REPRESENTATION LEARNING FOR OBJECT DETECTION FROM UNLABELED POINT CLOUD SEQUENCES

A method of representation learning for object detection from unlabeled point cloud sequences is described. The method includes detecting moving object traces from temporally-ordered, unlabeled point cloud sequences. The method also includes extracting a set of moving objects based on the moving object traces detected from the sequence of temporally-ordered, unlabeled point cloud sequences. The method further includes classifying the set of moving objects extracted from on the moving object traces detected from the sequence of temporally-ordered, unlabeled point cloud sequences. The method also includes estimating 3D bounding boxes for the set of moving objects based on the classifying of the set of moving objects.

BACKGROUND

Field

Certain aspects of the present disclosure generally relate to machine learning and, more particularly, a system and method for representation learning for object detection from unlabeled point cloud sequences.

Background

Autonomous agents rely on machine vision for sensing a surrounding environment by analyzing areas of interest in images of the surrounding environment. Although scientists have spent decades studying the human visual system, a solution for realizing equivalent machine vision remains elusive. Realizing equivalent machine vision is a goal for enabling truly autonomous agents. Machine vision is distinct from the field of digital image processing because of the desire to recover a three-dimensional (3D) structure of the world from images and using the 3D structure for fully understanding a scene. That is, machine vision strives to provide a high-level understanding of a surrounding environment, as performed by the human visual system.

Autonomous agents may rely on a trained convolutional neural network (CNN) to identify objects within areas of interest in an image of a surrounding scene of the autonomous agent. For example, a CNN may be trained to identify and track objects captured by sensors, such as light detection and ranging (LIDAR) sensors, sonar sensors, red-green-blue (RGB) cameras, RGB-depth (RGB-D) cameras, and the like. The sensors may be in communication with a device, such as an autonomous vehicle for collecting unlabeled 3D data.

Although this unlabeled 3D data is easy to collect, state-of-the-art machine learning techniques for 3D object detection still rely on difficult-to-obtain manual annotations. To reduce this dependence on the expensive and error-prone process of manual labeling, a technique for representation learning from unlabeled LIDAR point cloud sequences is desired.

SUMMARY

A method of representation learning for object detection from unlabeled point cloud sequences is described. The method includes detecting moving object traces from temporally-ordered, unlabeled point cloud sequences. The method also includes extracting a set of moving objects based on the moving object traces detected from the sequence of temporally-ordered, unlabeled point cloud sequences. The method further includes classifying the set of moving objects extracted from on the moving object traces detected from the sequence of temporally-ordered, unlabeled point cloud sequences. The method also includes estimating 3D bounding boxes for the set of moving objects based on the classifying of the set of moving objects.

A non-transitory computer-readable medium having program code recorded thereon for representation learning and object detection from unlabeled point cloud sequences is described. The program code being executed by a processor. The non-transitory computer-readable medium includes program code to detect moving object traces from temporally-ordered, unlabeled point cloud sequences. The non-transitory computer-readable medium also includes program code to extract a set of moving objects based on the moving object traces detected from the sequence of temporally-ordered, unlabeled point cloud sequences. The non-transitory computer-readable medium further includes program code to classify the set of moving objects extracted from on the moving object traces detected from the sequence of temporally-ordered, unlabeled point cloud sequences. The non-transitory computer-readable medium also includes program code to estimate 3D bounding boxes for the set of moving objects based on the classifying of the set of moving objects.

A system of representation learning for object detection from unlabeled point cloud sequences is described. The system includes a moving object trace detection module to detect moving object traces from temporally-ordered, unlabeled point cloud sequences. The system also includes a moving object extraction module to extract a set of moving objects based on the moving object traces detected from the sequence of temporally-ordered, unlabeled point cloud sequences. The system further includes an object classification and labeling module to classify the set of moving objects extracted from on the moving object traces detected from the sequence of temporally-ordered, unlabeled point cloud sequences. The system also includes a bounding box estimation module to estimate 3D bounding boxes for the set of moving objects based on the classifying of the set of moving objects.

DETAILED DESCRIPTION

Based on the teachings, one skilled in the art should appreciate that the scope of the present disclosure is intended to cover any aspect of the present disclosure, whether implemented independently of or combined with any other aspect of the present disclosure. For example, an apparatus may be implemented or a method may be practiced using any number of the aspects set forth. In addition, the scope of the present disclosure is intended to cover such an apparatus or method practiced using other structure, functionality, or structure and functionality in addition to, or other than the various aspects of the present disclosure set forth. It should be understood that any aspect of the present disclosure disclosed may be embodied by one or more elements of a claim.

Autonomous agents may rely on a trained convolutional neural network (CNN) to identify objects within areas of interest in an image of a surrounding scene of the autonomous agent. For example, a CNN may be trained to identify and track objects captured by sensors, such as light detection and ranging (LIDAR) sensors, sonar sensors, red-green-blue (RGB) cameras, RGB-depth (RGB-D) cameras, and the like. The sensors may be in communication with a device, such as an autonomous vehicle for collecting unlabeled 3D data from which to perform object detection.

Among the modalities used for object detection during autonomous driving, LIDAR point clouds capture an accurate 3D scene structure, yielding state-of-the-art performance. Unfortunately, sparsity and irregularity of LIDAR point clouds may prohibit models from generalizing to complicated real-world environments. Moreover, successful object detection involves jointly solving several tasks, including foreground-background segmentation, instance segmentation, object localization, and classification. This results in a high demand for human labels of object locations, velocities, orientations, and other properties within unlabeled 3D data. That is, although unlabeled 3D data is trivial to collect, state-of-the-art machine learning techniques for 3D object detection rely on difficult-to-obtain manual annotations.

As opposed to highly expensive human-annotated labels, autonomous vehicles equipped with LIDAR sensors can readily collect unlabeled point cloud sequences whenever they are on the road. These temporally-ordered sequences contain more information than single-frame point clouds. Some aspects of the present disclosure are directed to representation learning from unlabeled LIDAR point cloud sequences. These aspects of the present disclosure recognize that moving objects are reliably detected from point cloud sequences without involving human-labeled 3D bounding boxes. For example, a set of moving objects from a single LIDAR frame extracted from a point cloud sequence provides sufficient supervision for single-frame object detection. These aspects of the present disclosure design appropriate pretext tasks to learn point cloud features that generalize to both moving and static unseen objects. These features are applied to object detection, which achieves strong performance on self-supervised representation learning and unsupervised object detection tasks.

Some aspects of the present disclosure are directed to a representation learning approach for learning features and object detection from unlabeled LIDAR point cloud sequences without 3D bounding box annotations. These aspects of the present disclosure provide generalization from limited labeled data that are combined with various geometry processing techniques to derive a pseudo-label generator with relatively few parameters. In operation, the pseudo-label generator ingests unlabeled point cloud sequences and produces annotations valuable for pretext tasks like motion segmentation and moving object detection. In some aspects of the present disclosure, the generated annotations are used to pre-train a single-frame feature extractor that is subsequently used for downstream tasks such as object detection. Beneficially, representation learning from unlabeled LIDAR point cloud sequences reduces dependence on the expensive and error-prone process of manual labeling.

