Patent ID: 12205004

DETAILED DESCRIPTION

Overview

Example aspects of the present disclosure are directed to systems and methods for training probabilistic object motion prediction models using non-differentiable representations of prior knowledge. As one example, object motion prediction models can be used by autonomous vehicles to probabilistically predict the future location(s) of observed objects (e.g., other vehicles, bicyclists, pedestrians, etc.). For example, such models can output a probability distribution that provides a distribution of probabilities for the future location(s) of each object at one or more future times. Aspects of the present disclosure enable these models to be trained using non-differentiable prior knowledge about motion of objects within the autonomous vehicle's environment such as, for example, prior knowledge about lane or road geometry or topology and/or traffic information such as current traffic control states (e.g., traffic light status).

In particular, roads or other transportation networks typically have well-defined geometries or topologies and well-defined traffic rules. While these aspects have been exploited in motion planning methods to produce control maneuvers for autonomous vehicles that adhere to driving norms, past work has failed to utilize these priors in perception and motion forecasting methods (e.g., techniques for predicting future location(s) of object(s) observed by an autonomous vehicles). The present disclosure provides systems and methods which enable the incorporation of these or other structured priors as or within a loss function which, for example as opposed to crafting hard rules about object behavior, allows a probabilistic object motion prediction model to handle illegal and unexpected maneuvers when those happen in the real world. Thus, aspects of the present disclosure provide improved probabilistic characterization of the possible future unrolls of a scene, which ultimately enables autonomous vehicle motion planning with improved safety and rider comfort.

Specifically, one example aspect of the present disclosure provides an example framework that leverages gradient estimation techniques such as, for example, the REINFORCE technique to incorporate non-differentiable priors over sample motion forecasts from a probabilistic model, thus training the whole distribution output by the model. The proposed framework is effective on different types of training data, including real-world self-driving datasets containing complex road topologies and multi-agent interactions. The resulting motion forecasts produced by the trained model not only exhibit a better map understanding but also result in safer motion plans from the autonomous vehicle.

More particularly, a core component of every autonomous vehicle is its ability to perceive the world (including dynamic objects) and to forecast how the future might unroll. The latter is important in order to plan a safe maneuver. In recent years there has been incredible progress in perception systems. However, many challenges still remain in providing motion forecasts that are simultaneously diverse and precise. That is, having the ability to cover all the modes of the data distribution while generating bad trajectories only very rarely.

Roads in modern cities have well defined geometries or topologies as well as traffic rules. The vast majority of actors in the scene will adhere to this structure such as, for example, driving close to the middle of their lane, respecting stop signs or obeying yielding laws. These agents will also most likely act in a socially acceptable manner, avoiding collisions with other traffic participants. Despite this fact, most perception and motion forecasting systems are trained to be as close as possible to ground truth trajectories, including existing techniques which employ symmetric loss functions that do not take this structure into account. As one example, Euclidean distance between a predicted object trajectory and a ground truth trajectory is a common choice for motion forecasting. Thus, prior approaches might treat a trajectory that deviates 10 degrees leftward from the ground truth as equal to a trajectory that devices 10 degrees rightward from the ground truth. However, if one of these trajectories observes traffic laws while the other does not, treating them equally fails to capitalize upon strong prior knowledge that most actors observe traffic laws.

Failure to incorporate prior knowledge in such fashion (e.g., by reliance upon pure Euclidean measures of distance) can cause uncomfortable rides for the autonomous vehicle. Specifically, this approach can result in a large number of false positive motion forecasts of the observed object coming into the lane of the ego-vehicle (e.g., the autonomous vehicle making and acting upon the predictions). These false positives can cause the ego-vehicle to exhibit uncomfortable sudden braking operations. Even worse, false positive predictions can cause drastic steering changes to avoid an imminent collision, potentially causing another collision as a by-product.

FIGS.1A and1Bshows example visualizations of the problems raised by naive symmetric loss functions that do not take prior knowledge about road geometry into account. In particular, inFIG.1A, an ego vehicle10is observing another vehicle12on a roadway14. For the vehicle12, trajectory16corresponds to a ground truth that is observed in fact. If a symmetric loss function is used, trajectory predictions18and20have the same L2 loss relative to the ground truth trajectory16. However, trajectory prediction18would cause a harmful event while prediction20would not. Specifically, if trajectory18is predicted then the ego vehicle10may respond by swerving out of the corresponding lane. However, if trajectory20is predicted then no issue is caused for the ego vehicle10. Thus, predictions18and20should not receive the same loss.

Similarly, inFIG.1B, an ego vehicle50is observing another vehicle52on a roadway54. For the vehicle52, trajectory56corresponds to a ground truth that is observed in fact. If a symmetric loss function is used, trajectory predictions58and60have the same L2 loss relative to the ground truth trajectory56. However, trajectory prediction58would cause a harmful event while prediction60would not. Specifically, if trajectory58is predicted, then the ego vehicle50could potentially accelerate and collide with the vehicle52. However, if trajectory60is predicted then no issue is caused for the ego vehicle50. Thus, predictions58and60should not receive the same loss.

One alternative approach to completely disregarding prior knowledge is to hard code the aforementioned intuitions into the motion forecasting model (e.g., by naively rejecting predicted object trajectories which violate traffic rules). However, this approach is not resilient to non-compliant behavior from other actors and map failures, producing possibly dangerous situations. In contrast to these existing techniques, the present disclosure leverages loss functions that encourage the perception and prediction system of the autonomous vehicle to only violate these constraints when they happen in reality.

Incorporating prior knowledge in the loss function when the perception and prediction systems are deterministic can be easily done. However, deterministic systems provide less safe outcomes, as they can be catastrophic when predicting the wrong actor intention (e.g., crossing the street vs waiting, yielding vs not).

In addition, in order to plan a safe maneuver, coverage of the possible future scenarios is required, along with information about the likelihood of each possible future such that the motion planner can choose the trajectory with the lowest expected cost. The Gaussian distribution and mixtures thereof have been widely used to represent uncertainty over spatial locations. However, training probabilistic models to match the negative log likelihood of the data encourages the model to produce distributions with high recall in order to avoid the big penalty associated with low-density areas in the distribution. As a consequence many unrealistic samples are generated, sacrificing the precision of the model.

According to an aspect of the present disclosure, systems and methods are provided which make explicit use of prior knowledge about the geometry or topology of the environment as well as the traffic rules, thereby providing more precise distributions over future outcomes while preserving recall. However, this is challenging as these priors are typically non-differentiable and thus not directly amenable to gradient based optimization. For instance, the fact that humans tend to follow the traffic rules can be better described as a discrete (follow/not follow) action. To this end, the present disclosure proposes a flexible framework to incorporate non-differentiable prior knowledge as a loss and exploit gradient estimation techniques such as, for example, the REINFORCE gradient estimator. See R. J. Williams, “Simple statistical gradient-following algorithms for connectionist reinforcement learning,” Machine learning, vol. 8, no. 3-4, pp. 229-256, 1992.

Thus, the present disclosure provides a novel framework to incorporate prior knowledge explicitly into probabilistic motion forecasting systems. Importantly the proposed approach still allows predicting non-compliant behavior that does not follow traffic rules in the rare event that this happens. The proposed methods are general and can be applied to any model that can generate trajectory samples y and evaluate their marginal likelihood p (y|x) efficiently, where x is the observations of the environment (e.g., as represented by collected sensor data). In particular, given a traffic scene, humans have rich prior knowledge over how the traffic participants might behave. The systems and methods of the present disclosure directly use this prior knowledge as supervision when learning a distribution over future trajectories that is both diverse and precise.

Towards this goal, example systems and methods encode this prior knowledge as a deterministic reward function r(y,x). The prior knowledge loss can then be defined as the negative expected reward over samples from the future trajectory distribution. Applying the loss directly to the point estimate of the means is typically not sufficient since the goal is to learn an accurate characterization of the full distribution for safe motion planning. The goal is then to learn a stochastic policy or model (parameterized by θ) that maximizes the expected reward: Thus:

ℒprior(x;θ)=𝔼y∼p⁢θ⁡(y⁢❘"\[LeftBracketingBar]"x)[-r⁡(y,x)]=∫-pθ(y⁢❘"\[LeftBracketingBar]"x)⁢r⁡(y,x)⁢dy

Most priors are non-differentiable and cannot be easily relaxed (e.g., a motion forecast following the traffic rules or not) Thus, example implementations of the present disclosure leverage policy gradient algorithms, which do not assume differentiability of the reward function r and allow optimization without making any approximations. In particular, some example implementations exploit the REINFORCE algorithm, which only requires the policy to be differentiable, and provides efficient sampling and likelihood evaluation.

