SYSTEMS AND METHODS FOR VEHICLES NAVIGATING ROADS USING A CONTROL MODEL TRAINED WITH RESIDUAL POLICIES

Systems, methods, and other embodiments described herein relate to an automated vehicle (AV) navigating on roads with a control model trained using residual policies for reducing error. In one embodiment, a method includes generating a generic policy for a control model used to navigate a road having multiple agents with traffic data acquired, the generic policy applying to general traffic scenarios associated with the road. The method also includes training a task policy with reinforcement learning a plurality of residual functions for error reduction of the generic policy, the residual functions factoring parameters about the multiple agents and specific traffic scenarios. The method also includes communicating the generic policy, the task policy for error reduction, and a domain distribution learned by comparing simulated data with the traffic data to a vehicle.

TECHNICAL FIELD

The subject matter described herein relates, in general, to navigating roads by automated vehicles (AV), and, more particularly, to an AV navigating on roads with a control model trained using residual policies for reducing error.

BACKGROUND

Automated vehicles (AV) are rapidly becoming more commonplace for mitigating traffic congestion, curbing emissions, and improving traffic flow. AVs may be equipped with sensors generating data that facilitate perceiving other vehicles, obstacles, pedestrians, and additional aspects of a surrounding environment. For example, a vehicle may be equipped with a light detection and ranging (LIDAR) sensor that uses light to scan the surrounding environment, while logic associated with the LIDAR analyzes acquired data to detect object presence and other features of the surrounding environment. In further examples, additional/alternative sensors such as cameras may be implemented to acquire information about the surrounding environment from which a system derives awareness about aspects of the surrounding environment. This sensor data can be useful in various circumstances for improving perceptions of the surrounding environment so that systems such as automated driving systems (ADS) can perceive the noted aspects and accurately navigate roads.

Moreover, AVs in real environments encounter difficulties navigating complex vehicle interactions, diverse roadway geometries, traffic lights, stop signs, and so on. For example, traffic scenarios involving AVs that are grouped (e.g., cooperative control) encounter unconnected vehicles at intersections causing challenges, such as lane changes due to collision risk. Furthermore, AVs may implement model-based controllers (e.g., model predictive control (MPC)) that rely upon assumptions for a specific scenario and an environment model that is known. If the underlying assumptions are untrue, these models generate results having errors and suboptimal solutions. As such, model-based controllers that are otherwise efficient and reliable become a liability under atypical and rapidly changing traffic scenarios. In one approach, AVs implement machine learning (ML) models for navigating complex tasks. However, ML models may be unable at adapting to new driving scenarios, atypical intersections, and so on. For instance, an ML model trained without snow data underperforms vehicle handling and control during weather conditions having snow, thereby increasing inefficiencies. Therefore, AVs navigating traffic scenarios and intersections that are complex using models for control encounter inefficiencies and safety risks.

SUMMARY

In one embodiment, example systems and methods relate to an automated vehicle (AV) navigating on roads with a control model trained using residual policies for reducing error. In various implementations, systems using control models (e.g., model predictive control (MPC), a neural network (NN), etc.) to navigate traffic scenarios and intersections that are complex encounter difficulties from irregularities. For example, eco-maneuvers at signalized intersections (e.g., a green light optimized speed advisory (GLOSA) function) are systems that adjust speed using signal timing at intersections so that grouped vehicles (e.g., cooperative adaptive cruise control (CACC) vehicles) pass efficiently and safely. However, data about signal timing may be insufficient in complex traffic conditions without factoring traffic queues, weather conditions, operator behavior, and so on. Furthermore, systems may rely on AV as Lagrangian actuators for traffic control rather than actuators having fixed-location (e.g., traffic signals) by influencing human-driven vehicles through mimicking vehicle dynamics. Also known as Lagrangian control, these AVs complicate traffic scenarios from executing irregular maneuvers for efficiency that confuse surrounding traffic. As such, in one approach, reinforcement learning (RL) that is model-free can assist AVs with navigating complex traffic scenarios and intersections. Still, learning control policies from RL that generalize traffic scenarios involving multiple agents is difficult, especially for vehicles implementing Lagrangian control.

Therefore, in one embodiment, a planning system trains a policy for a control model to identify maneuvers by an AV that are energy-efficient for mixed traffic (i.e., including AVs and non-AVs). In particular, the planning system forms a hierarchical arrangement for control with a generic policy and a task policy that adapts to various traffic scenarios, roads, and intersection configurations having multiple agents. Here, the generic policy may be utilized by a data-driven model, an adaptive cruise control (ACC) model, a MPC model, a heuristic control, and so on for outputting motion commands about typical scenarios. The task policy is a model that reduces errors of the motion commands from the generic policy. In one approach, the planning system trains the task policy with RL from residual functions that factor parameters about the multiple agents, thereby having a framework for multi-residual task learning (MRTL). For example, the MRTL for multi-agents decomposes task scenarios into parts that are efficiently solved by the RL using control functions and the residual functions. As such, a complete policy for a task scenario becomes the superposition of two control inputs. Once training is completed, the planning system communicates the generic policy and the task policy to a vehicle for implementing with the control model. Accordingly, the planning system trains the control model with a generic framework through MRTL that generalizes RL computations for efficiency while improving the accuracy of the control model with residual functions adjusting for multi-agent encounters.