FIG.1illustrates an example implementation of the aforementioned system and method for representation learning and object detection from unlabeled point cloud sequences using a system-on-a-chip (SOC)100of an ego vehicle150. The SOC100may include a single processor or multi-core processors (e.g., a central processing unit (CPU)102), in accordance with certain aspects of the present disclosure. Variables (e.g., neural signals and synaptic weights), system parameters associated with a computational device (e.g., neural network with weights), delays, frequency bin information, and task information may be stored in a memory block. The memory block may be associated with a neural processing unit (NPU)108, a CPU102, a graphics processing unit (GPU)104, a digital signal processor (DSP)106, a dedicated memory block118, or may be distributed across multiple blocks. Instructions executed at a processor (e.g., CPU102) may be loaded from a program memory associated with the CPU102or may be loaded from the dedicated memory block118.

The SOC100may also include additional processing blocks configured to perform specific functions, such as the GPU104, the DSP106, and a connectivity block110, which may include fourth generation long term evolution (4G LTE) connectivity, unlicensed Wi-Fi connectivity, USB connectivity, Bluetooth® connectivity, and the like. In addition, a multimedia processor112in combination with a display130may, for example, classify and categorize semantic keypoints of objects in an area of interest, according to the display130illustrating a view of a vehicle. In some aspects, the NPU108may be implemented in the CPU102, DSP106, and/or GPU104. The SOC100may further include sensors114, image signal processors (ISPs)116, and/or navigation120, which may, for instance, include a global positioning system (GPS).

The SOC100may be based on an Advanced Risk Machine (ARM) instruction set or the like. In another aspect of the present disclosure, the SOC100may be a server computer in communication with the ego vehicle150. In this arrangement, the ego vehicle150may include a processor and other features of the SOC100.

In this aspect of the present disclosure, instructions loaded into a processor (e.g., CPU102) or the NPU108of the ego vehicle150may include code to perform representation learning for object detection from unlabeled point cloud sequences captured by the sensors114(e.g., a LIDAR sensor/camera). The instructions loaded into the NPU108may also include code to detect moving object traces from temporally-ordered, unlabeled point cloud sequences captured by the sensors114. The instructions loaded into the NPU108may also include code to extract a set of moving objects based on the moving object traces detected from the sequence of temporally-ordered, unlabeled point cloud sequences. The instructions loaded into the NPU108may also include code to classify the set of moving objects extracted from on the moving object traces detected from the sequence of temporally-ordered, unlabeled point cloud sequences. The instructions loaded into the NPU108may further include code to estimate 3D bounding boxes for the set of moving objects based on the classifying of the set of moving objects.

FIG.2is a block diagram illustrating a software architecture200that may modularize functions for representation learning and object detection from unlabeled point cloud sequences, according to aspects of the present disclosure. Using the architecture, a planner/controller application202is designed to cause various processing blocks of a system-on-a-chip (SOC)220(for example a CPU222, a DSP224, a GPU226, and/or an NPU228) to perform supporting computations during run-time operation of the planner/controller application202.

The planner/controller application202may be configured to call functions defined in a user space204that may, for example, provide for representation learning and object detection from unlabeled point cloud sequences in frames captured by a LIDAR camera of an ego vehicle. The planner/controller application202may make a request to compile program code associated with a library defined in a moving object extraction application programming interface (API)206for detection and extraction of moving objects from unlabeled point cloud sequences, which enables self-supervised representation learning from point cloud data. The planner/controller application202may make a request to compile program code associated with a library defined in a feature extraction module API207for the task of extracting a feature vector from unlabeled point cloud sequences of frames captured by a LIDAR camera of an autonomous agent. The planner/controller application202may configure a vehicle control action by planning a trajectory of the ego vehicle according to objects within a scene surrounding the ego vehicle detected from the feature vectors.

A run-time engine208, which may be compiled code of a runtime framework, may be further accessible to the planner/controller application202. The planner/controller application202may cause the run-time engine208, for example, to perform tracking of moving objects in subsequent point cloud sequences of a LIDAR camera stream. When an object is detected within a predetermined distance of the ego vehicle, the run-time engine208may in turn send a signal to an operating system210, such as a Linux Kernel212, running on the SOC220. The operating system210, in turn, may cause a computation to be performed on the CPU222, the DSP224, the GPU226, the NPU228, or some combination thereof. The CPU222may be accessed directly by the operating system210, and other processing blocks may be accessed through a driver, such as drivers214-218for the DSP224, for the GPU226, or for the NPU228. In the illustrated example, the deep neural network may be configured to run on a combination of processing blocks, such as the CPU222and the GPU226, or may be run on the NPU228, if present.

FIG.3is a diagram illustrating an example of a hardware implementation for a representation learning and object detection system300for 3D bounding box estimation from unlabeled point cloud sequences, according to aspects of the present disclosure. The representation learning and object detection system300may be configured for planning and control of an ego vehicle in response to detected objects in point cloud sequences from a LIDAR camera during operation of a car350. The representation learning and object detection system300may be a component of a vehicle, a robotic device, or other device. For example, as shown inFIG.3, the representation learning and object detection system300is a component of the car350. Aspects of the present disclosure are not limited to the representation learning and object detection system300being a component of the car350, as other devices, such as a bus, motorcycle, or other like vehicle, are also contemplated for using the representation learning and object detection system300. The car350may be autonomous or semi-autonomous.

The representation learning and object detection system300may be implemented with an interconnected architecture, represented generally by an interconnect308. The interconnect308may include any number of point-to-point interconnects, buses, and/or bridges depending on the specific application of the representation learning and object detection system300and the overall design constraints of the car350. The interconnect308links together various circuits including one or more processors and/or hardware modules, represented by a sensor module302, an ego perception module310, a processor320, a computer-readable medium322, communication module324, a locomotion module326, a location module328, a planner module330, and a controller module340. The interconnect308may also link various other circuits such as timing sources, peripherals, voltage regulators, and power management circuits, which are well known in the art, and therefore, will not be described any further.

The representation learning and object detection system300includes a transceiver332coupled to the sensor module302, the ego perception module310, the processor320, the computer-readable medium322, the communication module324, the locomotion module326, the location module328, a planner module330, and the controller module340. The transceiver332is coupled to an antenna334. The transceiver332communicates with various other devices over a transmission medium. For example, the transceiver332may receive commands via transmissions from a user or a remote device. As discussed herein, the user may be in a location that is remote from the location of the car350. As another example, the transceiver332may transmit the pseudo-labeled point cloud sequences and/or planned actions from the ego perception module310to a server (not shown).