In this case, the gradient of the expected loss can be computed as:
∇prior(x;θ)=y˜πθ(y)[−∇log pθ(y|x)r(y,x)]

The expectation can then be approximated by drawing samples from the predicted distribution as follows

∇ℒprior(x;θ)≈1S⁢∑iS∑tT-∇log⁢pθ(yti⁢❘"\[LeftBracketingBar]"x)⁢r⁡(yti,x)
with S the number of samples. Although this Monte Carlo estimation is unbiased, it has typically high variance. However, example experiments have shown that this does not pose a problem when using a policy that has an efficient sampling mechanism, since a large number of samples can be efficiently drawn.

In practice, some example proposed rewards functions consider prior knowledge about the fact that drivers tend to follow their reachable lanes (e.g., based on the lane-graph or road topology), as well as to respect traffic lights. Furthermore, in some implementations, knowledge about the autonomous vehicle's desired route can be leveraged to focus more on the forecasting of the most relevant actors. In particular, missing the prediction of an actor coming in conflict with the autonomous vehicle's route (false negatives) or predicting that an actor will cross in front of the autonomous vehicle when in reality it stops (false positives) are of greater importance than an actor 50 meters behind the autonomous vehicle, since these can create harmful events. In some implementations, an example final reward can be expressed as a simple linear combination of the two rewards describe above:
r(y,x)=rreach(y,x)+rroute(y,x)

In particular, some example implementations include a reward function that evaluates whether a sample trajectory generated for an object (e.g., sampled from a predicted probability distribution for location(s) of the object at future time(s)) intersects with a reachable area for the object. For example, the reachable area can be defined by a set of one or more reachable lanes that are reachable from a current location of the object while observing traffic rules.

More particularly, human driving behavior is highly structured: in the majority of scenarios, drivers will follow the road topology and traffic rules. To leverage this informative prior, but not overly penalize non-compliant behavior, example implementations define a flexible traffic-rule informed loss that is conditioned on ground-truth behavior.

To this end, some example implementations leverage a lane-graph representation (or other map data or lane or road geometry or topology data) where the nodes encode lane segments and the edges represent relationships between lane segments such as neighborhood, predecessor, and successor (e.g., taking into account direction of traffic flow). This allows the computing system to define a set of reachable lanes or, more generally, a reachable “area” as reachable areas for pedestrians may include sidewalks, crosswalks, etc.

As such, one example reward function (e.g., a “reach loss”) which can be formulated based on the reachable area for an object is as follows:

rreach(y,x)={rd,if⁢y∈reach⁢(b)∧ygt∈reach⁢(bgt)-rd,if⁢y∉reach⁢(b)∧ygt∈reach⁢(bgt)0,otherwise
where bgtis the closest ground truth bounding box (e.g., in terms of intersection-over-union) to a detected bounding box b associated with the object.

Note that to be robust to noise in the lane graph and avoid penalizing non-compliant behaviors, some example implementations only apply the loss if the ground truth trajectory y gt falls within the binary mask designating the reachable area.

In some implementations, to define the reachable area for each actor in the scene, a computing system can capture or analyze lane divider infractions on the lane-graph. Lane dividers limit the set of legal high-level actions a vehicle can take in the road. As one example, lane changing over a solid line or taking over another vehicle by crossing a yellow double solid line into opposite traffic are not allowed. This prior can be incorporated by removing the edges of the lane graph that correspond to illegal maneuvers from the lane-graph. Encoding this prior helps the model predict less entropic distributions.

In some implementations, to define the reachable area for each actor in the scene, a computing system can capture or analyze traffic state violations such as traffic light violations. In particular, many interactions occur at intersections, some of them safety critical. Thus it is important to have accurate actor predictions at intersections, particularly differentiating stopping and going behaviors. To this end, example implementations leverage the traffic control states (e.g., green, red, yellow) to remove edges connecting lane segments that are currently governed by a red traffic light or otherwise not permitted to access given the current state.

Once the lane-graph has been processed by applying the aforementioned rules (e.g., lane divider plus traffic state), lane association can be performed to match each detected vehicle to a lane (or set of lanes when the vehicle overlaps with multiple ones for example during a lane change). Subsequently, a search (e.g., a depth first search) can be performed starting from the current lane, obtaining a set of reachable lanes, which can then be used to evaluate the reward function for each sample trajectory.

As one example,FIG.2Aprovides a visualization of an example set of reachable lanes for a vehicle200. The set of reachable lanes is shown in darker coloration. Specifically, vehicle200is traveling in a first lane202. A second lane204is reachable from lane202without violating any traffic rules. Thus, lane204is included in the set of reachable lanes. However, a third lane206cannot be reached from lane202without violating traffic rules (e.g., crossing over a solid line). Therefore, lane206may be excluded to from the set of reachable lanes. Predicted trajectories can be penalized based on whether or not they remain within in the set of reachable lanes. For example, predicted trajectories208and210may avoid penalization because they remain within the set of reachable lanes while predicted trajectory212may incur a reach loss as it exceeds the set of reachable lanes.

As another example, some example implementations include a reward function that evaluates whether the sample trajectory intersects with a route associated with an ego vehicle. In particular, it is more important to precisely characterize the motion of vehicles that might interact with the autonomous vehicle, rather than other traffic participants that do not influence its behavior. As such, the area of interest can be approximated with the autonomous vehicle's planned high-level route, which can, as one example, be defined as the union of all lane segments that the autonomous vehicle can travel on to reach a preset goal, given the lane-graph.

More concretely, in some example implementations, the horizon can be set to be equal to the prediction horizon (e.g., ˜5 s), and the target lane can be generated by a high level route planner. This gives a safe approximation over its future possible locations, which can then be used to evaluate the reward function for each sample trajectory.

Specifically, positive trajectories can be defined as those with at least one waypoint falling within the autonomous vehicle's route, and negative otherwise. The reward function can be structured so that the trajectory predictions achieve high precision and high recall under this definition, taking into account if the ground-truth trajectory intersects the route (positive) or not (negative).

As one example,FIG.2Billustrates an ego vehicle300which is near another vehicle302. A high level route planner for the ego vehicle300may indicate that the ego vehicle300will continue straight in lane304. For the other vehicle302, a first predicted trajectory306may be a positive trajectory as it intersects the route of the ego vehicle300; while a second predicted trajectory308may be a negative trajectory as it does not intersect the route of the ego vehicle300.

More concretely, one example reward function (e.g., a “route loss”) which can be formulated based on the route of the ego vehicle is as follows:

rroute(y,x)={rtp⁢if⁢y∈route∧ygt∈routerfp⁢if⁢y∈route∧ygt∉routertn⁢if⁢y∉route∧ygt∉routerfn⁢if⁢y∉route∧ygt∈route
where different rewards are provided for true positive, false positive, true negative and false negative trajectory predictions since there is high imbalance in the data and they have different impact on the safety of the motion planner.