In one embodiment, a planning system involving an AV navigating on roads with a control model trained using residual policies for reducing error is disclosed. The planning system includes a memory including instructions that, when executed by a processor, cause the processor to generate a generic policy for a control model used to navigate a road having multiple agents with traffic data acquired, the generic policy applying to general traffic scenarios associated with the road. The instructions also include instructions to train a task policy with reinforcement learning a plurality of residual functions for error reduction of the generic policy, the residual functions factoring parameters about the multiple agents and specific traffic scenarios. The instructions also include instructions to communicate the generic policy, the task policy for error reduction, and a domain distribution learned by comparing simulated data with the traffic data to a vehicle.

In one embodiment, a non-transitory computer-readable medium having an AV navigating on roads with a control model trained using residual policies for reducing error and including instructions that when executed by a processor cause the processor to perform one or more functions is disclosed. The instructions include instructions to generate a generic policy for a control model used to navigate a road having multiple agents with traffic data acquired, the generic policy applying to general traffic scenarios associated with the road. The instructions also include instructions to train a task policy with reinforcement learning a plurality of residual functions for error reduction of the generic policy, the residual functions factoring parameters about the multiple agents and specific traffic scenarios. The instructions also include instructions to communicate the generic policy, the task policy for error reduction, and a domain distribution learned by comparing simulated data with the traffic data to a vehicle.

In one embodiment, a method for an AV navigating on roads with a control model trained using residual policies for reducing error is disclosed. In one embodiment, the method includes generating a generic policy for a control model used to navigate a road having multiple agents with traffic data acquired, the generic policy applying to general traffic scenarios associated with the road. The method also includes training a task policy with reinforcement learning a plurality of residual functions for error reduction of the generic policy, the residual functions factoring parameters about the multiple agents and specific traffic scenarios. The method also includes communicating the generic policy, the task policy for error reduction, and a domain distribution learned by comparing simulated data with the traffic data to a vehicle.

DETAILED DESCRIPTION

Systems, methods, and other embodiments including an automated vehicle (AV) navigating on roads with a control model trained using residual policies for reducing error are disclosed herein. In various implementations, systems utilizing reinforcement learning (RL) train a control model without predefined dynamics, thereby having a model-free approach that mitigates model-based limitations and adapting the control model for driving scenarios that vary. In particular, RL specifies control objectives indirectly within a reward function rather than control actions that are explicit for attaining certain objectives. However, RL may encounter difficulties with driving environments that are non-deterministic and certain traffic scenarios, such as Lagrangian control where AVs act as Lagrangian actuators for traffic control rather than actuators having fixed-location (e.g., traffic signals). In one approach, RL uses residual learning for complex and atypical tasks involving a single-agent. Still, residual learning for a control model lacks accuracy for multi-agent scenarios, such as cooperative control and Lagrangian control.

Therefore, in one embodiment, a planning system generalizes training of a control model (e.g., model predictive control (MPC), a neural network (NN), etc.) using RL across driving scenarios that vary, including those induced by Lagrangian control. In particular, the planning system trains with multi-residual task learning (MRTL) having a generic framework that synergizes RL (e.g., deep RL (DRL)) strengths and task estimation for generalizable control. In one approach, the MRTL decomposes driving scenarios into parts solved by a function (e.g., model-based control, heuristics, etc.) and residuals that improve computational efficiency for RL of the control model. As such, the planning system reduces error for the control model with task synthesis involving a generic policy and a task policy. Here, the generic policy applies to traffic scenarios generally while the planning system trains the task policy with RL of residual functions for error reduction of the generic policy through factoring parameters about multiple agents and specific traffic scenarios. In this way, MRTL reduces error of a generic policy implemented by the control model through learning the task policy with generalization while increasing training efficiency.

Moreover, in various implementations, the planning system learns the task policy with a Markov decision process (MDP) that segments vehicle actions into different components (e.g., tasks, task policies, etc.) for the generic policy and the residual functions. Here, the MDP factors the multiple agents and the specific traffic scenarios that trains the task policy to reduce errors from the generic policy. Furthermore, another component augments the generic policy as an additional enhancement to accuracy. In one approach, the planning system learns an intermediate policy for the generic policy that increases accuracy when safety metrics are unmet. For example, the intermediate policy factors lane geometries about an intersection that alters a complete task to decelerate since the generic policy excluded factoring a wet road reducing stopping distances. Accordingly, the planning system trains the control model with MRTL that generalizes RL computations by including a task policy for residual errors that increases efficiency and improving accuracy for multi-agent encounters.

Referring toFIG.1, an example of a vehicle100is illustrated. As used herein, a “vehicle” is any form of motorized transport. In one or more implementations, the vehicle100is an automobile. While arrangements will be described herein with respect to automobiles, it will be understood that embodiments are not limited to automobiles. In some implementations, a planning system170uses road-side units (RSU), consumer electronics (CE), mobile devices, robots, drones, and so on that benefit from the functionality discussed herein associated with navigating through traffic by an AV with a control model trained using residual policies for reducing error. As further explained below, the planning system170may have parts for the control model that are trained remotely with traffic data acquired from the vehicle100. The trained parts and the traffic data can be communicated over the network interface180for implementing the control model by the vehicle100. In particular, the network interface180can utilize a wireless or wired connection through one of a V2X protocol (e.g., cellular V2X), a modem of the vehicle100, dedicated short-range communications (DSRC) protocol, and so on and receive one or more of the trained parts.