The representation learning and object detection system300includes the processor320coupled to the computer-readable medium322. The processor320performs processing, including the execution of software stored on the computer-readable medium322to provide representation learning and object detection functionality based on unlabeled point cloud sequences, according to aspects of the present disclosure. The software, when executed by the processor320, causes the representation learning and object detection system300to perform the various functions described for ego vehicle perception based on object detection from pseudo labeled point cloud sequences captured by a LIDAR camera of an ego vehicle, such as the car350, or any of the modules (e.g.,302,310,324,326,328,330, and/or340). The computer-readable medium322may also be used for storing data that is manipulated by the processor320when executing the software.

The sensor module302may obtain images via different sensors, such as a first sensor304and a second sensor306. The first sensor304may be a vision sensor (e.g., a stereoscopic camera or a red-green-blue (RGB) camera) for capturing 2D RGB images. The second sensor306may be a ranging sensor, such as a light detection and ranging (LIDAR) sensor or a radio detection and ranging (RADAR) sensor. Of course, aspects of the present disclosure are not limited to the aforementioned sensors, as other types of sensors (e.g., thermal, sonar, and/or lasers) are also contemplated for either of the first sensor304or the second sensor306.

The images of the first sensor304and/or the second sensor306may be processed by the processor320, the sensor module302, the ego perception module310, the communication module324, the locomotion module326, the location module328, and the controller module340. In conjunction with the computer-readable medium322, the images from the first sensor304and/or the second sensor306are processed to implement the functionality described herein. In one configuration, detected 3D object information captured by the first sensor304and/or the second sensor306may be transmitted via the transceiver332. The first sensor304and the second sensor306may be coupled to the car350or may be in communication with the car350.

The location module328may determine a location of the car350. For example, the location module328may use a global positioning system (GPS) to determine the location of the car350. The location module328may implement a dedicated short-range communication (DSRC)-compliant GPS unit. A DSRC-compliant GPS unit includes hardware and software to make the car350and/or the location module328compliant with one or more of the following DSRC standards, including any derivative or fork thereof: EN 12253:2004 Dedicated Short-Range Communication—Physical layer using microwave at 5.9 GHz (review); EN 12795:2002 Dedicated Short-Range Communication (DSRC)—DSRC Data link layer: Medium Access and Logical Link Control (review); EN 12834:2002 Dedicated Short-Range Communication—Application layer (review); EN 13372:2004 Dedicated Short-Range Communication (DSRC)—DSRC profiles for RTTT applications (review); and EN ISO 14906:2004 Electronic Fee Collection—Application interface.

A DSRC-compliant GPS unit within the location module328is operable to provide GPS data describing the location of the car350with space-level accuracy for accurately directing the car350to a desired location. For example, the car350is driving to a predetermined location and desires partial sensor data. Space-level accuracy means the location of the car350is described by the GPS data sufficient to confirm a location of the parking space of the car350. That is, the location of the car350is accurately determined with space-level accuracy based on the GPS data from the car350.

The communication module324may facilitate communications via the transceiver332. For example, the communication module324may be configured to provide communication capabilities via different wireless protocols, such as Wi-Fi, 5G new radio (NR), long term evolution (LTE), 3G, etc. The communication module324may also communicate with other components of the car350that are not modules of the representation learning and object detection system300. The transceiver332may be a communications channel through a network access point360. The communications channel may include DSRC, LTE, LTE-D2D, mmWave, Wi-Fi (infrastructure mode), Wi-Fi (ad-hoc mode), visible light communication, TV white space communication, satellite communication, full-duplex wireless communications, or any other wireless communications protocol such as those mentioned herein.

In some configurations, the network access point360includes Bluetooth® communication networks or a cellular communications network for sending and receiving data, including via short messaging service (SMS), multimedia messaging service (MMS), hypertext transfer protocol (HTTP), direct data connection, wireless application protocol (WAP), e-mail, DSRC, full-duplex wireless communications, mmWave, Wi-Fi (infrastructure mode), Wi-Fi (ad-hoc mode), visible light communication, TV white space communication, and satellite communication. The network access point360may also include a mobile data network that may include third generation (3G), fourth generation (4G), fifth generation (5G), long term evolution (LTE), LTE-vehicle-to-everything (V2X), LTE-driver-to-driver (D2D), Voice over LTE (VoLTE), or any other mobile data network or combination of mobile data networks. Further, the network access point360may include one or more IEEE 802.11 wireless networks.

The representation learning and object detection system300also includes the planner module330for planning a selected route/action (e.g., collision avoidance) of the car350and the controller module340to control the locomotion of the car350. The controller module340may perform the selected action via the locomotion module326for autonomous operation of the car350along, for example, a selected route. In one configuration, the planner module330and the controller module340may collectively override a user input when the user input is expected (e.g., predicted) to cause a collision according to an autonomous level of the car350. The modules may be software modules running in the processor320, resident/stored in the computer-readable medium322, and/or hardware modules coupled to the processor320, or some combination thereof.

The National Highway Traffic Safety Administration (NHTSA) has defined different “levels” of autonomous vehicles (e.g., Level 0, Level 1, Level 2, Level 3, Level 4, and Level 5). For example, if an autonomous vehicle has a higher level number than another autonomous vehicle (e.g., Level 3 is a higher level number than Levels 2 or 1), then the autonomous vehicle with a higher level number offers a greater combination and quantity of autonomous features relative to the vehicle with the lower level number. These different levels of autonomous vehicles are described briefly below.

Level 0: In a Level 0 vehicle, the set of advanced driver assistance system (ADAS) features installed in a vehicle provide no vehicle control, but may issue warnings to the driver of the vehicle. A vehicle which is Level 0 is not an autonomous or semi-autonomous vehicle.

Level 1: In a Level 1 vehicle, the driver is ready to take driving control of the autonomous vehicle at any time. The set of ADAS features installed in the autonomous vehicle may provide autonomous features such as: adaptive cruise control (ACC); parking assistance with automated steering; and lane keeping assistance (LKA) type II, in any combination.

Level 2: In a Level 2 vehicle, the driver is obliged to detect objects and events in the roadway environment and respond if the set of ADAS features installed in the autonomous vehicle fail to respond properly (based on the driver's subjective judgement). The set of ADAS features installed in the autonomous vehicle may include accelerating, braking, and steering. In a Level 2 vehicle, the set of ADAS features installed in the autonomous vehicle can deactivate immediately upon takeover by the driver.

Level 3: In a Level 3 ADAS vehicle, within known, limited environments (such as freeways), the driver can safely turn their attention away from driving tasks, but must still be prepared to take control of the autonomous vehicle when needed.

Level 4: In a Level 4 vehicle, the set of ADAS features installed in the autonomous vehicle can control the autonomous vehicle in all but a few environments, such as severe weather. The driver of the Level 4 vehicle enables the automated system (which is comprised of the set of ADAS features installed in the vehicle) only when it is safe to do so. When the automated Level 4 vehicle is enabled, driver attention is not required for the autonomous vehicle to operate safely and consistent within accepted norms.