Another example aspect of the present disclosure is directed to a state-of-the-art perception and prediction model (e.g., to which the prior knowledge framework described herein can be applied). In particular, some example implementations exploit a combination of a backbone feature extraction and object detection network and graph propagation from SPAGNN (S. Casas, C. Gulino, R. Liao, and R. Urtasun, “Spatially-aware graph neural networks for relational behavior forecasting from sensor data,” arXiv preprint arXiv: 1910.08233, 2019.), together with a mixture of Gaussians output parameterization (see, e.g., Y. Chai, B. Sapp, M. Bansal, and D. Anguelov, “Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction,” arXiv preprint arXiv: 1910.05449, 2019.). In some implementations, this perception and prediction model can take a voxelized LiDAR point cloud and a raster map as input, extracts scene features using a backbone CNN, and applies Rotated Region of Interest Align to extract per-actor features. After that, a fully-connected graph can be used where the nodes correspond to traffic participants, and a series of graph-propagations can be performed to refine each actor's representation by aggregating features from their neighbors. Finally, a network such as a multi-layer perceptron can predict the parameters of a multimodal distribution over future trajectories by using a mixture of Gaussians for each actor:

p⁡(y⁢❘"\[LeftBracketingBar]"x)=∑kαψ(k)(x)⁢∏t=1TN⁡(μt(k)(x),σt(k)(x))

In some implementations, the proposed models can be trained end-to-end using backpropagation and stochastic gradient descent. In particular, some implementations can perform training by minimizing a multi-objective loss containing a classification and regression terms for object detection, negative loglikelihood of the motion forecasts, as well as the prior informed non-differentiable loss described elsewhere herein.

The loss of each actor can be a weighted sum of the multiple objectives.
=α·det+β·nll+γ·pred-prior

For the classification branch of the detection header (e.g., background vs vehicle) a binary cross entropy loss with hard negative mining can be employed (cla). As one example, all positive examples can be selected from the ground-truth and 3 times as many negative examples from the rest of spatial locations. Regarding box fitting, a smooth L1 lossregcan be applied to each of the 5 parameters (xi, yi, wi, hi, φi) of the bounding boxes anchored to a positive example i.
det=cla+λ·reg

In some implementations, instead of directly optimizing the likelihood of the mixture model, a computing system can heuristically choose the closest matching mode and only apply prediction loss on that mode. This has been shown empirically to be a more stable training objective than optimizing the mixture likelihood directly. Thus some example implementations define

Lnll=-∑k1⁢(k=kˆ)[log⁢p⁡(ak⁢❘"\[LeftBracketingBar]"x)+∑tlog⁢p⁡(ytk⁢❘"\[LeftBracketingBar]"x,ak)]
where {circumflex over (k)}=arg minkdist(yk,ŷ) is the mode whose mean is closest to the ground truth trajectory in Euclidean distance.

The proposed systems and methods provide a number of technical effects and benefits. As one example technical effect, the proposed framework allows the model to optimize for any prior knowledge on future trajectories, as long as drawing samples from the perception and prediction model and obtaining their likelihood can be done efficiently. In particular, the formulation can be applied to model how the vehicles interact with the map, encouraging the predictions to respect lane dividers and traffic lights. The proposed framework can also be exploited to make the motion forecasting module more planning aware by emphasizing the importance of high recall and high precision near the ego autonomous vehicle route.

As another example technical benefit, example experiments show that the proposed framework can improve the map understanding of state-of-the-art motion forecasting methods in very complex, partially observable urban environments. Importantly, the proposed approach achieves significant improvements in the precision of the trajectory distribution, while maintaining the recall. The improved object motion predictions also significantly impact downstream motion planning. In particular, including prior knowledge not only results in significantly more comfortable rides, but also in major safety improvements over the decisions taken by a state-of-the-art motion planner.

The autonomous vehicle technology described herein can help improve the safety of passengers of an autonomous vehicle, improve the safety of the surroundings of the autonomous vehicle, improve the experience of the rider and/or operator of the autonomous vehicle, as well as provide other improvements as described herein. Moreover, the autonomous vehicle technology of the present disclosure can help improve the ability of an autonomous vehicle to effectively provide vehicle services to others and support the various members of the community in which the autonomous vehicle is operating, including persons with reduced mobility and/or persons that are underserved by other transportation options. Additionally, the autonomous vehicle of the present disclosure may reduce traffic congestion in communities as well as provide alternate forms of transportation that may provide environmental benefits.

With reference now to the Figures, example embodiments of the present disclosure will be discussed in further detail.

Example Systems

FIG.3depicts a block diagram of an example system100for controlling and communicating with a vehicle according to example aspects of the present disclosure. As illustrated,FIG.3shows a system100that can include a vehicle105and a vehicle computing system110associated with the vehicle105. The vehicle computing system100can be located onboard the vehicle105(e.g., it can be included on and/or within the vehicle105).

The vehicle105incorporating the vehicle computing system100can be various types of vehicles. For instance, the vehicle105can be an autonomous vehicle. The vehicle105can be a ground-based autonomous vehicle (e.g., car, truck, bus, etc.). The vehicle105can be an air-based autonomous vehicle (e.g., airplane, helicopter, vertical take-off and lift (VTOL) aircraft, etc.). The vehicle105can be a lightweight electric vehicle (e.g., bicycle, scooter, etc.). The vehicle105can be another type of vehicles (e.g., watercraft, etc.). The vehicle105can drive, navigate, operate, etc. with minimal and/or no interaction from a human operator (e.g., driver, pilot, etc.). In some implementations, a human operator can be omitted from the vehicle105(and/or also omitted from remote control of the vehicle105). In some implementations, a human operator can be included in the vehicle105.

The vehicle105can be configured to operate in a plurality of operating modes. The vehicle105can be configured to operate in a fully autonomous (e.g., self-driving) operating mode in which the vehicle105is controllable without user input (e.g., can drive and navigate with no input from a human operator present in the vehicle105and/or remote from the vehicle105). The vehicle105can operate in a semi-autonomous operating mode in which the vehicle105can operate with some input from a human operator present in the vehicle105(and/or a human operator that is remote from the vehicle105). The vehicle105can enter into a manual operating mode in which the vehicle105is fully controllable by a human operator (e.g., human driver, pilot, etc.) and can be prohibited and/or disabled (e.g., temporary, permanently, etc.) from performing autonomous navigation (e.g., autonomous driving, flying, etc.). The vehicle105can be configured to operate in other modes such as, for example, park and/or sleep modes (e.g., for use between tasks/actions such as waiting to provide a vehicle service, recharging, etc.). In some implementations, the vehicle105can implement vehicle operating assistance technology (e.g., collision mitigation system, power assist steering, etc.), for example, to help assist the human operator of the vehicle105(e.g., while in a manual mode, etc.).

To help maintain and switch between operating modes, the vehicle computing system110can store data indicative of the operating modes of the vehicle105in a memory onboard the vehicle105. For example, the operating modes can be defined by an operating mode data structure (e.g., rule, list, table, etc.) that indicates one or more operating parameters for the vehicle105, while in the particular operating mode. For example, an operating mode data structure can indicate that the vehicle105is to autonomously plan its motion when in the fully autonomous operating mode. The vehicle computing system110can access the memory when implementing an operating mode.

The operating mode of the vehicle105can be adjusted in a variety of manners. For example, the operating mode of the vehicle105can be selected remotely, off-board the vehicle105. For example, a remote computing system (e.g., of a vehicle provider and/or service entity associated with the vehicle105) can communicate data to the vehicle105instructing the vehicle105to enter into, exit from, maintain, etc. an operating mode. By way of example, such data can instruct the vehicle105to enter into the fully autonomous operating mode.

In some implementations, the operating mode of the vehicle105can be set onboard and/or near the vehicle105. For example, the vehicle computing system110can automatically determine when and where the vehicle105is to enter, change, maintain, etc. a particular operating mode (e.g., without user input). Additionally, or alternatively, the operating mode of the vehicle105can be manually selected via one or more interfaces located onboard the vehicle105(e.g., key switch, button, etc.) and/or associated with a computing device proximate to the vehicle105(e.g., a tablet operated by authorized personnel located near the vehicle105). In some implementations, the operating mode of the vehicle105can be adjusted by manipulating a series of interfaces in a particular order to cause the vehicle105to enter into a particular operating mode.

The vehicle computing system110can include one or more computing devices located onboard the vehicle105. For example, the computing device(s) can be located on and/or within the vehicle105. The computing device(s) can include various components for performing various operations and functions. For instance, the computing device(s) can include one or more processors and one or more tangible, non-transitory, computer readable media (e.g., memory devices, etc.). The one or more tangible, non-transitory, computer readable media can store instructions that when executed by the one or more processors cause the vehicle105(e.g., its computing system, one or more processors, etc.) to perform operations and functions, such as those described herein for controlling an autonomous vehicle, communicating with other computing systems, etc.

