SYSTEMS AND METHODS FOR GENERATING UNIFORM FRAMES HAVING SENSOR AND AGENT DATA

System, methods, and other embodiments described herein relate to a manner of generating and relating frames that improves the retrieval of sensor and agent data for processing by different vehicle tasks. In one embodiment, a method includes acquiring sensor data by a vehicle. The method also includes generating a frame including the sensor data and agent perceptions determined from the sensor data at a timestamp, the agent perceptions including multi-dimensional data that describes features for surrounding vehicles of the vehicle. The method also includes relating the frame to other frames of the vehicle by track, the other frames having processed data from various times and the track having a predetermined window of scene information associated with an agent. The method also includes training a learning model using the agent perceptions accessed from the track.

TECHNICAL FIELD

The subject matter described herein relates, in general, to generating frames having vehicle data, and, more particularly, to generating and relating frames that improves the retrieval of sensor and agent data for processing by different vehicle tasks.

BACKGROUND

Vehicles have sensors providing data that facilitate perceiving agents (e.g., other vehicles), obstacles, pedestrians, and additional aspects of a surrounding environment. For example, a vehicle with a light detection and ranging (LIDAR) sensor uses light to scan the surrounding environment, while logic associated with the LIDAR analyzes acquired data to detect a presence of objects and other features of the surrounding environment. In further examples, cameras acquire information about the surrounding environment from which a system derives awareness about aspects of the surrounding environment. This sensor data is useful for improving perceptions of the surrounding environment so that systems such as automated driving systems can perceive the noted aspects for accurately controlling the vehicle.

In general, the further awareness is developed by the vehicle about a surrounding environment, the better an operator can be supplemented with information for driving assistance or the better an automated system can control the vehicle to avoid hazards. However, a system organizing the sensor and other data acquired from multiple sources for retrieval by vehicle tasks creates difficulties. For example, the data from a LIDAR sensor and perception system is associated with different semantics for an environment surrounding a vehicle. As such, a system organizing this information by frame across time can decrease bandwidth due to size, such as when LIDAR data is retrieved together with unnecessary perception data.

SUMMARY

In one embodiment, example systems and methods relate to a manner of generating and relating frames that improves the retrieval of sensor and agent data for processing by different vehicle tasks. In various implementations, systems acquiring and assembling data from various vehicle sources or systems lead to processing inefficiencies. For example, a system organizing this information by frame across time can decrease bandwidth by retrieving unnecessary versions or forms of data from memory. Therefore, in one embodiment, a control system implements a unified schema (e.g., an organization, format, etc.) for sensor and agent data from a vehicle that provides efficient storage, retrieval, and versioning for vehicle tasks (e.g., motion planning, object tracking, etc.). In particular, the control system generates a frame including sensor data and agent perceptions at a timestamp. Here, the agent perceptions are derived from the sensor data and may include two or three-dimensional data describing features (e.g., relative size) for surrounding vehicles. Furthermore, the system relates the frame to other frames generated at different times by track. In this way, a vehicle task can selectively retrieve sensor data or agent perceptions by type, time, and so on in a track for processing, thereby avoiding the inefficient retrieval of unnecessary data. Accordingly, the control system generates and relates frames for improving bandwidth usage while serving requests by diverse vehicle tasks.

In one embodiment, a control system for generating and relating frames that improves the retrieval of sensor and agent data for processing by different vehicle tasks is disclosed. The control system includes a memory storing instructions that, when executed by a processor, cause the processor to acquire sensor data by a vehicle. The instructions also include instructions to generate a frame including the sensor data and agent perceptions determined from the sensor data at a timestamp, the agent perceptions including multi-dimensional data that describes features for surrounding vehicles of the vehicle. The instructions also include instructions to relate the frame to other frames of the vehicle by track, the other frames having processed data from various times and the track having a predetermined window of scene information associated with an agent. The instructions also include instructions to train a learning model using the agent perceptions accessed from the track.