Level 5: In a Level 5 vehicle, other than setting the destination and starting the system, no human intervention is involved. The automated system can drive to any location where it is legal to drive and make its own decision (which may vary based on the jurisdiction where the vehicle is located).

A highly autonomous vehicle (HAV) is an autonomous vehicle that is Level 3 or higher. Accordingly, in some configurations the car 350 is one of the following: a Level 0 non-autonomous vehicle; a Level 1 autonomous vehicle; a Level 2 autonomous vehicle; a Level 3 autonomous vehicle; a Level 4 autonomous vehicle; a Level 5 autonomous vehicle; and an HAV.

The ego perception module310may be in communication with the sensor module302, the processor320, the computer-readable medium322, the communication module324, the locomotion module326, the location module328, the planner module330, the transceiver332, and the controller module340. In one configuration, the ego perception module310receives sensor data from the sensor module302. The sensor module302may receive the sensor data from the first sensor304and the second sensor306. According to aspects of the present disclosure, the ego perception module310may receive sensor data directly from the first sensor304or the second sensor306to perform monocular ego-motion estimation from images captured by the first sensor304or the second sensor306of the car350.

Among the modalities used for object detection during autonomous driving, LIDAR point clouds capture an accurate 3D scene structure, yielding state-of-the-art performance. Unfortunately, sparsity and irregularity of LIDAR point clouds may prohibit models from generalizing to complicated real-world environments. Moreover, successful object detection involves jointly solving several tasks, including foreground-background segmentation, instance segmentation, object localization, and classification. This results in a high demand for human labels of object locations, velocities, orientations, and other properties within unlabeled 3D data. That is, although unlabeled 3D data is trivial to collect, state-of-the-art machine learning techniques for 3D object detection rely on difficult-to-obtain manual annotations.

As opposed to highly expensive human-annotated labels, autonomous vehicles equipped with LIDAR sensors, such as the first sensor304and/or the second sensor306, can readily collect unlabeled point cloud sequences while on the road. These temporally-ordered sequences generally contain more information than single-frame point clouds. Some aspects of the present disclosure are directed to representation learning from these unlabeled point cloud sequences. These aspects of the present disclosure recognize that moving objects are reliably detected from point cloud sequences without relying on human-labeled 3D bounding boxes.

In some aspects of the present disclosure, a set of moving objects of a single LIDAR frame extracted from a point cloud sequence provides sufficient supervision for training single-frame object detection. These aspects of the present disclosure design appropriate pretext tasks to learn point cloud features that generalize to both moving and static unseen objects in the point cloud sequences. These learned point cloud features are applied to object detection in the form of pseudo labels. Object detection based on the pseudo labels achieves strong performance on self-supervised representation learning and unsupervised object detection tasks.

Some aspects of the present disclosure are directed to a representation learning approach for learning features for object detection from unlabeled LIDAR point cloud sequences without 3D bounding box annotations. These aspects of the present disclosure provide generalization from limited labeled data that are combined with various geometry processing techniques to derive a pseudo-label generator with relatively few parameters. In operation, the pseudo-label generator ingests unlabeled point cloud sequences and produces annotations valuable for pretext tasks like motion segmentation and moving object detection. In some aspects of the present disclosure, the generated annotations are used to pre-train a single-frame feature extractor that is subsequently used for downstream tasks such as object detection. Beneficially, representation learning from unlabeled LIDAR point cloud sequences reduces dependence on the expensive and error-prone process of manual labeling.

As shown inFIG.3, the ego perception module310includes a moving object trace detection module312, a moving object extraction module314, an object classification and labeling module316, and a bounding box estimation module318. The moving object trace detection module312, the moving object extraction module314, the object classification and labeling module316, and the bounding box estimation module318may be components of a same or different artificial neural network. For example, the artificial neural network is a convolutional neural network (CNN) communicably coupled to a LIDAR camera. The ego perception module310receives unlabeled point cloud sequences from the first sensor304and/or the second sensor306. In one configuration, the first sensor304and the second sensor306are configured as a LIDAR camera sensor.

The ego perception module310is configured to perform 3D bounding box estimation from unlabeled point cloud sequences, according to aspects of the present disclosure. In this aspect of the present disclosure, the moving object trace detection module312is configured to detect moving object traces from temporally-ordered, unlabeled point cloud sequences captured by the first sensor304and/or the second sensor306. In response, the moving object extraction module314is configured to extract a set of moving objects based on the moving object traces detected from the sequence of temporally-ordered, unlabeled point cloud sequences from the sensor module302. Next, the object classification and labeling module316is configured to classify the set of moving objects extracted from on the moving object traces detected from the sequence of temporally-ordered, unlabeled point cloud sequences.

In some aspects of the present disclosure, the object classification and labeling module316is configured as a pseudo-label generator to provide pseudo labels to the set of moving objects based on the classification (e.g., vehicle, pedestrian, or cyclist). Based on the pseudo labels, the bounding box estimation module318is configured to estimate 3D bounding boxes for the set of moving objects based on the pseudo labels on the set of moving objects. The representation learning and object detection system300may be configured for planning and control of an ego vehicle based on detected objects according to 3D bounding boxes estimated from pseudo labels of point cloud sequences from LIDAR camera sensors during operation of an ego vehicle, for example, as shown inFIG.4.

FIG.4illustrates an example of an ego vehicle450(e.g., the car350) in an environment400, according to aspects of the present disclosure. As shown inFIG.4, the ego vehicle450is traveling on a road410. A first vehicle404(e.g., other agent) may be ahead of the ego vehicle450, and a second vehicle416may be adjacent to the ego vehicle450. In this example, the ego vehicle450may include a 2D camera456, such as a 2D red-green-blue (RGB) camera, and a light detection and ranging (LIDAR) camera458. Alternatively, the LIDAR camera458may be another RGB camera or another type of sensor, such as ultrasound, and/or a radio detection and ranging (RADAR) sensor, as shown by reference number462. Additionally, or alternatively, the ego vehicle450may include one or more additional sensors. For example, the additional sensors may be side facing and/or rear facing sensors.

In one configuration, the 2D camera456captures a 2D image that includes objects in the field of view460of the 2D camera456. The LIDAR camera458may generate LIDAR point cloud sequences. The LIDAR point cloud sequences captured by the LIDAR camera458may include a 3D point cloud of the first vehicle404, as the first vehicle404is in the field of view470of the LIDAR camera458. A field of view460of the 2D camera456is also shown.

The information obtained from the 2D camera456and the LIDAR camera458may be used to navigate the ego vehicle450along a route when the ego vehicle450is in an autonomous mode. The 2D camera456and the LIDAR camera458may be powered from electricity provided from the battery (not shown) of the ego vehicle450. The battery may also power the motor of the ego vehicle450. The information obtained from the LIDAR camera458may be used to estimate bounding boxes using self-supervised learning based on detected object traces within LIDAR point clouds.