The vehicle105can include a communications system115configured to allow the vehicle computing system110(and its computing device(s)) to communicate with other computing devices. The communications system115can include any suitable components for interfacing with one or more network(s)120, including, for example, transmitters, receivers, ports, controllers, antennas, and/or other suitable components that can help facilitate communication. In some implementations, the communications system115can include a plurality of components (e.g., antennas, transmitters, and/or receivers) that allow it to implement and utilize multiple-input, multiple-output (MIMO) technology and communication techniques.

The vehicle computing system110can use the communications system115to communicate with one or more computing device(s) that are remote from the vehicle105over one or more networks120(e.g., via one or more wireless signal connections). The network(s)120can exchange (send or receive) signals (e.g., electronic signals), data (e.g., data from a computing device), and/or other information and include any combination of various wired (e.g., twisted pair cable) and/or wireless communication mechanisms (e.g., cellular, wireless, satellite, microwave, and radio frequency) and/or any desired network topology (or topologies). For example, the network(s)120can include a local area network (e.g. intranet), wide area network (e.g. Internet), wireless LAN network (e.g., via Wi-Fi), cellular network, a SATCOM network, VHF network, a HF network, a WiMAX based network, and/or any other suitable communication network (or combination thereof) for transmitting data to and/or from the vehicle105and/or among computing systems.

In some implementations, the communications system115can also be configured to enable the vehicle105to communicate with and/or provide and/or receive data and/or signals from a remote computing device associated with a user125and/or an item (e.g., an item to be picked-up for a courier service). For example, the communications system115can allow the vehicle105to locate and/or exchange communications with a user device130of a user125. In some implementations, the communications system115can allow communication among one or more of the system(s) on-board the vehicle105.

As shown inFIG.3, the vehicle105can include one or more sensors135, an autonomy computing system140, a vehicle interface145, one or more vehicle control systems150, and other systems, as described herein. One or more of these systems can be configured to communicate with one another via one or more communication channels. The communication channel(s) can include one or more data buses (e.g., controller area network (CAN)), on-board diagnostics connector (e.g., OBD-II), and/or a combination of wired and/or wireless communication links. The onboard systems can send and/or receive data, messages, signals, etc. amongst one another via the communication channel(s).

The sensor(s)135can be configured to acquire sensor data155. The sensor(s)135can be external sensors configured to acquire external sensor data. This can include sensor data associated with the surrounding environment of the vehicle105. The surrounding environment of the vehicle105can include/be represented in the field of view of the sensor(s)135. For instance, the sensor(s)135can acquire image and/or other data of the environment outside of the vehicle105and within a range and/or field of view of one or more of the sensor(s)135. The sensor(s)135can include one or more Light Detection and Ranging (LIDAR) systems, one or more Radio Detection and Ranging (RADAR) systems, one or more cameras (e.g., visible spectrum cameras, infrared cameras, etc.), one or more motion sensors, one or more audio sensors (e.g., microphones, etc.), and/or other types of imaging capture devices and/or sensors. The one or more sensors can be located on various parts of the vehicle105including a front side, rear side, left side, right side, top, and/or bottom of the vehicle105. The sensor data155can include image data (e.g., 2D camera data, video data, etc.), RADAR data, LIDAR data (e.g., 3D point cloud data, etc.), audio data, and/or other types of data. The vehicle105can also include other sensors configured to acquire data associated with the vehicle105. For example, the vehicle105can include inertial measurement unit(s), wheel odometry devices, and/or other sensors.

In some implementations, the sensor(s)135can include one or more internal sensors. The internal sensor(s) can be configured to acquire sensor data155associated with the interior of the vehicle105. For example, the internal sensor(s) can include one or more cameras, one or more infrared sensors, one or more motion sensors, one or more weight sensors (e.g., in a seat, in a trunk, etc.), and/or other types of sensors. The sensor data155acquired via the internal sensor(s) can include, for example, image data indicative of a position of a passenger or item located within the interior (e.g., cabin, trunk, etc.) of the vehicle105. This information can be used, for example, to ensure the safety of the passenger, to prevent an item from being left by a passenger, confirm the cleanliness of the vehicle105, remotely assist a passenger, etc.

In some implementations, the sensor data155can be indicative of one or more objects within the surrounding environment of the vehicle105. The object(s) can include, for example, vehicles, pedestrians, bicycles, and/or other objects. The object(s) can be located in front of, to the rear of, to the side of, above, below the vehicle105, etc. The sensor data155can be indicative of locations associated with the object(s) within the surrounding environment of the vehicle105at one or more times. The object(s) can be static objects (e.g., not in motion) and/or dynamic objects/actors (e.g., in motion or likely to be in motion) in the vehicle's environment. The sensor(s)135can provide the sensor data155to the autonomy computing system140.

In addition to the sensor data155, the autonomy computing system140can obtain map data160. The map data160can provide detailed information about the surrounding environment of the vehicle105and/or the geographic area in which the vehicle was, is, and/or will be located. For example, the map data160can provide information regarding: the identity and location of different roadways, road segments, buildings, or other items or objects (e.g., lampposts, crosswalks and/or curb); the location and directions of traffic lanes (e.g., the location and direction of a parking lane, a turning lane, a bicycle lane, or other lanes within a particular roadway or other travel way and/or one or more boundary markings associated therewith); traffic control data (e.g., the location and instructions of signage, traffic lights, and/or other traffic control devices); obstruction information (e.g., temporary or permanent blockages, etc.); event data (e.g., road closures/traffic rule alterations due to parades, concerts, sporting events, etc.); nominal vehicle path data (e.g., indicate of an ideal vehicle path such as along the center of a certain lane, etc.); and/or any other map data that provides information that assists the vehicle computing system110in processing, analyzing, and perceiving its surrounding environment and its relationship thereto. In some implementations, the map data160can include high definition map data. In some implementations, the map data160can include sparse map data indicative of a limited number of environmental features (e.g., lane boundaries, etc.). In some implementations, the map data can be limited to geographic area(s) and/or operating domains in which the vehicle105(or autonomous vehicles generally) may travel (e.g., due to legal/regulatory constraints, autonomy capabilities, and/or other factors).

The vehicle105can include a positioning system165. The positioning system165can determine a current position of the vehicle105. This can help the vehicle105localize itself within its environment. The positioning system165can be any device or circuitry for analyzing the position of the vehicle105. For example, the positioning system165can determine position by using one or more of inertial sensors (e.g., inertial measurement unit(s), etc.), a satellite positioning system, based on IP address, by using triangulation and/or proximity to network access points or other network components (e.g., cellular towers, WiFi access points, etc.) and/or other suitable techniques. The position of the vehicle105can be used by various systems of the vehicle computing system110and/or provided to a remote computing system. For example, the map data160can provide the vehicle105relative positions of the elements of a surrounding environment of the vehicle105. The vehicle105can identify its position within the surrounding environment (e.g., across six axes, etc.) based at least in part on the map data160. For example, the vehicle computing system110can process the sensor data155(e.g., LIDAR data, camera data, etc.) to match it to a map of the surrounding environment to get an understanding of the vehicle's position within that environment. Data indicative of the vehicle's position can be stored, communicated to, and/or otherwise obtained by the autonomy computing system140.

The autonomy computing system140can perform various functions for autonomously operating the vehicle105. For example, the autonomy computing system140can perform the following functions: perception170A, prediction170B, and motion planning170C. For example, the autonomy computing system130can obtain the sensor data155via the sensor(s)135, process the sensor data155(and/or other data) to perceive its surrounding environment, predict the motion of objects within the surrounding environment, and generate an appropriate motion plan through such surrounding environment. In some implementations, these autonomy functions can be performed by one or more sub-systems such as, for example, a perception system, a prediction system, a motion planning system, and/or other systems that cooperate to perceive the surrounding environment of the vehicle105and determine a motion plan for controlling the motion of the vehicle105accordingly. In some implementations, one or more of the perception, prediction, and/or motion planning functions170A,170B,170C can be performed by (and/or combined into) the same system and/or via shared computing resources. In some implementations, one or more of these functions can be performed via difference sub-systems. As further described herein, the autonomy computing system140can communicate with the one or more vehicle control systems150to operate the vehicle105according to the motion plan (e.g., via the vehicle interface145, etc.).

