Accuracy of Predictions on Radar Data using Vehicle-to-Vehicle Technology

This document describes techniques and systems for improving accuracy of predictions on radar data using vehicle-to-vehicle (V2V) technology. V2V communications data and the matching sensor data related to one or more vehicles in the vicinity of a host vehicle are collected. The V2V data is used as label data and the radar data is used as the input data for training the model. The training may either occur onboard the host vehicle or remotely. Further, multiple host vehicles may contribute data to train the model. Once the model has been updated with the included training, the updated model is deployed to the sensor tracking system of the host vehicle. By using the dataset that includes the V2V communications data and the matching sensor data, the updated model may accurately track other vehicles and enable the host vehicle to utilize advanced driver-assistance systems safely and reliably.

BACKGROUND

Advanced safety or driving systems for vehicles may use sensors to track nearby objects. These objects may include other vehicles, pedestrians, and animals, as well as inanimate objects, such as trees and street signs. The sensors (e.g., optical cameras, radar, lidar) collect low-level data that is processed in different ways to estimate positions, trajectories, and movements of the objects. Often, machine-learned models are used to estimate objects in or near a road; predictions are made while sensor data is collected and input to the models. For machine-learned models to quickly and accurately predict object whereabouts and movements in and around a road, the models need to be trained using large complex datasets that address as many different driving scenarios as possible. Manually generating a huge or intricate dataset that is sufficiently detailed and complex to be used for training a machine-learned model that assists with sensor based driving can be a challenging and time consuming task.

SUMMARY

This document describes techniques, systems, and methods for improving accuracy of predictions on radar data using vehicle-to-vehicle (V2V) technology. In one example, a method includes receiving, from a V2V communications platform of a host vehicle, V2V communications data from one or more other vehicles in a vicinity of the host vehicle. The method also includes receiving sensor data generated by a sensor system of the host vehicle indicative of the one or more other vehicles. The method further includes updating a model to generate an updated model by inputting the V2V communications data from the one or more other vehicles as label data for the sensor data, inputting the sensor data to the model, and training the model based on the sensor data and the V2V communications data. The method further includes deploying the updated model to the host vehicle for detecting and tracking objects in the vicinity of the host vehicle.

In another example, a system includes one or more processors configured to receive, from a V2V communications platform of a first vehicle, V2V communications data from one or more other vehicles in a vicinity of the first vehicle. The one or more processors are also configured to receive, from a sensor system of the first host vehicle, sensor data related to the one or more other vehicles. The one or more processors are further configured to update a model to generate an updated model by inputting, to the model, the V2V communications data from the one or more other vehicles as label data for the sensor data, inputting the sensor data to the model, and training the model based on the sensor data and the V2V communications data. The one or more processors are further configured to deploy the updated model to the first vehicle for detecting and tracking objects in the vicinity of the first vehicle. The system may be located remotely from the vehicle.

In another example, a system includes one or more processors configured to receive, from a V2V communications platform of a host vehicle, V2V communications data from one or more other vehicles in a vicinity of the host vehicle. The one or more processors are also configured to receive, from a sensor system of the first host vehicle, sensor data related to the one or more other vehicles. The one or more processors are further configured to update a model to generate an updated model by inputting, to the model, the V2V communications data from the one or more other vehicles as label data for the sensor data, inputting the sensor data to the model, and training the model based on the sensor data and the V2V communications data. The one or more processors are further configured to output the updated model to the host vehicle for detecting and tracking objects in the vicinity of the host vehicle. The system may be located on the host vehicle.

This Summary introduces simplified concepts related to improving accuracy of predictions on radar data using V2V technology, further described in the Detailed Description and Drawings. This Summary is not intended to identify essential features of the claimed subject matter, nor is it intended for use in determining the scope of the claimed subject matter. Although primarily described in the context of improving the accuracy of predictions on radar data using V2V communications data, the techniques for using V2V communications data as ground truth data for training sensor-based models can be applied to other applications where accuracy of trained models are desired. Further, these techniques may also be applied to other communications data such as vehicle-to-infrastructure (V2I), vehicle-to-pedestrian (V2P), and vehicle-to-everything data (V2X).