Among the modalities used for object detection during autonomous driving of the ego vehicle, the LIDAR point clouds from the LIDAR sensor capture an accurate 3D scene structure surrounding the ego vehicle. Unfortunately, sparsity and irregularity of LIDAR point clouds may prohibit models from generalizing to complicated real-world environments. Moreover, successful object detection involves jointly solving several tasks, including foreground-background segmentation, instance segmentation, object localization, and classification. This results in a high demand for human labels of object locations, velocities, orientations, and other properties within unlabeled 3D data. That is, although unlabeled 3D data is trivial to collect, state-of-the-art machine learning techniques for 3D object detection rely on difficult-to-obtain manual annotations.

As opposed to highly expensive human-annotated labels, the ego vehicle450, using the LIDAR camera458, can readily collect unlabeled point cloud sequences while traveling on the road410. Some aspects of the present disclosure are directed to representation learning from unlabeled LIDAR point cloud sequences captured by the LIDAR camera458of the ego vehicle450. These aspects of the present disclosure recognize that moving objects are reliably detected from point cloud sequences without relying on human-labeled 3D bounding boxes. For example, object traces extracted from input point cloud sequences provide sufficient supervision for single-frame object detection. These aspects of the present disclosure learn point cloud features that generalize to both moving and static unseen objects using supervision based on a set of moving objects from a single LIDAR frame, for example, as shown inFIG.5.

FIG.5is a diagram500of an object trace510detected from temporally-ordered, unlabeled point cloud sequences, according to aspects of the present disclosure. As described, an object trace is a sequence of point clusters that move along smooth trajectories. As described, an object trace may be classified as moving or non-moving, which the object trace may be referred to as a moving object trace. In this example, the object trace510is a moving vehicle. In some aspects of the present disclosure, object traces may be classified and labeled to enable bounding box estimation.

In some aspects of the present disclosure a pseudo-label generator identifies moving objects represented as sequences of point clusters across frames, such as the object trace510shown inFIG.5. These aspects of the present disclosure are directed to single-frame LIDAR point cloud segmentation methods that estimate motion and planar structures from each point cloud sequence. This information enables reliable separation of moving objects, resulting in a highly-accurate moving object detection technique. By combining all detected point clusters in the sequence, the location, orientation, and category of each moving object instance are estimated, enabling pretext tasks like moving object detection. To promote generalization and mitigate the bias of working exclusively with moving objects, estimated object labels are propagated to static objects. This propagation checks the consistency of predicted object labels across adjacent frames to prune incorrect predictions, which further improves the robustness of the label propagation to enable a self-supervised learning framework, for example, as shown inFIGS.6A-6E.

FIGS.6A-6Eare diagrams illustrating an overview of a self-supervised learning framework, in which moving objects detected from input point cloud sequences are used to train self-supervised tasks for feature extraction, according to aspects of the present disclosure.FIG.6Aillustrates an input point cloud sequence600, which may be captured using the LIDAR camera458of the ego vehicle450, as shown inFIG.4.FIGS.6A-6Eillustrate a process for representation learning for object detection from unlabeled point cloud sequences performed by a self-supervised learning framework, according to aspects of the present disclosure.

In some aspects of the present disclosure, the self-supervised learning framework shown inFIGS.6A-6Eoperates by extracting a set of moving objects (e.g., defined below as detected object traces) from the sequence of temporally-ordered and unlabeled, input point cloud sequence600, and training a feature extraction module via self-supervised tasks using the detected object traces. It should be recognized that the process of representation learning for object detection from unlabeled point cloud sequences is performed by a self-supervised learning framework that does not rely on human-labeled 3D bounding boxes at any stage of training.

FIG.6Billustrates a first step602of the process of representation learning for object detection from unlabeled point cloud sequences, in which moving object traces are detected from the input point cloud sequence600, according to aspects of the present disclosure. In this example, a first moving object trace610, a second moving object trace620, and a third moving object trace630are detected from the input point cloud sequence600.

In these aspects of the present disclosure, detection of moving object traces is enabled by point cloud semantic segmentation. As described, point cloud semantic segmentation involves finding instances of objects, represented by sets of point cluster {Ci} where Ci∈Mi×3. In point cloud sequence data, object instances appear consistently in consecutive frames. Therefore, aspects of the present disclosure are directed to detecting object traces, which are described as sequences of point clusters across consecutive frames corresponding to the same object.

Formally, given a point cloud sequence={P1, . . . , PN} where Ptis the t-th frame from a LIDAR camera (e.g., the LIDAR camera458), an object trace is a temporally-ordered sequence of clusters={C1, . . . , Cr}, where Ct⊆Ptfor any 1≤l≤t≤r≤N. For example,FIG.5illustrates an object trace510detected from temporally-ordered, unlabeled point cloud sequences. The object trace510is a sequence of point clusters that move along smooth trajectories, which may be classified as moving or non-moving. In this example, the object trace510is a moving vehicle. Classification of the object trace510may provide a pseudo label of the object trace to enable bounding box estimation.

Aspects of the present disclosure recognize that many object classes relevant for autonomous driving are dynamic, including pedestrians, vehicles, and cyclists. Statistical analysis on the popular datasets shows that a large fraction of ground truth object traces follow smooth moving trajectories. In particular, the length and motion consistency of object traces reduce uncertainty in their detection, enabling a robust, non-learning object trace detection process for unlabeled point cloud sequences. In some aspects of the present disclosure, detected object traces are subsequently used to learn pseudo tasks in later stages.

As shown inFIG.6B, detecting the object traces (e.g., the first moving object trace610, the second moving object trace620, and the third moving object trace630) is inevitably disturbed by a number of factors, such as the interaction between objects and environment, the irregularity of motion, and different sampling density across frames. Even supervised methods suffer from such detection uncertainty. Aspects of the present disclosure focus on the object traces that move along smooth trajectories. Therefore, an object detector, such as a single-frame object detection model670shown inFIG.6E, optimizes for detection precision instead of coverage. This choice may add a mild bias to the detected objects. For example, object velocities are recognized as independent from geometric appearance in optimizing for detection precision.

Some aspects of the present disclosure detect object traces by following a standard proposal-and-rejection framework. For each point cloud sequence, a candidate set of clusters corresponding to movable objects is proposed. Then, a Kalman filter is applied to acquire object traces according to their motion. Next, a subset of object traces with smooth trajectories is collected.

FIG.6Cillustrates a second step640for the process of representation learning for object detection from unlabeled point cloud sequences, in which detected moving object traces are used as training data, according to aspects of the present disclosure. Given a set of high-quality detected object traces (e.g., the first moving object trace610, the second moving object trace620, and the third moving object trace630) extracted from unlabeled, input point cloud sequence600, self-supervised tasks are designed for enabling representation learning of point cloud models.

As shown inFIG.6C, a first pretext task trains a single-frame semantic instance segmentation model650to identify moving objects, according to aspects of the present disclosure. In these aspects of the present disclosure, ground truth may be computed by propagating detected object traces back into each frame. Nevertheless, training of the single-frame semantic instance segmentation model650does not use all information in the object traces, such as orientations inferred from objects' directions of motion, which provide a motivation for selecting more complex pretext tasks.