The vehicle100also includes various elements. It will be understood that in various embodiments, the vehicle100may have less than the elements shown inFIG.1. The vehicle100can have any combination of the various elements shown inFIG.1. Furthermore, the vehicle100can have additional elements to those shown inFIG.1. In some arrangements, the vehicle100may be implemented without one or more of the elements shown inFIG.1. While the various elements are shown as being located within the vehicle100inFIG.1, it will be understood that one or more of these elements can be located external to the vehicle100. Furthermore, the elements shown may be physically separated by large distances. For example, as discussed, one or more components of the disclosed system can be implemented within a vehicle while further components of the system are implemented within a cloud-computing environment or other system that is remote from the vehicle100.

Some of the possible elements of the vehicle100are shown inFIG.1and will be described along with subsequent figures. However, a description of many of the elements inFIG.1will be provided after the discussion ofFIGS.2-6for purposes of brevity of this description. Additionally, it will be appreciated that for simplicity and clarity of illustration, where appropriate, reference numerals have been repeated among the different figures to indicate corresponding or analogous elements. In addition, the discussion outlines numerous specific details to provide a thorough understanding of the embodiments described herein. Those of skill in the art, however, will understand that the embodiments described herein may be practiced using various combinations of these elements. In either case, the vehicle100includes a planning system170that is implemented to perform methods and other functions as disclosed herein relating to navigating through traffic by an AV with a control model trained using residual policies for reducing error. As will be discussed in greater detail subsequently, the planning system170, in various embodiments, is implemented partially within the vehicle100, and as a cloud-based service for training.

With reference toFIG.2, one embodiment of the planning system170is further illustrated. The planning system170is shown as including a processor(s)210that may be associated with from the vehicle100ofFIG.1. Accordingly, the processor(s)210may be a part of the planning system170, the planning system170may include a separate processor from the processor(s)110of the vehicle100, or the planning system170may access the processor(s)210through a data bus or another communication path. In one embodiment, the planning system170includes a memory220that stores a policy module230. The memory220is a random-access memory (RAM), a read-only memory (ROM), a hard-disk drive, a flash memory, or other suitable memory for storing the policy module230. The policy module230is, for example, computer-readable instructions that when executed by the processor(s)210cause the processor(s)210to perform the various functions disclosed herein.

With reference toFIG.2, the policy module230generally includes instructions that function to control the processor(s)210to receive data inputs from one or more sensors of the vehicle100, such as over the network interface180. The inputs are, in one embodiment, observations of one or more objects in an environment proximate to the vehicle100and/or other aspects about the surroundings. As provided for herein, the policy module230, in one embodiment, acquires sensor data260that includes at least camera images. In further arrangements, the policy module230acquires the sensor data260from further sensors such as radar sensors123, LIDAR sensors124, and other sensors as may be suitable for identifying vehicles and locations of the vehicles.

Accordingly, the policy module230, in one embodiment, controls the respective sensors to provide the data inputs in the form of the sensor data260. Additionally, while the policy module230is discussed as controlling the various sensors to provide the sensor data260, in one or more embodiments, the policy module230can employ other techniques to acquire the sensor data260that are either active or passive. For example, the policy module230may passively sniff the sensor data260from a stream of electronic information provided by the various sensors to further components within the vehicle100. Moreover, the policy module230can undertake various approaches to fuse data from multiple sensors when providing the sensor data260and/or from sensor data acquired over a wireless communication link. Thus, the sensor data260, in one embodiment, represents a combination of perceptions acquired from multiple sensors.

Furthermore, in one embodiment, the planning system170includes a data store240. In one embodiment, the data store240is a database. The database is, in one embodiment, an electronic data structure stored in the memory220or another data store and that is configured with routines that can be executed by the processor(s)210for analyzing stored data, providing stored data, organizing stored data, and so on. Thus, in one embodiment, the data store240stores data used by the policy module230in executing various functions. In one embodiment, the data store240includes the sensor data260along with, for example, metadata that characterize various aspects of the sensor data260. For example, the metadata can include location coordinates (e.g., longitude and latitude), relative map coordinates or tile identifiers, time/date stamps from when the separate sensor data260was generated, and so on. In one embodiment, the data store240further includes the traffic data250. For example, a server (e.g., a cloud server, edge server, RSU, etc.) acquires the traffic data250from the vehicle100over the network interface180. The traffic data250can include parameters associated with any one of road configurations, intersection configurations, intersection types, lane geometry (e.g., width, curvature, etc.), a number of lanes, a lane bound, bucket availability (e.g., left-turn, right-turn, etc.), signal timing (e.g., phases, durations, permissive left-turn, permitted left-turn, etc.), operator behavior, weather conditions, traffic demand, and so on. The traffic data250may be associated with a road, highway, intersection, and so on. The planning system170can also enhance training by acquiring signal timing data (SPaT) for a signalized intersection associated with a road that identifies signal protocols (e.g., time-to-green), emergency signals, and so on.