In one embodiment, a non-transitory computer-readable medium for generating and relating frames that improves the retrieval of sensor and agent data for processing by different vehicle tasks 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 acquire sensor data by a vehicle. The instructions also include instructions to generate a frame including the sensor data and agent perceptions determined from the sensor data at a timestamp, the agent perceptions including multi-dimensional data that describes features for surrounding vehicles of the vehicle. The instructions also include instructions to relate the frame to other frames of the vehicle by track, the other frames having processed data from various times and the track having a predetermined window of scene information associated with an agent. The instructions also include instructions to train a learning model using the agent perceptions accessed from the track.

In one embodiment, a method for generating and relating frames that improves the retrieval of sensor and agent data for processing by different vehicle tasks is disclosed. In one embodiment, the method includes acquiring sensor data by a vehicle. The method also includes generating a frame including the sensor data and agent perceptions determined from the sensor data at a timestamp, the agent perceptions including multi-dimensional data that describes features for surrounding vehicles of the vehicle. The method also includes relating the frame to other frames of the vehicle by track, the other frames having processed data from various times and the track having a predetermined window of scene information associated with an agent. The method also includes training a learning model using the agent perceptions accessed from the track.

DETAILED DESCRIPTION

Systems, methods, and other embodiments associated with generating and relating frames that improves the retrieval of sensor and agent data for processing by different vehicle tasks are disclosed herein. In various implementations, vehicle systems request the retrieval of sensor data and agent data (e.g., ado data) in different forms for processing tasks. For example, a deep learning model processes data in a block of multiple frames for a driving scene. On the contrary, a reinforcement learning model may process data per frame in a sequence. As such, a control system managing the storage and retrieval of data leads to inefficiencies or incompatibility when more data than requested is retrieved or retrieval is limited to a frame size (e.g., three frames). Therefore, in one embodiment, a control system acquires, organizes, and structures sensor and agent data associated with a vehicle using an agent-centric schema for versatile retrieval. In particular, the control system acquires data from vehicle sensors and relates raw data, processed light detection and ranging (LIDAR) data (e.g., point clouds, 3D annotations, etc.), and processed image data (e.g., object labels, semantic segmentations, etc.) as datums. Here, a datum can be structured or synchronized data from disparate sources grouped by frames, slices, tracks, and so on. In one approach, a track is a window of frames for a particular agent and a slice is information from a frame for multiple agents at a timestamp. In this way, the control system can retrieve frames flexibly by specific agents, agent type (e.g., 2D agent, 3D agent, etc.), and so on.

Moreover, the control system creates an agent datum having data for 2D and 3D agents. This data includes coordinates for ado vehicles, feature snapshots, and so on. The control system also forms a map datum in the frame as an integrated dataset that simplifies access for vehicle tasks. For example, the dataset is a schema that facilitates training of a map encoder for a ML model through various samples. As such, elements of the map datum can include a map message, dynamic traffic control elements (TCE), zone information, data from traffic lights, and so on structured for selective retrieval by track or slice to a system request.

In various implementations, the control system can apply the agent-centric schema to train a learning model using tracks. For example, the agent-centric schema allows an encoder of the learning model to selectively process agent information by track or sensor data cached in memory. This avoids organizing frames sequentially as part of a preprocessing operation for training operations by the learning model. In particular, the control system may relate consecutive frames in tracks or slices using pointers, thereby allowing non-sequential or random access directly within tracks. For example, tracks include 2D or 3D agent features accessible by blocks of multiple frames associated with a driving scene by time span (e.g., three seconds). Accordingly, the control system uses datums, tracks, and slices for retrieving sensor or agent data from a frame(s) non-sequentially or selectively instead of through a general query, thereby improving bandwidth efficiency.

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 control system uses road-side units (RSU), consumer electronics (CE), mobile devices, robots, drones, and so on that benefit from the functionality discussed herein associated with generating and relating frames that improve the retrieval of sensor and agent data (e.g., data for ado vehicles) for processing by different vehicle tasks.

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.