FIG.6Dillustrates a third step642for the process of representation learning for object detection from unlabeled point cloud sequences, in which detected moving object traces are classified and bounding box inference is performed, according to aspects of the present disclosure. Aspects of the present disclosure recognize that many object classes relevant for autonomous driving are dynamic, including pedestrians, vehicles, and cyclists. In this example, the first moving object trace610is classified as a pedestrian, while the second moving object trace620, and the third moving object trace630are classified as moving vehicles.

In this aspect of the present disclosure, first bounding boxes612are estimated from the first moving object trace610based on the classification of the first moving object trace610as a pedestrian. In addition, second bounding boxes622are estimated from the second moving object trace620based on the classification of the second moving object trace620as a moving vehicle. Similarly, third bounding boxes632are estimated from the third moving object trace630based on the classification of the third moving object trace630as a moving vehicle. These bounding boxes may provide pseudo labels for training object detection, for example, as shown inFIG.6E.

FIG.6Eillustrates a fourth step660for the process of representation learning for object detection from unlabeled point cloud sequences, in which estimated bounding boxes provide pseudo labels as training data for a single-frame object detection model670, according to aspects of the present disclosure. These aspects of the present disclosure rely on object trace classification and bounding box inference to perform a second pretext task for training the single-frame object detection model670to detect 3D bounding boxes of objects in single-frame point clouds. Many attributes of the ground truth 3D bounding boxes can be inferred from detected object traces. Some aspects of the present disclosure perform self-supervision on these inferred attributes to boost performance of the representation learning process for object detection from unlabeled point cloud sequences.

FIGS.7A-7Eillustrate an overview of an input point cloud sequence during various stages of an object trace detection process, according to aspects of the present disclosure. In some aspects of the present disclosure, the object trace detection process shown inFIGS.7A-7Einvolves three steps: (1) preprocessing, (2) object cluster proposal, and (3) object trace tracking. Some aspects of the present disclosure apply two preprocessing steps on each point cloud sequence input before passing it to a detection algorithm.FIG.7Aillustrates an input point cloud sequence700during an input point cloud stage of the object trace detection process, according to aspects of the present disclosure. As shown inFIG.7A, a first preprocessing step of the object trace detection process brings all frames into a world coordinate system using an ego-motion provided, for example, as shown inFIGS.3and4.

FIG.7Billustrates the input point cloud sequence700ofFIG.7Aduring a ground removal stage of the object trace detection process, according to aspects of the present disclosure. As shown inFIG.7B, a second preprocessing step removes points on the ground (e.g., ground points) of the input point cloud sequence700ofFIG.7A, which typically account for 50% to 90% of the total number of points for improving detection efficiency and robustness. In this example, the entire point cloud sequence instead of a single-frame point cloud is used to reconstruct the ground plane.

In some aspects of the present disclosure, removing points on the ground involves observing that the ground plane is a smooth surface that approximately aligns with the x-y plane. These aspects of the present disclosure solve an optimization problem to estimate the ground height hiat a set of 2D grid cells with centers (xi, yi) on the x-y plane; this implementation uses a 1000×1000 grid. In this example, the height of the j-th input point is denoted as zj, the set of points in grid cell i as Gi, and the neighboring cells of cell i as. The optimization problem is shown in Equation (1)

This example uses a piecewise-linear first term to tolerate noisy inputs. In addition Equation (1) is solved using gradient descent, with λ=0.1 and stopping condition ∥Δh∥2<0.1 m. Then, point j ∈ Giis removed if zj<hi+δ; this experiment uses δ=0.5 m. An example of a resulting point cloud710(e.g., a ground removed point cloud visualization) after ground plane removal is shown inFIG.7B.

FIG.7Cillustrates the resulting point cloud710ofFIG.7Bduring an object cluster proposal stage of the object trace detection process, according to aspects of the present disclosure. Because object sizes may vary significantly across classes, aspects of the present disclosure rely on point cloud segmentation to extract point clusters that correspond to object instances. In this LIDAR point cloud segmentation method, a subset of point cloud segments are extracted that correspond to moving object instances.

Given a point cloud sequence, motion is used as a cue for segmentation. In this example, the velocity vi∈3of each point piis estimated and a pairwise proximity score sijis computed according to Equation (2):

where σp=0.5 m and σv=0.5 m/s. Next normalized cuts are run with edge weights {sij} and clusters are selected with average velocity >0.1 m/s.

To compute motion estimates for providing object cluster proposals720, one of two methods is applied to the resulting point cloud710. A first method accommodates point cloud sequences by optimizing for a spatiotemporal velocity field fθ:4→3mapping any point location x ∈3together with time t to a velocity v ∈3. In this example, (x, t) is normalized into [0,1]4and network parameters θ are optimized over the chamfer distance between adjacent point cloud frames. A second method estimates scene flow between pairs of adjacent frames. In this second method, a model is trained via self-supervised learning. The pre-trained model may be applied to each pair of adjacent frames in the sequence.

FIG.7Dillustrates the object cluster proposals720ofFIG.7Cthat serve as input to a trace detection stage of the object trace detection process, according to aspects of the present disclosure. Assuming each point cluster is rigidly transformed across different frames, a multi-object tracking process is adopted that uses Kalman filtering to track the motion of each of the object cluster proposals720ofFIG.7C. In this example, a Kalman filter (KF) is used to estimate the velocity of each object's center from a sequence of cluster centers. This object trace tracking process begins with the geometric center of the object cluster proposals720ofFIG.7Cand zero velocity, and iteratively registers the object cluster to the adjacent frames according to the object trace tracking process shown in Table 1. In this aspect of the present disclosure, Table 1 illustrates pseudo code for the object trace tracking stage of the object trace detection process, according to aspects of the present disclosure. As shown in Table 1, two stopping conditions are used for trace tracking to reject large average registration errors and large acceleration magnitudes, with thresholds σ0=1.0 m and σ1=3.0 m/s2. As shown inFIG.7C, the object cluster proposals720are highlighted according to a shading pattern.

FIG.7Dillustrates detected object traces750following the trace detection step of the object trace detection process, according to aspects of the present disclosure. As shown inFIG.7D, each of the detected object traces750is identified using a different shading. For each of the object cluster proposals720ofFIG.7C, the output of the object trace tracking process is a sequence of rigid transformations that transforms the cluster across frames. In some aspects of the present disclosure, points that are geometrically close to any of the transformed object cluster points are collected (e.g., within a distance r=0.3 m). If the points of two object traces overlap by at least a 10% intersection over union (IoU), the one with the smaller number of points is rejected.

FIG.7Eillustrates ground truth objects, according to aspects of the present disclosure. As shown inFIG.7E, the ground truth object points770are visualized in these aspects of the present disclosure. In this example, a vehicle780is shaded in a first pattern, and a pedestrian790is shaded in a second pattern. According to aspects of the present disclosure, the detected object traces750and the ground truth object points770may provide training data for classification and a pseudo label of the detected object traces750, for example, as shown inFIGS.8A-8C.