The vehicle computing system110(e.g., the autonomy computing system140) can identify one or more objects that within the surrounding environment of the vehicle105based at least in part on the sensor data135and/or the map data160. The objects perceived within the surrounding environment can be those within the field of view of the sensor(s)135and/or predicted to be occluded from the sensor(s)135. This can include object(s) not in motion or not predicted to move (static objects) and/or object(s) in motion or predicted to be in motion (dynamic objects/actors). The vehicle computing system110(e.g., performing the perception function170A, using a perception system, etc.) can process the sensor data155, the map data160, etc. to obtain perception data175A. The vehicle computing system110can generate perception data175A that is indicative of one or more states (e.g., current and/or past state(s)) of one or more objects that are within a surrounding environment of the vehicle105. For example, the perception data175A for each object can describe (e.g., for a given time, time period) an estimate of the object's: current and/or past location (also referred to as position); current and/or past speed/velocity; current and/or past acceleration; current and/or past heading; current and/or past orientation; size/footprint (e.g., as represented by a bounding shape, object highlighting, etc.); class (e.g., pedestrian class vs. vehicle class vs. bicycle class, etc.), the uncertainties associated therewith, and/or other state information. The vehicle computing system110can utilize one or more algorithms and/or machine-learned model(s) that are configured to identify object(s) based at least in part on the sensor data155. This can include, for example, one or more neural networks trained to identify object(s) within the surrounding environment of the vehicle105and the state data associated therewith. The perception data175A can be utilized for the prediction function175B of the autonomy computing system140.

The vehicle computing system110can be configured to predict a motion of the object(s) within the surrounding environment of the vehicle105. For instance, the vehicle computing system110can generate prediction data175B associated with such object(s). The prediction data175B can be indicative of one or more predicted future locations of each respective object. For example, the prediction system175B can determine a predicted motion trajectory along which a respective object is predicted to travel over time. A predicted motion trajectory can be indicative of a path that the object is predicted to traverse and an associated timing with which the object is predicted to travel along the path. The predicted path can include and/or be made up of a plurality of way points. In some implementations, the prediction data175B can be indicative of the speed and/or acceleration at which the respective object is predicted to travel along its associated predicted motion trajectory. The vehicle computing system110can utilize one or more algorithms and/or machine-learned model(s) that are configured to predict the future motion of object(s) based at least in part on the sensor data155, the perception data175A, map data160, and/or other data. For example, the vehicle computing system110can perform any of the methods or probabilistic object motion prediction models described herein to generate the prediction data175B. This can include, for example, one or more neural networks trained to predict the motion of the object(s) within the surrounding environment of the vehicle105based at least in part on the past and/or current state(s) of those objects as well as the environment in which the objects are located (e.g., the lane boundary in which it is travelling, etc.). The prediction data175B can be utilized for the motion planning function170C of the autonomy computing system140.

The vehicle computing system110can determine a motion plan for the vehicle105based at least in part on the perception data175A, the prediction data175B, and/or other data. For example, the vehicle computing system110can generate motion planning data175C indicative of a motion plan. The motion plan can include vehicle actions (e.g., speed(s), acceleration(s), other actions, etc.) with respect to one or more of the objects within the surrounding environment of the vehicle105as well as the objects' predicted movements. The motion plan can include one or more vehicle motion trajectories that indicate a path for the vehicle105to follow. A vehicle motion trajectory can be of a certain length and/or time range. A vehicle motion trajectory can be defined by one or more way points (with associated coordinates). The planned vehicle motion trajectories can indicate the path the vehicle105is to follow as it traverses a route from one location to another. Thus, the vehicle computing system110can take into account a route/route data when performing the motion planning function170C.

The motion planning system180can implement an optimization algorithm, machine-learned model, etc. that considers cost data associated with a vehicle action as well as other objective functions (e.g., cost functions based on speed limits, traffic lights, etc.), if any, to determine optimized variables that make up the motion plan. The vehicle computing system110can determine that the vehicle105can perform a certain action (e.g., pass an object, etc.) without increasing the potential risk to the vehicle105and/or violating any traffic laws (e.g., speed limits, lane boundaries, signage, etc.). For instance, the vehicle computing system110can evaluate the predicted motion trajectories of one or more objects during its cost data analysis to help determine an optimized vehicle trajectory through the surrounding environment. The motion planning system180can generate cost data associated with such trajectories. In some implementations, one or more of the predicted motion trajectories and/or perceived objects may not ultimately change the motion of the vehicle105(e.g., due to an overriding factor). In some implementations, the motion plan may define the vehicle's motion such that the vehicle105avoids the object(s), reduces speed to give more leeway to one or more of the object(s), proceeds cautiously, performs a stopping action, passes an object, queues behind/in front of an object, etc.

The vehicle computing system110can be configured to continuously update the vehicle's motion plan and a corresponding planned vehicle motion trajectories. For example, in some implementations, the vehicle computing system110can generate new motion planning data175C/motion plan(s) for the vehicle105(e.g., multiple times per second, etc.). Each new motion plan can describe a motion of the vehicle105over the next planning period (e.g., next several seconds, etc.). Moreover, a new motion plan may include a new planned vehicle motion trajectory. Thus, in some implementations, the vehicle computing system110can continuously operate to revise or otherwise generate a short-term motion plan based on the currently available data. Once the optimization planner has identified the optimal motion plan (or some other iterative break occurs), the optimal motion plan (and the planned motion trajectory) can be selected and executed by the vehicle105.

The vehicle computing system110can cause the vehicle105to initiate a motion control in accordance with at least a portion of the motion planning data175C. A motion control can be an operation, action, etc. that is associated with controlling the motion of the vehicle105. For instance, the motion planning data175C can be provided to the vehicle control system(s)150of the vehicle105. The vehicle control system(s)150can be associated with a vehicle interface145that is configured to implement a motion plan. The vehicle interface145can serve as an interface/conduit between the autonomy computing system140and the vehicle control systems150of the vehicle105and any electrical/mechanical controllers associated therewith. The vehicle interface145can, for example, translate a motion plan into instructions for the appropriate vehicle control component (e.g., acceleration control, brake control, steering control, etc.). By way of example, the vehicle interface145can translate a determined motion plan into instructions to adjust the steering of the vehicle105“X” degrees, apply a certain magnitude of braking force, increase/decrease speed, etc. The vehicle interface145can help facilitate the responsible vehicle control (e.g., braking control system, steering control system, acceleration control system, etc.) to execute the instructions and implement a motion plan (e.g., by sending control signal(s), making the translated plan available, etc.). This can allow the vehicle105to autonomously travel within the vehicle's surrounding environment.

The vehicle computing system110can store other types of data. For example, an indication, record, and/or other data indicative of the state of the vehicle (e.g., its location, motion trajectory, health information, etc.), the state of one or more users (e.g., passengers, operators, etc.) of the vehicle, and/or the state of an environment including one or more objects (e.g., the physical dimensions and/or appearance of the one or more objects, locations, predicted motion, etc.) can be stored locally in one or more memory devices of the vehicle105. Additionally, the vehicle105can communicate data indicative of the state of the vehicle, the state of one or more passengers of the vehicle, and/or the state of an environment to a computing system that is remote from the vehicle105, which can store such information in one or more memories remote from the vehicle105. Moreover, the vehicle105can provide any of the data created and/or store onboard the vehicle105to another vehicle.

The vehicle computing system110can include the one or more vehicle user devices180. For example, the vehicle computing system110can include one or more user devices with one or more display devices located onboard the vehicle15. A display device (e.g., screen of a tablet, laptop, and/or smartphone) can be viewable by a user of the vehicle105that is located in the front of the vehicle105(e.g., driver's seat, front passenger seat). Additionally, or alternatively, a display device can be viewable by a user of the vehicle105that is located in the rear of the vehicle105(e.g., a back passenger seat). The user device(s) associated with the display devices can be any type of user device such as, for example, a table, mobile phone, laptop, etc. The vehicle user device(s)180can be configured to function as human-machine interfaces. For example, the vehicle user device(s)180can be configured to obtain user input, which can then be utilized by the vehicle computing system110and/or another computing system (e.g., a remote computing system, etc.). For example, a user (e.g., a passenger for transportation service, a vehicle operator, etc.) of the vehicle105can provide user input to adjust a destination location of the vehicle105. The vehicle computing system110and/or another computing system can update the destination location of the vehicle105and the route associated therewith to reflect the change indicated by the user input.