Turning now toFIG.3, one embodiment of the planning system170training the control model using generic and residual policies in a hierarchical arrangement300is illustrated. Here, the policy module230includes instructions that cause the processor210to execute the control model with a generic policy310. In various implementations, the control model is any one of a MPC model, a NN, a data-driven model, an adaptive cruise control (ACC) model, a heuristic control, an eco-Lagrangian control model, and so on that outputs motion commands as navigation actions for the vehicle100. The control model may be fixed while factoring driving scenarios with the generic policy310. In one approach, the generic policy310is a function that outputs acceleration and speed for basic tasks by the vehicle100on a road without factoring parameters from the traffic data250. Furthermore, the planning system170can share through a cloud system the generic policy310to the vehicle100for applying toward most road configurations, traffic scenarios, weather conditions, and so on. A policy may be a function accepting an input and outputting an optimal action (e.g., acceleration, maneuver, braking, etc.). In one approach, the planning system170trains and maintains the control model on the server and shares updates with the vehicle100through the network interface180. For example, the updates follow a time interval (e.g., six months, two months, etc.).

Moreover, the configuration specific320layer is a component within the hierarchical arrangement300that the planning system170may utilize to reduce errors of the generic policy310caused by different road and intersection configurations. Here, a model reduces error for the generic policy310and identifies an intermediate policy using specialized controllers that may be model-based controllers, ML-based controllers, data-driven models, and so on. For example, the planning system170trains the configuration specific320layer with policies for 3-leg, 4-leg, and so on intersection configurations that are complex using outputs from specialized controllers in a simulator that refine results for the generic policy310. In one approach, a control model skips processing by the configuration specific320layer and identifies the intermediate policy when the generic policy310has satisfactory policies, such as for various intersection configurations. In another approach, the planning system170trains and maintains the configuration specific320layer on the server and shares updates with the vehicle100through the network interface180. For example, the updates are scheduled with a time interval (e.g., six months, two months, etc.). Accordingly, the planning system170trains the generic policy for robustness with the configuration specific320layer, particularly involving complex and atypical intersections.

Concerning the error reduction330layer, the planning system170reduces the gap between the generic policy310and the optimal policy in complex tasks (e.g., real traffic, weather, multi-agents, etc.) by training a task policy for multi-agent scenarios that factor the traffic data250. Here, the error reduction330layer involves training a task policy to generate optimal and specialized solutions toward various intersections involving different operator behaviors, weather conditions, atypical configurations, demand levels, SPAT, and so on. The gap may exist since the generic policy310layer can forego factoring conditions (e.g., weather), thereby leading to sub-optimal actions. As further explained below, training the task policy to reduce error may involve RL learning residual functions for the generic policy310associated with the control model using the traffic data250, simulated data, and virtual agents (e.g., vehicles) offline on the server. In one approach, the planning system170trains the error reduction330layer on the server and shares updates with the vehicle100. For example, the updates are scheduled with a time interval (e.g., six months, two months, etc.) where the vehicle100receives the generic policy310from upper layers and the task policy from lower layers through the network interface180, thereby reducing error and decreasing the optimality gap associated with specific road types.

Regarding further details on cloud-based training,FIGS.4A and4Billustrate an example of a control model estimating tasks according to road configurations through cloud-based training. AlthoughFIG.4Aillustrates cloud-based training, the planning system170can train agents within simulated environments. Here, the planning system170may include the cloud system410that learns a traffic model with the learning network420through acquiring the traffic data250about 4-leg single-lane, 3-leg single-lane, 3-leg 2-lane, and so on lane configurations. The other configurations430may include additional data about lane configurations that are atypical or uncommon. Furthermore, the cloud system410acquires domain distributions about actual vehicle actions involving lane configurations that are site-specific, thereby improving the accuracy of the generic policy310and MRTL. For example, the domain distribution factors differences between operator behavior at a geographic location from simulated data and the traffic data250. In particular, optimized Task1, Task2, . . . . Task, is learned and associated with 4-leg single-lane, 3-leg single-lane, 3-leg 2-lane, respectively. The vehicle100can use the traffic model and the domain distributions to identify the optimal action (e.g., acceleration, maneuver, braking, etc.) for learning-based control, MPC, and so on. Therefore, the cloud system410learns the generic policy310along with the traffic model and shares the generic policy310and the traffic model with the vehicle100over the network interface180.

In one approach, the cloud system410periodically (e.g., every month) shares the traffic model learned, the generic policy310, domain distributions, and so on with the vehicle100through the network interface180. This task reduces error and decreases the optimality gap in a specific intersection, thereby improving performance of control models. In various implementations, the vehicle100processes the traffic model to identify a base solution using MPC, a data-driven model, and so on for control. Furthermore, the cloud system410can communicate the generic policy310for the vehicle100to compute a base solution. In this way, the generic policy310is kept current through updates of the base solution. The vehicle100uses the received policies and domain distributions to compute improved actions (e.g., acceleration) given input data (e.g., current position, speed, signal timings, etc.). Accordingly, the cloud system410can train generic policies and implement MRTL that improves accuracy for control models and share the generic policy, MRTL, and domain distributions with the vehicle100.