DETAILED DESCRIPTION

Overview

As vehicles are more often being equipped with autonomous and semi-autonomous systems, machine-learned trained models are being used to assist these systems in object recognition, identification, and/or object tracking applications. The trained models provide a minimum level of accuracy to the sensor systems (e.g., optical cameras, radar, lidar, ultrasonic sensors) and enable the sensor systems to reliably detect and track various objects (e.g., other vehicles, pedestrians, animals, stationary objects such as road signs and vegetation). However, accuracy of the trained models can improve, for example, as the trained models are provided better training data sets (e.g., a larger dataset can account for more object types and driving scenarios). Generating datasets that improve the training of models has been tried, however, several issues remain as obstacles to the creation of the datasets.

One issue is gathering reliable information for the datasets. Sensor data alone may not provide enough accuracy to reliably train the models. However, as recognized by this disclosure, pairing the sensor data with other highly accurate data may overcome this deficiency. For example, sensor data and V2V communications data can be used in combination to improve the accuracy of datasets used to train machine-learned models. While this disclosure primarily describes these training datasets in relation to radar data combined with V2V communications data, the concepts disclosed herein may, likewise, be applied to other combinations of sensor data (e.g., lidar, camera) and other vehicle communications data (e.g., V2I, V2P).

Another issue is finding information sources that can provide values that populate these very large datasets. A solution to this issue, as described herein, is to leverage information gathering already provided by other vehicles or infrastructure near or on the roads. By using data collected by many vehicles in many different environmental and driving scenarios (e.g., urban environments versus rural environments, city scenarios versus non-city scenarios), large quantities of data can be acquired to encompass many different real-world scenarios. By training models with sensor data, in combination with accurate V2V communications data, the models of vehicles may more-accurately or more-quickly detect and track objects and avoid collisions with the objects, than when using models trained in other ways.

This document describes techniques and systems for improving accuracy of predictions on radar data using V2V technology. V2V communications data and the matching sensor data related to one or more vehicles in the vicinity of a host vehicle are collected. The V2V data is used as label data and the radar data is used as the input data for training the model. The training may either occur onboard the host vehicle or remotely (e.g., in a cloud). Further, multiple host vehicles may contribute data to train the model. Once the model has been updated with the included training, the updated model is deployed to the sensor tracking system of the host vehicle. By using the dataset that includes the V2V communications data and the matching sensor data, the updated model may accurately track other vehicles and enable the host vehicle to utilize advanced driver-assistance systems safely and reliably.

Example Environment

FIG.1illustrates an example operating environment100of a host vehicle102that is configured to improve accuracy of predictions on radar data using V2V technology, in accordance with techniques of this disclosure. In the depicted environment100, a V2V system104and a radar system106are mounted to, or integrated within, the host vehicle102. Although illustrated as a car, the host vehicle102can represent other types of vehicles and machinery (e.g., a motorcycle, a bus, a tractor, a semi-trailer truck, watercraft, aircraft, or other heavy equipment) including manned and unmanned systems that may be used for a variety of purposes. The host vehicle102may operate in an autonomous or semi-autonomous mode.

In general, manufacturers can mount V2V and radar sensors/antennas to any moving platform that can travel in the environment100. The sensors/antennas can project their respective field-of-view (FOV) from any exterior surface of the host vehicle102. For example, vehicle manufacturers can integrate at least a part of the radar system106(e.g., the radar sensors/antennas) into a side mirror, bumper, roof, or any other interior or exterior location where the FOV includes a portion of the environment100and objects moving or stationary that are in the environment100. Manufacturers can design the location of the sensors/antennas to provide a particular FOV that sufficiently encompasses portions of the environment100in which the host vehicle102may be traveling. In the depicted implementation, a portion of the V2V system104and a portion of the radar system106are mounted near the front bumper section of the host vehicle102.