With reference toFIG.2, one embodiment of the control system170ofFIG.1is further illustrated. The control system170is shown as including a processor(s)110from the vehicle100ofFIG.1. Accordingly, the processor(s)110may be a part of the control system170, the control system170may include a separate processor from the processor(s)110of the vehicle100, or the control system170may access the processor(s)110through a data bus or another communication path. In one embodiment, the control system170includes a memory210that stores a generation module220. The memory210is a random-access memory (RAM), a read-only memory (ROM), a hard-disk drive, a flash memory, or other suitable memory for storing the generation module220. The generation module220is, for example, computer-readable instructions that when executed by the processor(s)110cause the processor(s)110to perform the various functions disclosed herein.

The control system170as illustrated inFIG.2is generally an abstracted form of the control system170having modules. Furthermore, the generation module220generally includes instructions that function to control the processor(s)110to receive data inputs from one or more sensors of the vehicle100. 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 generation module220, in one embodiment, acquires sensor data250that includes at least camera images. In further arrangements, the generation module220acquires the sensor data250from 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 generation module220, in one embodiment, controls the respective sensors to provide the data inputs in the form of the sensor data250. Additionally, while the generation module220is discussed as controlling the various sensors to provide the sensor data250, in one or more embodiments, the generation module220can employ other techniques to acquire the sensor data250that are either active or passive. For example, the generation module220passively sniffs the sensor data250from a stream of electronic information provided by the various sensors to further components within the vehicle100. Moreover, the generation module220can undertake various approaches to fuse data from multiple sensors when providing the sensor data250and/or from sensor data acquired over a wireless communication link. Thus, the sensor data250, in one embodiment, represents a combination of perceptions acquired from multiple sensors.

Moreover, in one embodiment, the control system170includes a data store230. In one embodiment, the data store230is a database. The database is, in one embodiment, an electronic data structure stored in the memory210or another data store and that is configured with routines that can be executed by the processor(s)110for analyzing stored data, providing stored data, organizing stored data, and so on. Thus, in one embodiment, the data store230stores data used by the generation module220in executing various functions. In one embodiment, the data store230includes the sensor data250along with, for example, metadata that characterizes various aspects of the sensor data250. 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 data250was generated, and so on. In one embodiment, the data store230further includes the agent data240and the map data260. The agent data240may form a datum from perceptions derived by the vehicle100using the sensor data250. In one approach, a datum can be structured or synchronized data from various and disparate sources grouped by frames, slices, tracks, and so on. In particular, the control system may synchronize data according to scene changes, agent changes, quality metrics, and so on.

In various implementations, the perceptions include 2D or 3D data that describe features (e.g., relative size) for surrounding vehicles (i.e., agents) of the vehicle100. Furthermore, the map data260can have geographical information, TCE states, road characteristics (e.g., lane orientation, intersection configurations, etc.), and so on. A TCE describes a stop sign, states for bulbs associated with a traffic light, and so on.

The generation module220, in one embodiment, is further configured to perform additional tasks beyond controlling the respective sensors to acquire and provide the sensor data250. For example, the generation module220includes instructions that cause the processor110to generate a frame including the agent data240, the sensor data250, and the map data260using datums. Here, the agent data240may be agent perceptions that include multi-dimensional data that describe features determined from the sensor data250at a timestamp for surrounding vehicles. Furthermore, a frame can represent a snapshot for one or more agents associated with a vehicle state or driving scene.

Furthermore, the generation module220relates the frame to other frames of the vehicle100. A frame may be structured atomically with discrete datums having the sensor data250and the agent perceptions as snapshots in time. In this way, the control system170can provide an agent-centric schema (e.g., an organization, format, etc.) for vehicle and map data that is system agnostic and avoids retrieval errors (e.g., SQL query errors). In particular, the data is organized by the control system170in a unified and comprehensive manner that allows disparate systems to perform diverse processing tasks for the vehicle100by agent. For example, sequential, non-sequential, direct, or random access requests of the data are supported by the control system170due to the frame structure and organization. As such, the control system170supports the processing of data by ML models per frame in sequence (e.g., reinforcement learning, transforms, etc.) as well as systems processing scenes periodically.

