 320 surrounding the vehicle, and a reflected signal is sent back to a receiver on the vehicle. The vehicle is also equipped with a processor that processes the returned signal to measure the amplitude, travel time, and phase shift, among other features, in order to determine the distance to the objects 320, as well as the size and speed of the objects 320.

[0060] Now shows Fig. Figure 4 shows a functional block diagram of a LIDAR system 400 according to an exemplary method and system. The LIDAR receiver 410 is ready 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 and orientation measurement system (POS) measures the sensor position and orientation 430, the system processor 440 controls all the above-mentioned actions, the vehicle control system and user interface 450, and the data storage 460.

[0061] The LIDAR receiver 410 is ready to generate a laser beam, transmit it to the field of view, and detect the energy reflected from a target. LIDAR sensors use time-of-flight measurements to determine the distance to objects from which the pulsed laser beams are reflected. The oscillating light signal is reflected by 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 then translated into a distance measurement using established techniques.

[0062] The Scanner 420 is used to move the laser beam across the field of view. In one example application, a rotating mirror is used to reflect a stationary laser across the field of view. In another example application, a number of fixed lasers are pulsed in different directions to generate a field-of-view object model.

[0063] A POS 430 is used to determine the time, position, and orientation of the scanner 420 when a laser is pulsed. The system can include a GPS sensor, an inertial measurement system, and other sensors. The POS can also be operational to determine distance, scan angle, sensor position, sensor orientation, and signal amplitude. The data generated by the POS 430 can be combined with data generated by the LIDAR receiver 410 to generate a field-of-view object model.

[0064] The system processor 440 is operational 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 240 receives the data and determines the location of objects within the field of view. It can also determine further information, such as the speed of objects, the composition of objects, signal filtering, etc. The memory 460 is operational 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 operational to receive input from a user, to display results as required, and optionally to generate vehicle control signals in response to the data generated by the system processor 440. Vehicle control signals can be used to control an autonomous vehicle, which, among other things,They can be used to avoid collisions or as a driver warning system.

[0065] Fig. Figure 5 shows a diagram of an exemplary Lens 500 with laser detectors. It is desirable to simplify light collection in LiDAR systems with a large field of view and a large aperture. Flash LiDARs typically have an array of detectors located on a flat surface. This requires the collecting optics to direct light from the field of view angle to a flat surface. However, as the field of view increases, for example, by more than ninety degrees, and the aperture opens to allow for greater light collection, the optical design becomes more complex. In addition to the larger angles, the edges of the field collect significantly less light than the central edges. This results in a shorter coverage area for the LiDAR.

[0066] In the proposed exemplary embodiment, the LiDAR detection arrangements can be flexed to a curved surface using flexible printed circuit boards and the like. Unlike high-resolution imaging applications, many LiDAR systems can tolerate deviations and distortions. Therefore, a spherical lens 510 is proposed as the focusing optic, along with a mechanical support for the curved arrangement. Alternatively, the lens can be a convex lens with a first and a second side. Each field of view angle is focused on the opposite side, with a detector 520 mounted on it. Large detectors 520 can be mounted, each supporting a smaller under-field view.

[0067] Now to Fig.Figure 6, in which an exemplary detection arrangement 600 is shown. In this embodiment, four spherical lenses 615 are shown, each spherical lens comprising several detectors. The arrangement housing 610 contains the spherical lenses 615 and can include a processor for converting the signals from the individual detectors into data, which is used to generate a point map that is used to assist in the control of a vehicle, such as an autonomous vehicle.

[0068] In another embodiment, the LiDAR detection arrangement can include a convex lens with a first side and a second side, and a first detector mounted on the second side to receive a first light pulse where the angle of incidence is on the first side. The first detector is operated to generate a first control signal in response to the first light pulse. A second detector can also be mounted on the second side to receive a second light pulse where the angle of incidence is on the second side. The second detector is operated to generate a second control signal in response to the second light pulse. A processor is used to receive the control signals and to generate data related to a field-of-view point map.The processor can be operated to generate the point map, or it can generate data that is used by another processor that generates the point map.

[0069] A transmission arrangement can be created using a similar configuration, with laser transmitter diodes mounted on a first side of a convex lens and a second side of the convex lens aligned to a viewing field of the transmission arrangement.

[0070] Although this exemplary embodiment is described in the context of a fully functional computer system, it is understood that those skilled in the art will recognize that the mechanisms of the present disclosure can be distributed as a program product using one or more types of non-volatile, computer-readable signal carrier media, which are used to store the program and the associated instructions and to carry out their distribution, such as a non-volatile, computer-readable medium containing the program and computer instructions stored therein to induce a computer processor to execute the program. Such a program product can take many forms, and the present disclosure applies equally regardless of the specific type of computer-readable signal carrier medium used for distribution.Examples of signal carrier media include: writable media, such as floppy disks, hard disks, memory cards and optical storage media, as well as transmission media, such as digital and analog communication links.

Claims

[1] Device comprising: - a convex lens (510, 615) having a first and a second side; - a first detector (520) mounted on the second side for receiving a light pulse having a first angle of incidence on the first side; wherein the first detector (520) is operable to generate a first control signal in response to the first light pulse; - a second detector (520) mounted on the second side for receiving a second light pulse having a second angle of incidence on the second side, the second detector (520) being operable to generate a second control signal in response to the second light pulse; and - a processor (440) for generating an area map in response to the first control signal and the second control signal. [2] The apparatus of claim 1, further comprising a first transmitter (410) for transmitting the first light pulse and a second transmitter (410) for transmitting the second light pulse. [3] The apparatus of claim 2, further comprising a first relay lens (420) for aligning the first light pulse and the second light pulse. [4] The apparatus of claim 1, wherein the apparatus is part of a LIDAR system (400). [5] The device according to claim 1, wherein the convex lens (510, 615) is spherical. [6] The apparatus of claim 1, wherein the first transmitter and the second transmitter are part of a vertical pole cavity surface emitting laser array. [7] The apparatus of claim 1, further comprising a second convex lens (510, 615) having a third and a fourth side, wherein the third detector (520) is mounted on the fourth side and a fourth detector (520) is mounted on the fourth side, wherein the third detector (520) is operated to generate a third control signal in response to the third light pulse, and the fourth detector (520) is operated to generate a fourth control signal in response to the fourth light pulse, and wherein the processor (440) is further operated to generate the area map in response to the third control signal and the fourth control signal. [8] LiDAR system (400), comprising: - a detector comprising a convex lens (510, 615) having a first and a second side, a first sensor (520) mounted on the second side for receiving a first light pulse, a second sensor (520) mounted on the second side for receiving a second light pulse; and - a processor (440) for generating an area map in response to the first light pulse and the second light pulse. [9] The LiDAR system (400) of claim 8, further comprising a first transmitter (410) for transmitting the first light pulse and a second transmitter (410) for transmitting the second light pulse. [10] Active detection system comprising: - a laser transmission arrangement having a first transmitter (410) for transmitting a first light pulse and a second transmitter (410) for transmitting a second light pulse and a first lens (420) for aligning the first light pulse and the second light pulse; - a detection arrangement comprising a second lens (510, 615), the second lens (510, 615) having a first convex side and a second convex side, a first sensor (520) mounted on the second convex side for receiving the first light pulse, a second sensor (520) mounted on the second convex side for receiving the second light pulse, and a processor (440) for controlling the laser transmission arrangement and for generating a range map in response to the first light pulse and the second light pulse; and - a processor (440) for controlling the laser transmission arrangement and for generating a range map in response to the first light pulse and the second light pulse.

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