The vehicle105can be configured to perform vehicle services for one or a plurality of different service entities185. A vehicle105can perform a vehicle service by, for example and as further described herein, travelling (e.g., traveling autonomously) to a location associated with a requested vehicle service, allowing user(s) and/or item(s) to board or otherwise enter the vehicle105, transporting the user(s) and/or item(s), allowing the user(s) and/or item(s) to deboard or otherwise exit the vehicle105, etc. In this way, the vehicle105can provide the vehicle service(s) for a service entity to a user.

A service entity185can be associated with the provision of one or more vehicle services. For example, a service entity can be an individual, a group of individuals, a company (e.g., a business entity, organization, etc.), a group of entities (e.g., affiliated companies), and/or another type of entity that offers and/or coordinates the provision of one or more vehicle services to one or more users. For example, a service entity can offer vehicle service(s) to users via one or more software applications (e.g., that are downloaded onto a user computing device), via a website, and/or via other types of interfaces that allow a user to request a vehicle service. As described herein, the vehicle services can include transportation services (e.g., by which a vehicle transports user(s) from one location to another), delivery services (e.g., by which a vehicle transports/delivers item(s) to a requested destination location), courier services (e.g., by which a vehicle retrieves item(s) from a requested origin location and transports/delivers the item to a requested destination location), and/or other types of services. The vehicle services can be wholly performed by the vehicle105(e.g., travelling from the user/item origin to the ultimate destination, etc.) or performed by one or more vehicles and/or modes of transportation (e.g., transferring the user/item at intermediate transfer points, etc.).

An operations computing system190A of the service entity185can help to coordinate the performance of vehicle services by autonomous vehicles. The operations computing system190A can include and/or implement one or more service platforms of the service entity. The operations computing system190A can include one or more computing devices. The computing device(s) can include various components for performing various operations and functions. For instance, the computing device(s) can include one or more processors and one or more tangible, non-transitory, computer readable media (e.g., memory devices, etc.). The one or more tangible, non-transitory, computer readable media can store instructions that when executed by the one or more processors cause the operations computing system190(e.g., its one or more processors, etc.) to perform operations and functions, such as those described herein matching users and vehicles/vehicle fleets, deploying vehicles, facilitating the provision of vehicle services via autonomous vehicles, etc.

A user125can request a vehicle service from a service entity185. For example, the user125can provide user input to a user device130to request a vehicle service (e.g., via a user interface associated with a mobile software application of the service entity185running on the user device130). The user device130can communicate data indicative of a vehicle service request195to the operations computing system190A associated with the service entity185(and/or another associated computing system that can then communicate data to the operations computing system190A). The vehicle service request195can be associated with a user. The associated user can be the one that submits the vehicle service request (e.g., via an application on the user device130). In some implementations, the user may not be the user that submits the vehicle service request. The vehicle service request can be indicative of the user. For example, the vehicle service request can include an identifier associated with the user and/or the user's profile/account with the service entity185. The vehicle service request195can be generated in a manner that avoids the use of personally identifiable information and/or allows the user to control the types of information included in the vehicle service request195. The vehicle service request195can also be generated, communicated, stored, etc. in a secure manner to protect information.

The vehicle service request195can indicate various types of information. For example, the vehicle service request194can indicate the type of vehicle service that is desired (e.g., a transportation service, a delivery service, a courier service, etc.), one or more locations (e.g., an origin location, a destination location, etc.), timing constraints (e.g., pick-up time, drop-off time, deadlines, etc.), and/or geographic constraints (e.g., to stay within a certain area, etc.). The service request195can indicate a type/size/class of vehicle such as, for example, a sedan, an SUV, luxury vehicle, standard vehicle, etc. The service request195can indicate a product of the service entity185. For example, the service request195can indicate that the user is requesting a transportation pool product by which the user would potentially share the vehicle (and costs) with other users/items. In some implementations, the service request195can explicitly request for the vehicle service to be provided by an autonomous vehicle or a human-driven vehicle. In some implementations, the service request195can indicate a number of users that will be riding in the vehicle/utilizing the vehicle service. In some implementations, the service request195can indicate preferences/special accommodations of an associated user (e.g., music preferences, climate preferences, wheelchair accessibility, etc.) and/or other information.

The operations computing system190A of the service entity185can process the data indicative of the vehicle service request195and generate a vehicle service assignment that is associated with the vehicle service request. The operations computing system can identify one or more vehicles that may be able to perform the requested vehicle services to the user195. The operations computing system190A can identify which modes of transportation are available to a user for the requested vehicle service (e.g., light electric vehicles, human-drive vehicles, autonomous vehicles, aerial vehicle, etc.) and/or the number of transportation modes/legs of a potential itinerary of the user for completing the vehicle service (e.g., single or plurality of modes, single or plurality of legs, etc.). For example, the operations computing system190A can determined which autonomous vehicle(s) are online with the service entity185(e.g., available for a vehicle service assignment, addressing a vehicle service assignment, etc.) to help identify which autonomous vehicle(s) would be able to provide the vehicle service.

The operations computing system190A and/or the vehicle computing system110can communicate with one or more other computing systems190B that are remote from the vehicle105. This can include, for example, computing systems associated with government functions (e.g., emergency services, regulatory bodies, etc.), computing systems associated with vehicle providers other than the service entity, computing systems of other vehicles (e.g., other autonomous vehicles, aerial vehicles, etc.). Communication with the other computing systems190B can occur via the network(s)120.

Example Methods

FIG.4depicts a flow diagram of an example method400for training probabilistic object motion prediction models using non-differentiable prior knowledge according to example embodiments of the present disclosure. One or more portion(s) of the method400can be implemented by one or more computing devices such as, for example, the computing devices described inFIGS.3,5, and/or6. Moreover, one or more portion(s) of the method400can be implemented as an algorithm on the hardware components of the device(s) described herein (e.g., as inFIGS.3,5, and/or6).FIG.4depicts elements performed in a particular order for purposes of illustration and discussion. Those of ordinary skill in the art, using the disclosures provided herein, will understand that the elements of any of the methods discussed herein can be adapted, rearranged, expanded, omitted, combined, and/or modified in various ways without deviating from the scope of the present disclosure.

At (402), the method400can include obtaining, by a computing system, sensor data descriptive of an environment that includes an object. In some implementations, the sensor data descriptive of the environment can be or include real world sensor data collected by sensors associated with a vehicle while the vehicle operated in the real world.

At (404), the method400can include processing, by the computing system, the sensor data with a machine-learned object motion prediction model to obtain a predicted location probability distribution for a future location of the object at one or more future times.

In some implementations, the one or more future times can be a plurality of future times.

In some implementations, the machine-learned object motion prediction model can be or include a spatially-aware graph neural network combined with a multi-layer perceptron parameterized as a mixture of Gaussians.

At (406), the method400can include sampling, by the computing system, a plurality of sample trajectories from the predicted location probability distribution for the object.

At (408), the method400can include evaluating, by the computing system and for each sample trajectory, a non-differentiable prior knowledge reward function that encodes prior knowledge about motion of the object to obtain a respective reward value for the sample trajectory.

In some implementations, the non-differentiable prior knowledge reward function compares the sample trajectory with a ground truth data associated with a real world object captured in the real world sensor data.

In some implementations, the prior knowledge about motion of the object can be or include prior knowledge about lane geometry, road topology, or traffic rules within the environment.

In some implementations, evaluating, by the computing system and for each sample trajectory, the non-differentiable prior knowledge reward function includes determining, by the computing system, a reachable area for the object and evaluating, by the computing system and for each sample trajectory, the non-differentiable prior knowledge reward function based on the reachable area. For example, the reachable area can be defined by a set of one or more reachable lanes that are reachable from a current location of the object while observing traffic rules.