Turning toFIG.5, an example of a comparison between multi-task learning (MTL)510and MRTL520involving various road configurations is illustrated. Here, the planning system170uses RL to train policies with residual learning for outputting vehicle actions (e.g., acceleration) for smoother trips and reduced energy consumption. In various implementations, the MRTL520treats a traffic scenario as a varying task involving multi-agents and follows centralized training and decentralized execution (CTDE) through deep RL (DRL). In RL, an agent learns a control policy by interacting with an environment that can be modeled as a Markov Decision Process (MDP). For example, a MDP is denoted as M=,, p, r, ρ, γ, whererepresents a set of states (e.g., position, velocity, acceleration, dynamics of the adjacent vehicles, context vector, etc.) andrepresents possible actions over certain dimensions and spaces. Regarding other functions, p(st+1|st, αt) denotes the transition probability from a current state stto a next state st+1upon taking action at over a time horizon t, the reward (e.g., sum of a power request and energy consumption) for action at αtstate stis r (st, αt)∈, and a distribution over the initial states is p. In addition, γ∈[0,1] is a discounting factor that balances immediate and future rewards. Given the MDP, the planning system170searches for an optimal policy π*:→that maximizes the cumulative discounted reward expected over the MDP:

Compared to the MTL510, the MRTL520extends the single-MDP (i.e., single task, scenario, etc.) RL to multiple-MDPs (e.g., multiple-tasks, multiple scenarios, etc.) and identifies a unified policy over all MDPs. As such, the planning system170may solve the optimal policy through

whereis a MDP set. The planning system170can generalize the RL across MDPs that originate from a single task, such as eco-Lagrangian control, using contextual MDP (cMDP). Regarding details, cMDP expands upon a MDP framework by incorporating context that parameterizes environmental variations encountered within a task policy, such as changes in lane lengths at different intersections, atypical factors in eco-Lagrangian control, and so on. Mathematically, cMDP can be represented as=,,, pc, rc, ρc, γthat involves a context space, an action space A, and a state space S. Unlike MDP, the transition dynamics pc, reward structure rc, and initial state distribution ρcadapt and vary according to the specific context c∈. In certain respects, a cMDPmay define a MDP collection that individually differ according to contextual factors represented by, such that={Mc}c˜.

Additionally, the planning system170solving a given cMDP can implicate a problem of algorithmic generalization within a task policy or task. In other words, the planning system170searches to find a policy that performs well on the MDPs within the cMDP. The generalization can be stated as follows, where the goal is to find a unified policy π*(·) that performs well on all Mc∈:

The MRTL520framework can readily solve problems associated with cMDPs. Here, the contexts define the different tasks and align with a specific context c∈in Equation (3) corresponding to a task or MDP τ∈in Equation (2).

Referring still toFIG.5, the MTL510may have a unified policy for handling a MDP within the cMDP individually while MRTL decomposes a MDP family within a cMDP into solvable segments that increase computational efficiency and reduce complexity. Here, the segments are governed by the generic policy310and residual components by the error reduction330involving RL with one or more controls. For example, the planning system170trains a task policy by segmenting vehicle actions at an intersection forming a set into a first task for the generic policy310and a second task for the residual functions. In this case, the residual functions correct the suboptimality of the generic policy310with the second task that has residual components for the intersection. Furthermore, the MDP factors multiple agents and specific traffic scenarios that train the task policy and the second task augments the generic policy310. As previously explained, the planning system170can also learn an intermediate policy for the generic policy310associated with the control model upon safety metrics (e.g., stopping distance) being unsatisfied for increasing accuracy. The intermediate policy may factor lane geometries about the intersection. Accordingly, the planning system170incorporates a superposition of the two controls with an intermediate policy as needed and generates a task leading to improved training and performance.

In various implementations, the planning system170implements cMDP with eco-Lagrangian control at signalized intersections. For eco-Lagrangian control, systems can rely on AVs as Lagrangian actuators for traffic control rather than actuators having fixed-location (e.g., traffic signals) by influencing human-driven vehicles through mimicking vehicle dynamics to reduce emissions (e.g., mitigate stop-and-go). Here, lane lengths, speed limits, lane count, vehicle inflow rates, timings of traffic signals (e.g., green light, red light, etc.) are parameters. These parameters collectively shape diverse contexts within the cMDP for eco-Lagrangian control involving different signalized intersections having varying geometries, traffic flows, and so on. For example, the planning system170identifies a control policy that is unified for AVs and adeptly curbs emissions at fleet level across signalized intersections for eco-Lagrangian cMDP. MDPs within a cMDP can involve single-agent and multi-agent configurations. However, eco-Lagrangian control concerns the multi-agent paradigm as coordination and interaction between AVs can reduce emissions in an area having human-driven vehicles while overcoming partial observability. The planning system170solves this problem while maintaining or minimizing impact on travel times.

Moreover, given an instantaneous emission model E(·), the planning system170identifies a control policy that is unified for AVs and minimizes the objective in Equation (4):

Here, n represents the total number of vehicles that include AVs and human-driven vehicles. Tidenotes travel time of vehicle i, and vi(t) and αi(t) denote the speed and acceleration at time t, respectively. In addition,denotes the context space that factors a set of signalized intersections.