Given a set of object traces corresponding to moving object instances in LIDAR point cloud sequences, aspects of the present disclosure provide self-supervised tasks that yield point cloud feature representations for downstream tasks, such as single-frame LIDAR object detection. Some aspects of the present disclosure introduce two self-supervised tasks for training a feature extractor with a single-frame point cloud input. Aspects of the present disclosure are also directed to a deep feature extraction module.

Aspects of the present disclosure consider motion segmentation as a first self-supervised task for training a motion segmentation model, such as the single-frame semantic instance segmentation model650shown inFIG.6C. Because object traces are extracted with high precision, these object traces are used as segmentation masks to train a single-frame model (e.g., the single-frame semantic instance segmentation model650) for differentiating moving objects from the background, according to aspects of the present disclosure. Some aspects of the present disclosure train a pointwise binary classifier that classifies each feature vector as either moving or non-moving. In one configuration, the classifier is implemented as a multi-layer perceptron (MLP) that combines a rectilinear unit (ReLU) layer and a batch normalization layer in a model architecture. In this configuration a negative log-likelihood is used as a training loss.

To generalize from moving to static objects, some aspects of the present disclosure augment each scene with randomly-sampled point clusters corresponding to moving objects from other scenes. For example, for each scene and each object class in {Vehicle, Pedestrian, Cyclist}, a predetermined number (e.g., 15 for Vehicle, 10 for Pedestrian and Cyclist) of point clusters are randomly selected and placed at random locations in the scene.

FIGS.8A-8Care diagrams illustrating an overview of a bounding box inference step based on detected object traces and pseudo labels, according to aspects of the present disclosure.FIG.8Aillustrates input object traces800, which may be detected as shown inFIGS.7A-7D. This first self-supervised task is straightforward but fails to use all information in the detected object traces. For example, object orientations can be inferred from motion information but are ignored in this first self-supervised task.FIG.8Billustrates a trace classification stage810of the bounding box inference stage, according to aspects of the present disclosure.

As shown inFIG.8B, the input object traces800ofFIG.8Aare classified as a pedestrian object trace820and a vehicle object trace830.FIG.8Cillustrates a box inference stage850of the bounding box inference process, according to aspects of the present disclosure. As shown inFIG.8C, 3D bounding boxes860of the pedestrian object trace820and 3D bounding boxes870of the vehicle object trace830are estimated from the class labels directly from each of the input object traces800.

In some aspects of the present disclosure, the pseudo labels ofFIG.8Band the estimated bounding boxes ofFIG.8Care used to train a 3D object detector. Some aspects of the present disclosure use a box regression component and a box regression and classification loss to estimate the 3D bounding boxes860of the pedestrian object trace820and 3D bounding boxes870of the vehicle object trace830.

In some aspects of the present disclosure, estimating 3D bounding boxes used to train an object detector from object traces, such as the single-frame object detection model670shown inFIG.6Einvolves the following subtasks:(1) Registration: The velocity of each object cluster in each trace is estimated while enforcing smoothness. This enables approximate reconstruction of the object by incorporating geometry collected from multiple frames.(2) Trace classification: Each object trace is categorized into one of the movable object classes (e.g., vehicle, pedestrian, or cyclist). This subtask involves labeled data.(3) 3D bounding box estimation: Given estimated object class labels, 3D bounding boxes for each object class are estimated. The box size estimate is determined from densely reconstructed objects and is propagated to sparse objects in each single frame because the former provides high-confidence estimates. The end result of this procedure is a set of 3D bounding boxes that are used to train a single-frame object detector, such as the single-frame object detection model670shown inFIG.6E, according to aspects of the present disclosure.

Regarding the registration subtask, as noted above, moving objects are observed to move along smooth trajectories. To improve robustness against irregular sampling sparsity of LIDAR point clouds, this smooth trajectory property is used to help estimate a velocity νi∈3for each object cluster Ciframe i of the trace. To this end, Equation (3) is optimized as follows:

where λ is set to 1. The estimated velocities {vi} bring all object points into the same coordinate system, forming a denser point cloud of the object instance, enabling object trace classification and 3D bounding box estimation.

Regarding the trace classification subtask, because object class labels are semantic rather than geometric, some minimal supervision is involved in distinguishing object classes (e.g., car, pedestrian, cyclist), as shown inFIG.8B. This aspect of the present disclosure extracts ground truth object class labels for 10% of the object traces in the training dataset to train an object classifier that takes an object trace and outputs an object class label. An additional object class “Other” is included to deal with outliers. In some aspects of the present disclosure, an object trace classifier is configured as a point transformer, with 5 transition-down layers of dimension 32, 64, 128, 256, and 512, respectively. Each trace is represented as a 4D matrix with each row representing the location and time of a point.

Regarding the 3D bounding box estimation subtask, in LIDAR object detection, each 3D bounding box is represented by a 7-dimensional vector representing location, size, and orientation, for example, as shown inFIG.8C. Because object sizes vary among classes, aspects of the present disclosure group object traces based on the estimated object class labels. For each object class, a fraction of objects is densely captured by a LIDAR sensor while the remaining points represent portions of the objects.

Some aspects of the present disclosure learn a model that regresses the bounding box size for each object trace, assuming the bounding box size does not change for each trace. For each class, this aspect of the present disclosure selects the top 20% of object traces ranked by number of points. These aspects of the present disclosure use the velocity computed in Equation (3) to bring all point clusters into the same coordinate system and compute a 3D bounding box that covers all points with minimal volume. The resulting bounding box size vectors are used to train a model that regresses bounding box size. These aspects of the present disclosure use the model to predict bounding box sizes from all other object traces. The model architecture and input data representation of the regression model are the same as in trace classification, for example, as shown inFIGS.8B and8C.

Given the bounding box size, some aspects of the present disclosure then solve an optimization problem to estimate box location and orientation:

where birepresents box attributes, d1penalizes the difference in orientation and enforces smoothness of box locations, and d2encourages the i-th box bito cover the i-th point cluster C. Here the loss functions d1and d2are elaborated from (4). This example first unpacks the box attribute vector bi∈7as ci∈3, , si∈3and θi, representing box center, box size, and orientation, respectively. Because the box size is fixed for each trace, d1is defined as

where γ1is set to 0.1.

For the definition of d2, these aspects of the present disclosure define outward the normal vector ni,1. . . ni,6and face center ci,1, . . . ci,6for the faces of the 3D bounding box represented by bi:

where γ2is set to 0.1 to make sure that this loss function can tolerate a small fraction of outlier points in the point clusters.