In one approach, the generation module220allows multi-mode access of datasets by frame or agent. As such, the control system170supports access by dual-data systems, requests for multi-agent sources (e.g., human annotations, dot tracks, etc.), requests for raw data by agent/time, requests for map data by agent/time, and so on. Furthermore, multi-mode access improves version and software updates to driving stacks by avoiding updating discrete components. Accordingly, the control system170improves input/output efficiency of data and dataset scalability through an agent-centric schema and multi-mode access.

Now turning toFIG.3, one embodiment of the control system170generating and relating frames having various vehicle data for a scene and using tracks or slices is illustrated. The storage and retrieval system300includes frames3101-3having data from samples or snapshots at times t−1, t, and t+1. Here, a frame is extended to include the sensor data250, the agent data240, and the map data260for improving the processing efficiency of vehicle tasks. The sensor data250can include robust raw data for the vehicle100, a depth datum, and an image datum. The depth datum can use LIDAR data for the control system170to derive point clouds3201-3and 3D annotations3301-3describing objects surrounding the vehicle100. For example, the control system170represents a 3D annotation in LIDAR coordinates transformable into earth-centered, earth-fixed (ECEF) coordinates for versatility. The image datum can use images3401-3for the control system170to derive 2D annotations3501-3(e.g., object labels) and semantic segmentations3601-3of the driving scene.

Moreover, the storage and retrieval system300includes an agent datum having 3D agent and 2D agent data (e.g., coordinates for ado vehicles, snapshots of features, etc.) and a map datum having map information. The control system170can use the storage and retrieval system300to access data from a particular frame for Agent 1 robustly as agent schema instead of a general query, thereby improving bandwidth efficiency. In one approach, the control system170also retrieves data associated with multiple agents for a request. For example, the control system170retrieves frames specifically for Agent 1 and Agent 2 from frames t and t+1 in response to a request for data related to these agents. The control system170can also process a request to retrieve data particularly related to 3D agents or 2D agents. For example, Agent 1 has both 3D and 2D data, whereas Agent 2 has 3D data across times t−1, t, and t+1. Furthermore, the frames3101-3can include a feature schema associated with surrounding vehicles to allow requests by particular features.

In various implementations, frames3101-3can include an agent snapshot with features represented by a numerical value, numpy array, link to another frame, and so on. For example, the control system170represents the intent of an ego vehicle by parameters: {yaw_rate, steering_wheel_angle, torque_value, turn_signals_L, turn_signals_R, ego_lateral_offset_wrt_lane_center, lane_curvature, ego_heading_wrt_lane, L_lane_availability, R_lane_availability, throttle_position, brake_position, distance_to_nearest_front_intersection, Rasterized map}. Accordingly, this schema of agents allows comprehensive access to data across types for samples associated with a scene.

Regarding the map data260, the control system170may form a map datum as an integrated and versatile dataset that simplifies access for vehicle tasks, such as machine learning (ML) computations. For example, the dataset represents a schema that facilitates training a map encoder of a ML model through various samples. As such, a map datum can include a map message380(e.g., lane information, vehicle coordinates for ado or ego vehicles, etc.), dynamic TCEs385, zone information (e.g., road junction configurations), data from traffic lights, and so on elements. The map message380can include lane centers, lane boundaries, road boundaries, crosswalks, ramp information, transition information, and so on defined in 3D polyline or polygonal geometries. In one approach, the TCEs385can form a time-varying datum describing states of bulb groups associated with an upcoming traffic light, traffic signs, and so on.FIG.4illustrates an example of a driving scene400having agents410and map information420described by datums. Here, the datum for agents410includes inferences for attributes associated with 2D and 3D agents. Similarly, the datum for the map information420describes the number of lanes, traffic light states, and so on for the driving scene400.