In one approach, the MRTL520further solves cMDPs by addressing the complexity of combining multiple MDPs within a complete framework for learning. Here, the complete framework includes eco-Lagrangian control and other control models that improve efficiency and decrease emissions. In various implementations, the planning system170training MDPs concurrently encounters competition for the limited capacity of the learning agent, thereby causing difficulties with finding a suitable trade-off between MDP-specific and shared knowledge. Additionally, the MDP dynamics may vary significantly that causes difficulties for a control model to adapt and generalize robustly. Furthermore, unsafe interference from MDPs poses an obstacle to achieving generalization and effectively solving the cMDP. For example, unsafe interference involves learning new MDPs that disrupt the performance of previously learned MDPs. Therefore, the planning system170trains policies with a generic framework through the MRTL520to enhance the algorithmic generalization of RL, thereby solving cMDPs robustly.

As previously explained, the MRTL520unifies a learning approach and harnesses the synergy between the MTL510and learning residual policies. In one approach, the MRTL520augments a given generic policy, which exhibits average performance across various MDPs in a cMDP, through learning residuals on top of the generic policy. These residuals correct suboptimalities within the generic policy310. For example, eco-Lagrangian control at signalized intersections has an overall reward to reduce AV emissions defined as r=rα+rb. Initially, rαrepresents the reward obtained when the AV glides during red traffic signals that can reduce emissions. The variable rbrepresents potential rewards achievable by the AV through dynamically adjusting gliding for a driving environment, such as adapting to other vehicle maneuvers. The planning system170factoring these rewards for a control model may be difficult due to model dynamics that are complex. However, the planning system170training policies to acquire rbis achievable through employing a generic policy that identifies rαwhile the learning approach captures the remaining benefit rb. As such, the MRTL520augments a given generic policy πn:→by learning residuals on top. In particular, the planning system170trains a residual policy πr:→by learning a residual function fr(s):→(e.g., a NN) such that,

where s∈and c∈. In one approach, the residual function fr(s) is learned with a NN where rewards adjust learning parameters. Furthermore, using the ∇ gradient for ∇πr(s)=∇fr(s) means that the gradient of the πr(s) is uncoupled with the πn(s). This allows flexibility and compatibility for the generic policy310with the MRTL520framework for training a control model.

When training multi-residual policies, the planning system170may initially set the error reduction330layer to zero ensuring that the residuals start at zero. This prevents the residuals from adversely affecting the performance of the generic policy310, especially when having sufficient optimality. Additionally, a pre-training phase having a critic spanning multiple iterations helps the critic increasingly understand the generic policy310. In this way, the planning system170generates meaningful estimates instead of producing random values initially during training.

In addition, the learning objectives of the residual function fr(·) may be contingent upon the specific characteristics of an MDP and generic policy πn. In certain MDPs, the generic policy310serves as an initial reference point and provides minimum performance. In these cases, the generic policy310guides the exploration for learning the residual function associated with the error reduction330. On the other hand, the residuals can fine-tune the generic policy310as the policy approaches optimal performance.

Regarding testing the hierarchical arrangement300, the planning system170can factor the features: lane length, vehicle inflow, speed limit, phase time of green signals, and phase time of red signals associated with actual scenarios. As the generic policy310of the MRTL520framework, the planning system170can invoke a heuristic algorithm. For example, Algorithm 1 is a generic policy310for eco-Lagrangian control that can involve multi-agents around an intersection. In one approach, Algorithm 1 factors multi-agents that disrupt the glide through the intersection with MRTL520.

Algorithm 1:1: procedure GLIDE OR KEEP SPEED (vehicle speed v(t), vehicledistance to intersection d(t), traffic signal timing plan T and greenlight duration Tg)2:Calculate⁢time⁢to⁢intersection⁢TI←d⁡(t)v⁡(t)3: Calculate time to green light TGfrom T4: Calculate time to end green light TE← TG+ Tg5: if TG≤ TI≤ TEthen6: Target speed vT← v(t)7: else if TG≥ TIthen8: Calculate target speed based on gliding principle9:vt←d⁡(t)TG10: else11: Target speed vT← vIDM12: return vt13: end procedure

Algorithm 1 avoids idling that increases emissions. Furthermore, Algorithm 1 checks if the vehicle100can pass the intersection when traveling at the current speed. If yes, the generic policy maintains the current speed (lines 5 and 6). If the time remaining to reach the intersection is less than the time until the traffic light turns green, the generic policy initiates a gliding maneuver. This ensures that the vehicle arrives at the intersection when the light transitions to green (lines 7, 8, and 9). In cases where neither of these conditions is satisfied, the policy defaults to a driving approach that is human-like. Accordingly, the planning system170can train the generic policy310for eco-Lagrangian control using residual policies to efficiently maneuver an intersection involving multiple-agents.

Now turning toFIG.6, a flowchart of a method600that is associated with an AV navigating through traffic with a control model trained using residual policies for reducing error is illustrated. Method600will be discussed from the perspective of the planning system170ofFIGS.1and2. While method600is discussed in combination with the planning system170, it should be appreciated that the method600is not limited to being implemented within the planning system170but is instead one example of a system that may implement the method600.