The V2V system104can communicate with other vehicles in the environment100that are also equipped with V2V systems. For example, a vehicle108in the environment100includes a V2V system110. The V2V system104and the V2V system110can communicate to one another via a wireless communication link112. The data conveyed via the wireless communication link112by the V2V system104and the V2V system110to one another may include vehicle information such as speed, location, and heading information of their respective vehicles. Likewise, other vehicles (not illustrated) in the environment100that are equipped with V2V systems can transmit similar data and receive similar data from other vehicles with V2V systems. Generally, the V2V communications data represents accurate and precise information about the vehicle from which the data originates.

The radar system106can transmit radar signals (e.g., electromagnetic radiation) that can be reflected off of objects in the environment100(e.g., the vehicle108) and receive the reflected signals (e.g., as receive signals114). In this example, the receive signals114includes radar data (e.g., range data, range rate data, and azimuth data) describing the vehicle108. The radar system106may use this data, often by utilizing a trained model, to predict future driving actions (e.g., acceleration and braking actions, turning actions) of the vehicle108.

In the environment100, the radar system106of the host vehicle102is one type of a sensor-tracking system. In other examples, the techniques described herein may be applied to other types of sensor-tracking systems including camera systems, lidar systems, ultrasonic systems, or any other sensor systems used to identify, track, and/or avoid objects by the host vehicle102in the environment100. Additionally, for simplicity in describing this example, the radar system106includes the sensor and any object detection and tracking features (e.g., object-tracking modules, sensor-fusion modules, models assisting in the implementation of these features). In some examples, the sensor and the object detection and tracking features may be represented by different and/or separate systems.

The V2V communications data received by the V2V system104from the V2V system110and the radar data related to the vehicle108received by the radar system106can be collected by a data set collector module116. The V2V communications data is collected as label data set118. Similarly, the radar data is collected and stored as radar data set120. A model training system122can receive the label data set118and the radar data set120. The model training system122trains or retrains a model to be used by the radar system106in assisting the radar system106in predicting and tracking objects in the environment100. The label data set118can serve as the ground truth and the radar data can be used as input data for training (or retraining) a model. The model training system122can include instructions to be executed by a processor (e.g., electronic control unit (ECU)) installed on the host vehicle102. Alternatively, or additionally, the model training system122(or portions thereof) may be executed remotely (e.g., a cloud environment) to the host vehicle102.

The model training system122uses the label data set118and the radar data set120to update the model for the radar system106. In some implementations, the label data set118and the radar data set120can be an aggregate of label data sets and radar data sets collected by multiple host vehicles. After the model is updated, it can be deployed to the radar system106. Using the updated model, the radar system106may be more accurate and reliable in predicting the actions of objects in the environment100.

Example Systems

FIG.2-1illustrates an example of an automotive system200-1for improving accuracy of predictions on radar data using V2V technology, in accordance with techniques of this disclosure. The automotive system200-1can be integrated within the host vehicle102. For example, the automotive system200-1includes a controller202, a V2V system104-1, a radar system106-1, and a model training system122-1. The V2V system104-1is an example of the V2V system104, the radar system106-1is an example of the radar system106, and the model training system122-1is an example of the model training system122. The V2V system104-1and the radar system106-1can be integrated into an automotive or other vehicular environment. In this example, the model training system122-1is integrated into the host vehicle102. In some implementations, an additional model training system may be remote (e.g., in the cloud) to the vehicle and can train a sensor model concurrent with the model training system122-1, e.g., in an offline fashion.

The V2V system104-1, the radar system106-1, the model training system122-1, and the controller202communicate over a link230. The link230may be a wired or wireless link and in some cases includes a communication bus.

The controller202performs operations based on information received over the link230, such as data output from the V2V system104-1or the radar system106-1as objects in an environment (e.g., the environment100) are identified from the data. The controller202includes an over-the-air (OTA) interface204(optional in this implementation), a processor206-1, and a computer-readable storage media (CRM)208-1(e.g., a memory, long-term storage, short-term storage), which stores instructions for an automotive module210.