In various implementations, the control system170relates frames by tracks3701and3702for retrieval by vehicle systems that process multiple frames in blocks. A track can have a predetermined window (e.g., three frames) of scene information associated with an agent. As such, a track can include agent data and features according to type (e.g., 3D agents, 2D agents, etc.) across frames, samples, or times. In one approach, track-based access involves the control system170iterating tracks for a driving scene associated with various agents. For context-based access, the control system170iterates tracks for a driving scene associated with each agent.

Moreover, the control system170may slice agent information at a sample, timestamp, or time for context-based access. For example, the control system170iterates a window of frames in a driving scene for a plurality of scenes where a slice has samples across different agents having the same type. In the storage and retrieval system300, this involves slicing 3D agent data to include agents slice375comprising Agent 1 and Agent 2 at sample t+1. Furthermore, a data slice may include 3D agent data and 2D agent data at the timestamp to service data requests across different multi-dimensional agents. Accordingly, the control system170associating frames by tracks or samples by slice improves access speed of cache memory and creates agile data retrieval since systems process data as needed instead of frame-by-frame. This also avoids assembling blocks of frames on-the-fly by learning models, systems controlling vehicle motion, and so on.

FIG.5illustrates one embodiment of training a learning model500using sensor, agent, and map data arranged in datums either online or offline. Here, the training may involve the encoder510and the encoder520that transform agent tracks, sensor data, and map data spanning two seconds of agent history (e.g., location and state) through operations in the latent space. The transformation forms a neuron structure for processing by the attention operator530. Moreover, the attention operator530and the forecast operator540may be neural networks (NN) that transform encoded information for the decoder550to complete inferences associated with agent vehicles. Attention involves focusing on certain parts of the encoded data in a sequence for an object. Forecasting involves estimating future values (e.g., trajectories) of the encoded data for various objects.

In various implementations, the encoder510selectively processes agent tracks or sensor data250cached in memory. This avoids processing and organizing frames sequentially as part of a preprocessing operation for the training by the learning model500as well as during inference, such as during fully automated mode. In particular, the control system170may relate frames in tracks or agents through slices using pointers, thereby allowing non-sequential or random access directly within tracks. This process can include using pointers for the sensor data250associated with the training. For example, tracks include 2D or 3D agent features accessible by blocks of multiple frames associated with a driving scene.

In one approach, the training involves the learning model500accessing or retrieving the agent perceptions from a data slice for the encoder510. Here, the data slice includes information from the frame and another frame at a timestamp or snapshot having derived 3D or 2D perceptions associated with agents (e.g., ado agents). The encoder510extracts features associated with the 3D or 2D agents and the training computes loss functions for tuning the learning model500accordingly.

Turning now toFIG.6, a flowchart of a method600that is associated with generating and relating frames that improves the retrieval of sensor and agent data for processing by different vehicle tasks is illustrated. Method600will be discussed from the perspective of the control system170ofFIGS.1and2. While method600is discussed in combination with the control system170, it should be appreciated that the method600is not limited to being implemented within the control system170but is instead one example of a system that may implement the method600.

At610, the control system170acquires sensor data for a sample or snapshot of a vehicle state. For example, the control system170acquires data from the vehicle sensor(s)121or one or more environment sensors122. The data may be raw data for the vehicle100, LIDAR data, and image data. The depth datum can use LIDAR data for the control system170to derive point clouds and 3D annotations for objects within a vehicle environment. A 3D annotation may be represented in LIDAR coordinates for compatibility with different vehicle systems. Furthermore, the image data can use images from one or more cameras126for the control system170to derive 2D annotations (e.g., object labels) and semantic segmentations of a driving scene.

Moreover, the control system170acquires 3D agent and 2D agent data (e.g., coordinates for ado vehicles, features snapshots, etc.) and map information. As previously explained, map information can include map messages (e.g., lane information, vehicle coordinates for ado or ego vehicles, etc.), dynamic TCEs, zone information (e.g., road junction configurations), data from traffic lights, and so on elements. In particular, map messages can include lane centers, lane boundaries, road boundaries, crosswalks, ramp information, transition information, and so on defined in 3D polyline or polygonal geometries.