In some implementations, the non-differentiable prior knowledge reward function returns a positive reward when the sample trajectory stays within the reachable area; and the non-differentiable prior knowledge reward function returns a negative reward when the sample trajectory exits the reachable area.

In some implementations: the non-differentiable prior knowledge reward function returns a positive reward when the sample trajectory stays within the reachable area and a ground truth trajectory associated with the object stays within a ground truth reachable area; and the non-differentiable prior knowledge reward function returns a negative reward when the sample trajectory exits the reachable area and the ground truth trajectory associated with the object stays within the ground truth reachable area.

In some implementations, the traffic rules can be or include lane infraction rules that prohibit crossing a solid line or entering a lane having an opposite traffic flow direction.

In some implementations, the traffic rules can include adhering to a current traffic control state provided by a traffic control device.

In some implementations, evaluating, by the computing system and for each sample trajectory, the non-differentiable prior knowledge reward function can include determining, by the computing system and for each sample trajectory, whether the sample trajectory intersects with a route associated with an ego vehicle, wherein the reward value is a function of whether the sample trajectory intersects with the route.

In some implementations: the non-differentiable prior knowledge reward function returns a true positive reward value when the sample trajectory intersects the route and a ground truth trajectory associated with the object intersects the route; the non-differentiable prior knowledge reward function returns a false positive reward value when the sample trajectory intersects the route and the ground truth trajectory associated with the object does not intersect the route; the non-differentiable prior knowledge reward function returns a true negative reward value when the sample trajectory does not intersect the route and the ground truth trajectory associated with the object does not intersect the route; and the non-differentiable prior knowledge reward function returns a false positive reward value when the sample trajectory does not intersect the route and the ground truth trajectory associated with the object intersects the route.

In some implementations, the route associated with the ego vehicle can be or include a current motion plan for the ego vehicle.

At (410), the method400can include determining, by the computing system, an approximate gradient of an expected loss based at least in part on the plurality of reward values respectively determined for the plurality of sample trajectories.

In some implementations, determining, by the computing system, the approximate gradient of the expected loss based at least in part on the plurality of reward values respectively determined for the plurality of sample trajectories can include performing, by the computing system, a REINFORCE gradient estimation technique.

At (412), the method400can include modifying, by the computing system, one or more values of one or more parameters of the machine-learned object motion prediction model based at least in part on the approximate gradient of the expected loss.

In some implementations, modifying, by the computing system, the one or more values of the one or more parameters of the machine-learned object motion prediction model based at least in part on the approximate gradient of the expected loss can include modifying, by the computing system, the one or more values of the one or more parameters of the machine-learned object motion prediction model based at least in part on a heuristically chosen closest matching mode.

Example Means

Various means can be configured to perform the methods and processes described herein.FIG.5depicts example units associated with a computing system for performing operations and functions according to example embodiments of the present disclosure. As depicted,FIG.5depicts a computing system500that can include, but is not limited to, sensor data obtaining unit(s)505; object motion prediction unit(s)510; trajectory sampling unit(s)515; reward function evaluating unit(s)520; gradient approximating unit(s)525; and/or model training unit(s)530. In some implementations one or more units may be implemented separately. In some implementations, one or more units may be included in one or more other units.

In some implementations, one or more of the units may be implemented separately. In some implementations, one or more units may be a part of or included in one or more other units. These means can include processor(s), microprocessor(s), graphics processing unit(s), logic circuit(s), dedicated circuit(s), application-specific integrated circuit(s), programmable array logic, field-programmable gate array(s), controller(s), microcontroller(s), and/or other suitable hardware. The means can also, or alternately, include software control means implemented with a processor or logic circuitry, for example. The means can include or otherwise be able to access memory such as, for example, one or more non-transitory computer-readable storage media, such as random-access memory, read-only memory, electrically erasable programmable read-only memory, erasable programmable read-only memory, flash/other memory device(s), data registrar(s), database(s), and/or other suitable hardware.

The means can be programmed to perform one or more algorithm(s) for carrying out the operations and functions described herein (including the claims).

In particular, the sensor data obtaining unit(s)505can be configured to obtain sensor data descriptive of an environment that includes an object. In some implementations, the sensor data descriptive of the environment can be or include real world sensor data collected by sensors associated with a vehicle while the vehicle operated in the real world.

The objection motion prediction unit(s)510can be configured to process the sensor data with a machine-learned object motion prediction model to obtain a predicted location probability distribution for a future location of the object at one or more future times. In some implementations, the one or more future times can be a plurality of future times. In some implementations, the machine-learned object motion prediction model can be or include a spatially-aware graph neural network combined with a multi-layer perceptron parameterized as a mixture of Gaussians.

The trajectory sampling unit(s)515can be configured to sample a plurality of sample trajectories from the predicted location probability distribution for the object.

The reward function evaluating unit(s)520can be configured to evaluate, for each sample trajectory, a non-differentiable prior knowledge reward function that encodes prior knowledge about motion of the object to obtain a respective reward value for the sample trajectory.

In some implementations, the non-differentiable prior knowledge reward function compares the sample trajectory with a ground truth data associated with a real world object captured in the real world sensor data.

In some implementations, the prior knowledge about motion of the object can be or include prior knowledge about lane geometry, road topology, or traffic rules within the environment.

In some implementations, evaluating, for each sample trajectory, the non-differentiable prior knowledge reward function includes determining a reachable area for the object and evaluating, for each sample trajectory, the non-differentiable prior knowledge reward function based on the reachable area. For example, the reachable area can be defined by a set of one or more reachable lanes that are reachable from a current location of the object while observing traffic rules.

In some implementations, the non-differentiable prior knowledge reward function returns a positive reward when the sample trajectory stays within the reachable area; and the non-differentiable prior knowledge reward function returns a negative reward when the sample trajectory exits the reachable area.

In some implementations: the non-differentiable prior knowledge reward function returns a positive reward when the sample trajectory stays within the reachable area and a ground truth trajectory associated with the object stays within a ground truth reachable area; and the non-differentiable prior knowledge reward function returns a negative reward when the sample trajectory exits the reachable area and the ground truth trajectory associated with the object stays within the ground truth reachable area.

In some implementations, the traffic rules can be or include lane infraction rules that prohibit crossing a solid line or entering a lane having an opposite traffic flow direction.

In some implementations, the traffic rules can include adhering to a current traffic control state provided by a traffic control device.

In some implementations, evaluating, for each sample trajectory, the non-differentiable prior knowledge reward function can include determining, for each sample trajectory, whether the sample trajectory intersects with a route associated with an ego vehicle, wherein the reward value is a function of whether the sample trajectory intersects with the route.

In some implementations: the non-differentiable prior knowledge reward function returns a true positive reward value when the sample trajectory intersects the route and a ground truth trajectory associated with the object intersects the route; the non-differentiable prior knowledge reward function returns a false positive reward value when the sample trajectory intersects the route and the ground truth trajectory associated with the object does not intersect the route; the non-differentiable prior knowledge reward function returns a true negative reward value when the sample trajectory does not intersect the route and the ground truth trajectory associated with the object does not intersect the route; and the non-differentiable prior knowledge reward function returns a false positive reward value when the sample trajectory does not intersect the route and the ground truth trajectory associated with the object intersects the route.

In some implementations, the route associated with the ego vehicle can be or include a current motion plan for the ego vehicle.

The gradient approximating unit(s)525can be configured to determine an approximate gradient of an expected loss based at least in part on the plurality of reward values respectively determined for the plurality of sample trajectories. In some implementations, determining the approximate gradient of the expected loss based at least in part on the plurality of reward values respectively determined for the plurality of sample trajectories can include performing a REINFORCE gradient estimation technique.

The model training unit(s)530can be configured to modify one or more values of one or more parameters of the machine-learned object motion prediction model based at least in part on the approximate gradient of the expected loss. In some implementations, modifying the one or more values of the one or more parameters of the machine-learned object motion prediction model based at least in part on the approximate gradient of the expected loss can include modifying the one or more values of the one or more parameters of the machine-learned object motion prediction model based at least in part on a heuristically chosen closest matching mode.

Example Computing Systems

FIG.6depicts a block diagram of an example computing system1000according to example embodiments of the present disclosure. The example system1000includes a computing system1100and a machine learning computing system1200that are communicatively coupled over one or more networks1300.