The CRM208-1can include a data set collector module212. The data set collector module212can store object information derived from data obtained by the V2V system104-1and the radar system106-1, including the label data set118and the radar data set120illustrated inFIG.1. Alternatively, the data set collector module212may reside on any CRM208integrated within the host vehicle102.

The V2V system104-1includes a V2V antenna214, a processor206-2, and a CRM208-2, which stores host vehicle data216and object data218. The V2V system104-1can transmit, via the V2V antenna214, the host vehicle data216as V2V communications data to other vehicles or objects equipped with V2V systems through the wireless communication link112. Likewise, the V2V system can receive, via the V2V antenna214, V2V communications data from other vehicles equipped with V2V systems as the object data218.

The host vehicle data216and the object data218can include information (e.g., vehicle speed, location, heading, classification) about the respective vehicle and is considered more accurate and precise than sensor data (e.g., radar data). The label data set118of the data set collector module212can be derived from the object data218.

The radar system106-1includes one or more radar sensors220, a processor206-3, and a CRM208-3, which includes radar data222and a trained model224. The CRM208-3also includes instructions for performing sensor operations. The radar system106-1can receive signals (e.g., the receive signals114) reflected from nearby objects and can store the data as the radar data222. The radar system106-1can detect and track objects in the FOV using the radar data222. The radar data222, when input to the trained model224, can provide more accurate and reliable detections and classifications of objects, which the radar system106-1can output to the automotive module210.

The radar data set120of the data set collector module212can be derived from the radar data222. The trained model224may initially be trained using a generic radar data set and a label data set not derived from V2V communications data. As described in this disclosure, subsequent trained models224may include training using the label data set118and the radar data set120.

The model training system122-1includes a processor206-4and a CRM208-4, which stores instructions for a machine learning module226-1. The dedicated processor206-4enables the model training system122-1to train the trained model224without causing disruption to the radar system106-1or any other systems of the host vehicle102. The machine learning module226-1receives a label data set118-1and a radar data set120-1from the data set collector module212. In some aspects, the training will not start until the quantity of data passes a threshold.

Using the label data set118-1as ground truth data and the radar data set120-1as input, the machine learning module226-1can train the trained model224or retrain a previously trained model224and can deploy the updated trained model224to the radar system106-1. In some aspects, before the trained model224is deployed, the trained model224can be compared to another model (e.g., an initially trained model based on generic radar data and labels, an earlier trained model) for accuracy. If the updated trained model224does not outperform the other model, the updated trained model224may not be deployed.

The machine learning module226-1may train the trained model224using machine learning techniques, such as supervised learning, to perform object detection, object tracking, and/or object classification. The trained model224may include one or more artificial neural networks (e.g., long short-term memory (LSTM) networks, recurrent neural networks (RNN), convolution neural networks (CNN)). The output of the trained model224(e.g., predictions about objects) can be used by the automotive module210for driving applications.

Generally, the automotive system200-1executes the automotive module210to perform an automotive function, which may include using output from the radar system106-1. For example, the automotive module210can provide automatic cruise control and monitor the radar system106-1for output that indicates the presence of objects in or near the FOV, for instance, to slow the speed and prevent a rear-end collision with the vehicle108. In such an example, the trained model224can output object information to the automotive module210. The automotive module210may provide alerts or perform a specific maneuver when the data obtained from the radar system106-1indicates that one or more objects are crossing in front of the host vehicle102. By training the trained model224using the radar data222as input and the object data218as ground truth data, the accuracy and reliability of the radar system106-1may be improved.

FIG.2-2illustrates another example of an automotive system200-2for improving accuracy of predictions on radar data using V2V technology, in accordance with techniques of this disclosure. Similar to the automotive system200-1, the automotive system200-2includes the controller202, the V2V system104-1, and the radar system106-1. The controller202includes the OTA interface204through which the controller202can communicate to a model training system122-2. The controller202and the model training system122-2can communicate through wireless link232(e.g., WiFi link, cellular link). In other aspects, the OTA interface204and the data set collector module212may be in a different system or module (e.g., the V2V system104-1, the radar system106-1, the model training system122-1, a dedicated controller (not illustrated) integrated within the host vehicle102). Optionally, the automotive system200-2can, likewise, include a model training system122-1that operates according to the description with respect toFIG.2-1.