At610, the planning system170generates a generic policy for a control model using traffic data associated with AVs. For example, the vehicle100implements the control model to navigate an intersection having multiple AVs and human-driven vehicles. Here, a policy may be a function accepting an input and outputting an optimal action (e.g., acceleration, maneuver, braking, etc.). As such, the generic policy applies to general scenarios for navigating a road, highway, an intersection, and so on. As previously explained, the control model may be any one of a MPC model, a NN, a data-driven model, an ACC model, a heuristic control, an eco-Lagrangian control model, and so on that outputs motion commands as navigation actions for the vehicle100. In one approach, the generic policy is a function that outputs acceleration and speed for basic tasks by the vehicle100at the intersection. The generic policy may forego factoring parameters from the traffic data when the performance metrics for navigation actions are satisfied. Otherwise, the generic policy can demand further training for navigation actions that are suboptimal or underperform metrics when handling general scenarios with increased complexity.

At620, the planning system170and the policy module230train a task policy using learning with multiple residuals for error reduction of the control model. Here, the planning system170trains the task policy with RL of residual functions that reduces error of the generic policy. These residuals address and correct suboptimalities within the generic policy for control models, such as eco-Lagrangian control that foregoes factoring dynamics associated with traffic circles. Unlike the generic policy, the residual functions may factor parameters about multiple agents, traffic scenarios that are specific, and atypical traffic. In one approach, the planning system170invokes MRTL that decomposes a MDP family within a cMDP into solvable segments associated with tasks for atypical traffic having multiple agents, thereby increasing efficiency. The generic policy governs the segments while a layer for error reduction handles residuals as two controls. In another approach, the planning system170trains a task policy with MRTL by segmenting vehicle actions at an intersection forming a set into a first task for the generic policy and a second task for the residual functions. In this way, the residual functions correct the suboptimality of the generic policy with the second task that has residual components for the intersection while identifying the generic policy.

Moreover, the planning system170learns an intermediate policy for the generic policy associated with the control model for increasing accuracy when safety metrics for the generic policy are unsatisfied. Here, the intermediate policy factors certain lane geometries about the intersection before policy training for error reduction, such as training associated with atypical traffic. Accordingly, the planning system170can compute a complete task for the vehicle100using the control model through a superposition with the task policy and the generic policy inputs that improves AV maneuvers and traffic efficiency.

At630, the planning system170communicates the generic policy, the task policy, and domain distribution learned to the vehicle100. Here, the planning system170may learn the domain distribution by comparing simulated data with the traffic data, thereby closing gaps about actual environments after training. In other words, the planning system170adapts the domain using actual and simulation worlds by comparing distributions. In particular, the comparison involves calculations that reduce mismatches and discrepancies about road geometries, operator behavior, and so on. Accordingly, the planning system170invokes MRTL that generalizes RL computations for efficiency while improving the accuracy of generic policies associated with the control model through residual functions.

FIG.1will now be discussed in full detail as an example environment within which the system and methods disclosed herein may operate. In some instances, the vehicle100is configured to switch selectively between different modes of operation/control according to the direction of one or more modules/systems of the vehicle100. In one approach, the modes include:0, no automation;1, driver assistance;2, partial automation;3, conditional automation;4, high automation; and5, full automation. In one or more arrangements, the vehicle100can be configured to operate in a subset of possible modes.

In one or more arrangements, the map data116can include one or more terrain maps117. The terrain map(s)117can include information about the terrain, roads, surfaces, and/or other features of one or more geographic areas. The terrain map(s)117can include elevation data in the one or more geographic areas. The terrain map(s)117can define one or more ground surfaces, which can include paved roads, unpaved roads, land, and other things that define a ground surface.

As noted above, the vehicle100can include the sensor system120. The sensor system120can include one or more sensors. “Sensor” means a device that can detect, and/or sense something. In at least one embodiment, the one or more sensors detect, and/or sense in real-time. As used herein, the term “real-time” means a level of processing responsiveness that a user or system senses as sufficiently immediate for a particular process or determination to be made, or that enables the processor to keep up with some external process.

In arrangements in which the sensor system120includes a plurality of sensors, the sensors may function independently or two or more of the sensors may function in combination. The sensor system120and/or the one or more sensors can be operatively connected to the processor(s)110, the data store(s)115, and/or another element of the vehicle100. The sensor system120can produce observations about a portion of the environment of the vehicle100(e.g., nearby vehicles).

The sensor system120can include any suitable type of sensor. Various examples of different types of sensors will be described herein. However, it will be understood that the embodiments are not limited to the particular sensors described. The sensor system120can include one or more vehicle sensors121. The vehicle sensor(s)121can detect information about the vehicle100itself. In one or more arrangements, the vehicle sensor(s)121can be configured to detect position and orientation changes of the vehicle100, such as, for example, based on inertial acceleration. In one or more arrangements, the vehicle sensor(s)121can include one or more accelerometers, one or more gyroscopes, an inertial measurement unit (IMU), a dead-reckoning system, a global navigation satellite system (GNSS), a global positioning system (GPS), a navigation system147, and/or other suitable sensors. The vehicle sensor(s)121can be configured to detect one or more characteristics of the vehicle100and/or a manner in which the vehicle100is operating. In one or more arrangements, the vehicle sensor(s)121can include a speedometer to determine a current speed of the vehicle100.