At620, the generation module220generates a frame having sensor data and agent perceptions for the sample or snapshot of the vehicle state. In particular, the generation module220forms a depth datum and an image datum using the sensor data associated with an agent-centric schema. In one approach, a datum is structured or synchronized data from various and disparate sources grouped by frames, slices, tracks, and so on. In particular, the control system170may synchronize data according to scene changes, agent changes, quality metrics, and so on. The depth datum can include the point clouds and 3D annotations for objects within the vehicle environment. The image datum can include 2D annotations (e.g., object labels) and semantic segmentations of the driving scene.

Regarding the agent perceptions, the generation module220creates an agent datum having data for 2D and 3D agents. This data includes coordinates for ado vehicles, snapshots of features, and so on. Here, the control system170structures data as datums for retrieving or accessing data from a particular frame non-sequentially, selectively, randomly, and so on instead of through a general query, thereby improving bandwidth efficiency and speed. For example, the control system170retrieves frames inFIG.3specifically for Agent 1 and Agent 2 from frames t and t+1 in response to a request for data related to these agents. In another example, the control system170can also process a request to retrieve data particularly related to 3D agents or 2D agents. In one approach, Agent 1 has both 3D and 2D data whereas Agent 2 has 3D data across times t−1, t, and t+1. In this way, the control system170provides agile retrieval by avoiding frame-by-frame access of the data.

Furthermore, the control system170may form and include a map datum in the frame as an integrated and versatile dataset that simplifies access for vehicle tasks. For example, the dataset acts as a schema that facilitates training a map encoder of a ML model through various samples. As previously explained, a map datum can include a map message, dynamic TCEs, zone information, data from traffic lights, and so on elements for selective retrieval by track or slice in response to a system request.

At630, the control system170relates the frame to other frames by track. This adds versatility for systems that process multiple frames in blocks. In particular, a track may have a predetermined window of scene information associated with an agent. As such, a track can include agent data and features according to type (e.g., 3D agents, 2D agents, etc.) across frames, samples, or times. In one approach, track-based access involves the control system170iterating tracks for a driving scene associated with various agents. For context-based access, the control system170iterates tracks for a driving scene associated with each agent.

In various implementations, the control system170may slice agent information at a sample, timestamp, or time for context-based access. For example, the control system170iterates a window of frames in a scene where a slice has samples across different agents having the same type (e.g., 3D agents). In one approach, a data slice includes 3D agent data and 2D agent data at the timestamp to service data requests across different multi-dimensional agents. Accordingly, the control system170associating frames by tracks or samples by slice allows the processing of data as needed instead of frame-by-frame. Furthermore, this arrangement avoids delays from assembling blocks of frames on-demand.

Regarding an example of leveraging the agent-centric schema, at640the control system170trains a learning model using tracks of data. As previously explained, the agent-centric schema allows an encoder of the learning model to selectively process agent tracks or the sensor data250cached in memory. This avoids organizing frames sequentially as part of a preprocessing operation for training operations by the learning model. In particular, the control system170relates frames in tracks or slices using pointers, thereby allowing non-sequential or random access directly within tracks. For example, tracks include 2D or 3D agent features accessible by blocks of multiple frames associated with a driving scene.

Moreover, in one approach training involves the learning model accessing or retrieving the agent perceptions from a data slice for encoders. Here, the data slice includes the frame and another frame at a timestamp or snapshot having derived 3D or 2D perceptions associated with agents. The encoder510extracts features associated with the 3D or 2D agents and the training computes loss functions for tuning attention, forecast, decoder, and other model processes accordingly.

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; and 5, 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 control 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, returning toFIG.1, 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 control 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 control 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, returning toFIG.1, the processor(s)110, the control 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 control system170, and/or the automated driving module(s)160may control some or all of the vehicle systems140.

The processor(s)110, the control 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 control system170, and/or the automated driving module(s)160can control the direction and/or speed of the vehicle100. The processor(s)110, the control 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.