As the resulting 3D bounding boxes are limited to representing moving objects in each point cloud sequence, some aspects of the present disclosure involve moving-to-static object label propagation. To promote generalization, these aspects of the present disclosure train a single-frame object detector using estimated 3D bounding boxes and apply a single-frame object detector to predict 3D bounding boxes on the same set of training data. Due to the geometric similarity between moving and static objects, the single-frame object detector generates 3D bounding boxes for static objects. In some aspects of the present disclosure, the single-frame object detector is further configured to verify the consistency of temporally-adjacent bounding boxes and reject false positives for further improving robustness. The verified 3D bounding boxes for static objects are added to the training set to train another single-frame object detector from scratch, for example, as shown inFIG.6E. This procedure may be repeated (e.g., two times), yielding a single-frame object detector that reasonably generalizes to moving and static objects.

The goal of label propagation is generating 3D bounding boxes for static objects using distilled knowledge regarding the appearance of moving objects. Consequently, some aspects of the present disclosure apply a trained, single-frame object detector to all training point clouds, for example, as shown inFIG.6E. To further enforce consistency, the bounding boxes are tracked, while scanning for bounding boxes that do not move across temporally adjacent frames. Some aspects of the present disclosure connect adjacent bounding boxes under the following conditions: (1) if the adjacent bounding boxes are overlapping with an IoU greater than 0.5; (2) the translation between box centers of the adjacent bounding boxes is not more than 0.3 m; and (3) the adjacent bounding boxes are classified as the same class. These aspects of the present disclosure collect each chain of bounding boxes that are at least a predetermined number of frames long (e.g., 10 frames), and add all collected 3D bounding boxes to the training set. A process of representation learning for object detection from unlabeled point cloud sequences is further described inFIG.9.

FIG.9is a flowchart illustrating a method of representation learning for object detection from unlabeled point cloud sequences, according to aspects of the present disclosure. The method900begins at block902, in which moving object traces are detected from temporally-ordered, unlabeled point cloud sequences. For example,FIG.5illustrates the object trace510detected from temporally-ordered, unlabeled point cloud sequences.FIG.6Billustrates the first step602of the process of representation learning for object detection from unlabeled point cloud sequences, in which moving object traces are detected from the input point cloud sequence600. In this example, the first moving object trace610, the second moving object trace620, and the third moving object trace630are detected from the input point cloud sequence600.

At block904, a set of moving objects are extracted based on the moving object traces detected from the sequence of temporally-ordered, unlabeled point cloud sequences. For example,FIG.6Cillustrates the second step640for the process of representation learning for object detection from unlabeled point cloud sequences, in which detected moving object traces are used as training data. Given a set of high-quality detected object traces (e.g., the first moving object trace610, the second moving object trace620, and the third moving object trace630) extracted from unlabeled, input point cloud sequence600, self-supervised tasks are designed for enabling representation learning of point cloud models. As shown inFIG.6C, this first pretext task trains the single-frame semantic instance segmentation model650to identify moving objects, according to aspects of the present disclosure.

At block906, the set of moving objects extracted from on the moving object traces detected from the sequence of temporally-ordered, unlabeled point cloud sequences are classified. For example,FIG.6Dillustrates the third step642for the process of representation learning for object detection from unlabeled point cloud sequences, in which detected moving object traces are classified and bounding box inference is performed, according to aspects of the present disclosure. Aspects of the present disclosure recognize that many object classes relevant for autonomous driving are dynamic, including pedestrians, vehicles, and cyclists. In this example, the first moving object trace610is classified as a pedestrian, while the second moving object trace620and the third moving object trace630are classified as moving vehicles.

At block908, 3D bounding boxes are estimated for the set of moving objects based on the classifying of the set of moving objects. For example,FIG.6Eillustrates a fourth step660for the process of representation learning for object detection from unlabeled point cloud sequences, in which estimated bounding boxes provide pseudo labels as training data for a single-frame object detection model670, according to aspects of the present disclosure. These aspects of the present disclosure rely on object trace classification and bounding box inference to perform a second pretext task for training the single-frame object detection model670to detect 3D bounding boxes of objects in single-frame point clouds. As shown inFIG.8C, 3D bounding boxes860of the pedestrian object trace820and 3D bounding boxes870of the vehicle object trace830are estimated from the class labels directly from each of the input object traces800.

The method900may also include planning a vehicle control action of the ego vehicle according to the 3D bounding boxes identifying objects within a scene surrounding the ego vehicle. For example, as shown inFIG.3, the representation learning and object detection system300may be configured for planning and control of an ego vehicle using 3D bounding boxes estimated from unlabeled point cloud sequences from frames of a LIDAR camera during operation of the ego vehicle, for example, as shown inFIG.4.

The method900may extract the set of moving objects by training a single-frame semantic instance segmentation model to differentiate between feature vectors representing the set of moving objects and the feature vectors representing a background of the temporally-ordered, unlabeled point cloud sequences. The method900may extract the set of moving objects by identifying each of the feature vectors as a moving feature vector or a non-moving feature vector, labeling the moving object traces detected from the sequence of temporally-ordered, unlabeled point cloud sequences. The method900may further include training a first model to identify moving objects in a point cloud according to the labeled moving object traces. The method900may also include inferring attributes of the bounding boxes from the labeled moving object traces. The method900may further include training a second model to detect objects in the point cloud according to the attributes of the bounding boxes inferred from the labeled moving object traces.

Some aspects of the present disclosure are directed to detecting moving objects from a point cloud sequence and using information about detected moving objects to train a single-frame model for detecting objects. For example, moving objects can be detected from a point cloud sequence. A moving object can be represented as an object trace, which may be defined as a sequence of point clusters that correspond to the same object. These aspects of the present disclosure use labels to train a first model to identify moving objects in unlabeled point cloud sequences. For example, the object traces can be used as the labels. In some aspects of the present disclosure, attributes about bounding boxes can be inferred from the object traces, and these bounding boxes can be used to train a second model to detect objects in the unlabeled point cloud sequences.

In some aspects of the present disclosure, the method900may be performed by the system-on-a-chip (SOC)100(FIG.1) or the software architecture200(FIG.2) of the ego vehicle150(FIG.1). That is, each of the elements of the method900may, for example, but without limitation, be performed by the SOC100, the software architecture200, or the processor (e.g., CPU102) and/or other components included therein of the ego vehicle150.

The various illustrative logical blocks, modules, and circuits described in connection with the present disclosure may be implemented or performed with a processor configured according to the present disclosure, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array signal (FPGA) or other programmable logic device (PLD), discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The processor may be a microprocessor, but, in the alternative, the processor may be any commercially available processor, controller, microcontroller, or state machine specially configured as described herein. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

The processor may be responsible for managing the bus and processing, including the execution of software stored on the machine-readable media. Examples of processors that may be specially configured according to the present disclosure include microprocessors, microcontrollers, DSP processors, and other circuitry that can execute software. Software shall be construed broadly to mean instructions, data, or any combination thereof, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise. Machine-readable media may include, by way of example, random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, magnetic disks, optical disks, hard drives, or any other suitable storage medium, or any combination thereof. The machine-readable media may be embodied in a computer-program product. The computer-program product may comprise packaging materials.