Alternatively, or in addition, the sensor system120can include one or more environment sensors122configured to acquire data about an environment surrounding the vehicle100in which the vehicle100is operating. “Surrounding environment data” includes data about the external environment in which the vehicle is located or one or more portions thereof. For example, the one or more environment sensors122can be configured to sense obstacles in at least a portion of the external environment of the vehicle100and/or data about such obstacles. Such obstacles may be stationary objects and/or dynamic objects. The one or more environment sensors122can be configured to detect other things in the external environment of the vehicle100, such as, for example, lane markers, signs, traffic lights, traffic signs, lane lines, crosswalks, curbs proximate the vehicle100, off-road objects, etc.

As an example, in one or more arrangements, the sensor system120can include one or more of: radar sensors123, LIDAR sensors124, sonar sensors125, weather sensors, haptic sensors, locational sensors, and/or one or more cameras126. In one or more arrangements, the one or more cameras126can be high dynamic range (HDR) cameras, stereo, or infrared (IR) cameras.

The vehicle100can include an input system130. An “input system” includes components or arrangement or groups thereof that enable various entities to enter data into a machine. The input system130can receive an input from a vehicle occupant. The vehicle100can include an output system135. An “output system” includes one or more components that facilitate presenting data to a vehicle occupant.

The vehicle100can include one or more vehicle systems140. Various examples of the one or more vehicle systems140are shown inFIG.1. However, the vehicle100can include more, fewer, or different vehicle systems. It should be appreciated that although particular vehicle systems are separately defined, any of the systems or portions thereof may be otherwise combined or segregated via hardware and/or software within the vehicle100. The vehicle100can include a propulsion system141, a braking system142, a steering system143, a throttle system144, a transmission system145, a signaling system146, and/or a navigation system147. Any of these systems can include one or more devices, components, and/or a combination thereof, now known or later developed.

The processor(s)110, the planning system170, and/or the automated driving module(s)160can be operatively connected to communicate with the various vehicle systems140and/or individual components thereof. For example, the processor(s)110and/or the automated driving module(s)160can be in communication to send and/or receive information from the various vehicle systems140to control the movement of the vehicle100. The processor(s)110, the planning system170, and/or the automated driving module(s)160may control some or all of the vehicle systems140and, thus, may be partially or fully autonomous as defined by the society of automotive engineers (SAE) levels 0 to 5.

The processor(s)110, the planning system170, and/or the automated driving module(s)160can be operatively connected to communicate with the various vehicle systems140and/or individual components thereof. For example, the processor(s)110, the planning system170, and/or the automated driving module(s)160can be in communication to send and/or receive information from the various vehicle systems140to control the movement of the vehicle100. The processor(s)110, the planning system170, and/or the automated driving module(s)160may control some or all of the vehicle systems140.

The processor(s)110, the planning system170, and/or the automated driving module(s)160may be operable to control the navigation and maneuvering of the vehicle100by controlling one or more of the vehicle systems140and/or components thereof. For instance, when operating in an autonomous mode, the processor(s)110, the planning system170, and/or the automated driving module(s)160can control the direction and/or speed of the vehicle100. The processor(s)110, the planning system170, and/or the automated driving module(s)160can cause the vehicle100to accelerate, decelerate, and/or change direction. As used herein, “cause” or “causing” means to make, force, compel, direct, command, instruct, and/or enable an event or action to occur or at least be in a state where such event or action may occur, either in a direct or indirect manner.

The vehicle100can include one or more actuators150. The actuators150can be an element or a combination of elements operable to alter one or more of the vehicle systems140or components thereof responsive to receiving signals or other inputs from the processor(s)110and/or the automated driving module(s)160. For instance, the one or more actuators150can include motors, pneumatic actuators, hydraulic pistons, relays, solenoids, and/or piezoelectric actuators, just to name a few possibilities.

In one or more arrangements, one or more of the modules described herein can include artificial intelligence elements, e.g., neural network, fuzzy logic, or other machine learning algorithms. Furthermore, in one or more arrangements, one or more of the modules can be distributed among a plurality of the modules described herein. In one or more arrangements, two or more of the modules described herein can be combined into a single module.

The systems, components, and/or processes described above can be realized in hardware or a combination of hardware and software and can be realized in a centralized fashion in one processing system or in a distributed fashion where different elements are spread across several interconnected processing systems. Any kind of processing system or another apparatus adapted for carrying out the methods described herein is suited. A typical combination of hardware and software can be a processing system with computer-usable program code that, when being loaded and executed, controls the processing system such that it carries out the methods described herein.

The systems, components, and/or processes also can be embedded in a computer-readable storage, such as a computer program product or other data programs storage device, readable by a machine, tangibly embodying a program of instructions executable by the machine to perform methods and processes described herein. These elements also can be embedded in an application product which comprises the features enabling the implementation of the methods described herein and, which when loaded in a processing system, is able to carry out these methods.