In some implementations, the computing system1105can perform training of machine learning models and/or predict motion for objects. In some implementations, the computing system1105can be included in an autonomous vehicle. For example, the computing system1105can be on-board the autonomous vehicle. In other implementations, the computing system1105is not located on-board the autonomous vehicle. For example, the computing system1105can operate offline to perform training or predict motion for objects. The computing system1105can include one or more distinct physical computing devices.

The computing system1105can include one or more processors1110and a memory1115. The one or more processors1110can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, a FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. The memory1115can include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, one or more memory devices, flash memory devices, etc., and combinations thereof.

The memory1115can store information that can be accessed by the one or more processors1110. For instance, the memory1115(e.g., one or more non-transitory computer-readable storage mediums, memory devices) can store data1120that can be obtained, received, accessed, written, manipulated, created, and/or stored. The data1120can include, for instance, include examples as described herein. In some implementations, the computing system1100can obtain data from one or more memory device(s) that are remote from the computing system1100.

The memory1115can also store computer-readable instructions1125that can be executed by the one or more processors1120. The instructions1125can be software written in any suitable programming language or can be implemented in hardware. Additionally, or alternatively, the instructions1125can be executed in logically and/or virtually separate threads on processor(s)1110.

For example, the memory1115can store instructions1125that when executed by the one or more processors1110cause the one or more processors1110(the computing system) to perform any of the operations and/or functions described herein, including, for example, insert functions.

According to an aspect of the present disclosure, the computing system1105can store or include one or more machine-learned models1135. As examples, the machine-learned models1135can be or can otherwise include various machine-learned models such as, for example, neural networks (e.g., deep neural networks), support vector machines, decision trees, ensemble models, k-nearest neighbors models, Bayesian networks, or other types of models including linear models and/or non-linear models. Example neural networks include feed-forward neural networks, recurrent neural networks (e.g., long short-term memory recurrent neural networks), convolutional neural networks, or other forms of neural networks.

In some implementations, the computing system1100can receive the one or more machine-learned models1135from the machine learning computing system1200over network(s)1300and can store the one or more machine-learned models1135in the memory1115. The computing system1100can then use or otherwise implement the one or more machine-learned models1135(e.g., by processor(s)1110). In particular, the computing system1100can implement the machine learned model(s)1135to predict motion of objects.

The machine learning computing system1200can include one or more computing devices1205. The machine learning computing system1200can include one or more processors1210and a memory1215. The one or more processors1210can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, a FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. The memory1215can include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, one or more memory devices, flash memory devices, etc., and combinations thereof.

The memory1215can store information that can be accessed by the one or more processors1210. For instance, the memory1215(e.g., one or more non-transitory computer-readable storage mediums, memory devices) can store data1220that can be obtained, received, accessed, written, manipulated, created, and/or stored. The data1220can include, for instance, include examples as described herein. In some implementations, the machine learning computing system1200can obtain data from one or more memory device(s) that are remote from the machine learning computing system1200.

The memory1210can also store computer-readable instructions1225that can be executed by the one or more processors1210. The instructions1225can be software written in any suitable programming language or can be implemented in hardware. Additionally, or alternatively, the instructions1225can be executed in logically and/or virtually separate threads on processor(s)1210.

For example, the memory1215can store instructions1225that when executed by the one or more processors1210cause the one or more processors1210(the computing system) to perform any of the operations and/or functions described herein, including, for example, insert functions.

In some implementations, the machine learning computing system1200includes one or more server computing devices. If the machine learning computing system1200includes multiple server computing devices, such server computing devices can operate according to various computing architectures, including, for example, sequential computing architectures, parallel computing architectures, or some combination thereof.

In addition or alternatively to the model(s)1235at the computing system1100, the machine learning computing system1200can include one or more machine-learned models1235. As examples, the machine-learned models1235can be or can otherwise include various machine-learned models such as, for example, neural networks (e.g., deep neural networks), support vector machines, decision trees, ensemble models, k-nearest neighbors models, Bayesian networks, or other types of models including linear models and/or non-linear models. Example neural networks include feed-forward neural networks, recurrent neural networks (e.g., long short-term memory recurrent neural networks), convolutional neural networks, or other forms of neural networks.

As an example, the machine learning computing system1200can communicate with the computing system1100according to a client-server relationship. For example, the machine learning computing system1200can implement the machine-learned models1235to provide a web service to the computing system1100. For example, the web service can provide training services and/or motion prediction services.

Thus, machine-learned models1135can located and used at the computing system1100and/or machine-learned models1235can be located and used at the machine learning computing system1200.

In some implementations, the machine learning computing system1200and/or the computing system1100can train the machine-learned models1135and/or1140through use of a model trainer1240. The model trainer1240can train the machine-learned models1135and/or1240using one or more training or learning algorithms. One example training technique is backwards propagation of errors. In some implementations, the model trainer1240can perform supervised training techniques using a set of labeled training data. In other implementations, the model trainer1240can perform unsupervised training techniques using a set of unlabeled training data. The model trainer1240can perform a number of generalization techniques to improve the generalization capability of the models being trained. Generalization techniques include weight decays, dropouts, or other techniques.

In particular, the model trainer1240can train a machine-learned model1135and/or1140based on a set of training data1245. The training data1245can include, for example, real world sensor data and associated ground truth data such as ground truth observed trajectories. The model trainer1240can be implemented in hardware, firmware, and/or software controlling one or more processors.

The computing system1100and the machine learning computing system1200can each include a communication interface1130and1250, respectively. The communication interfaces1130/1250can used to communicate with one or more systems or devices, including systems or devices that are remotely located from the computing system1100and the machine learning computing system1200. A communication interface1130/1250can include any circuits, components, software, etc. for communicating with one or more networks (e.g.,1300). In some implementations, a communication interface1130/1250can include, for example, one or more of a communications controller, receiver, transceiver, transmitter, port, conductors, software and/or hardware for communicating data.

The network(s)1300can be any type of network or combination of networks that allows for communication between devices. In some embodiments, the network(s) can include one or more of a local area network, wide area network, the Internet, secure network, cellular network, mesh network, peer-to-peer communication link and/or some combination thereof and can include any number of wired or wireless links. Communication over the network(s)1300can be accomplished, for instance, via a network interface using any type of protocol, protection scheme, encoding, format, packaging, etc.

FIG.6illustrates one example computing system1000that can be used to implement the present disclosure. Other computing systems can be used as well. For example, in some implementations, the computing system1100can include the model trainer1240and the training dataset1245. In such implementations, the machine-learned models1240can be both trained and used locally at the computing system1100. As another example, in some implementations, the computing system1100is not connected to other computing systems.

In addition, components illustrated and/or discussed as being included in one of the computing systems1100or1200can instead be included in another of the computing systems1100or1200. Such configurations can be implemented without deviating from the scope of the present disclosure. The use of computer-based systems allows for a great variety of possible configurations, combinations, and divisions of tasks and functionality between and among components. Computer-implemented operations can be performed on a single component or across multiple components. Computer-implemented tasks and/or operations can be performed sequentially or in parallel. Data and instructions can be stored in a single memory device or across multiple memory devices.

Additional Disclosure

Computing tasks discussed herein as being performed at computing device(s) remote from the vehicle can instead be performed at the vehicle (e.g., via the vehicle computing system), or vice versa. Such configurations can be implemented without deviating from the scope of the present disclosure. The use of computer-based systems allows for a great variety of possible configurations, combinations, and divisions of tasks and functionality between and among components. Computer-implemented operations can be performed on a single component or across multiple components. Computer-implemented tasks and/or operations can be performed sequentially or in parallel. Data and instructions can be stored in a single memory device or across multiple memory devices.

While the present subject matter has been described in detail with respect to specific example embodiments and methods thereof, it will be appreciated that those skilled in the art, upon attaining an understanding of the foregoing can readily produce alterations to, variations of, and equivalents to such embodiments. Accordingly, the scope of the present disclosure is by way of example rather than by way of limitation, and the subject disclosure does not preclude inclusion of such modifications, variations and/or additions to the present subject matter as would be readily apparent to one of ordinary skill in the art.