As illustrated inFIG.2-2, the model training system122-2is remote (e.g., resides on a cloud-based server) to the rest of the automotive system200-2. The model training system122-2includes an OTA interface228used to communicate on the wireless link232. Similar to the model training system122-1, the model training system122-2includes a processor206-5, a CRM208-5which stores instructions for a machine learning module226-2to train or retrain a trained model224, and the model training system122-2may use the same training methods and techniques as the model training system122-1. Different than the model training system122-1, the machine learning module226-2of the model training system122-2includes a label data set118-2(e.g., truth data) and a radar data set120-2(e.g., input data) that includes more data than that of the model training system122-1. By residing remotely to the automotive system200-2, the model training system122-2can receive input from a plurality of automotive systems200-2. Further, the model training system122-2can include an artificial neural network that is common to each radar system106-1of each automotive system200-2of the plurality of automotive systems200-2. This enables the model training system122-2to use data acquired from each radar system106-1even if each radar system106-1is different or has different properties due to discretions of mounting or calibration between each radar system106-1. The label data set118-2and the radar data set120-2can include an aggregate of data from the plurality of automotive systems200-2. Because each automotive system200-2of the plurality of automotive systems200-2may experience different driving conditions (e.g., dense metropolitan conditions, sparse rural conditions, city streets, interstates, and others), the label data set118-2and the radar data set120-2can contain large amounts of data that includes the different driving conditions. In this manner, the machine learning module226-2can train the trained model224with the large amounts of data that includes the different driving conditions. The updated trained model224may be highly accurate in relation to many different driving conditions in many different environments. This may improve reliability and safety for any vehicle108with the updated trained model224regardless of the environment in which it travels.

Example Implementations

FIG.3illustrates an example implementation300using a local model training system302for improving accuracy of predictions on radar data using V2V technology, in accordance with techniques of this disclosure. A host vehicle304has an automotive system, similar to the automotive system200-1, that includes a V2V system306, a radar system308, and a model training system302(e.g., the model training system122-1) is traveling in an environment that includes objects310-1and310-2. Objects310-1and310-2include V2V systems312-1and312-2, respectively. The V2V systems306and312-1can communicate via the wireless link414-1, and the V2V systems306and312-2can communicate via the wireless link414-2. The radar system308receives reflected signals316-1and316-2from objects310-1and310-2, respectively.

The V2V system306can receive V2V communications data from the objects310-1and310-2that can include the vehicle velocity, location, heading, and classification of the objects310-1and310-2. Likewise, the radar system308can detect radar data from the objects310-1and310-2that complements the V2V communications data of the objects310-1and310-2.

The V2V communications data and the radar data can be collected and retained in a data set collector module. As the host vehicle304encounters different objects310, data from each of the objects310can be collected in the data set collector module. In some aspects when enough data is collected to surpass a threshold, the model training system302can begin the training process on a radar model of the radar system308. The model training system302may not disrupt the operations of the radar system308as the training is performed. Once the radar model is updated, the model training system302can deploy the updated model to the radar system308. The updated model may contain data that accounts for the different driving environments and conditions that the host vehicle304has experienced to the point of the training process. By accumulating V2V communications data and radar data from other vehicles as the host vehicle304encounters them, the data sets used to train the radar model can continue to grow. Larger data sets enable more accurate training. More accurate training may increase the reliability of the radar system308and the safety of the host vehicle304.

FIG.4illustrates another example implementation400using a remote model training system402for improving accuracy of predictions on radar data using V2V technology, in accordance with techniques of this disclosure. The model training system402resides remotely from host vehicles404, including a first group of host vehicles404-1and a second group of host vehicles404-2. That is, the model training system402may reside on a cloud-based server or other computational device that is located outside of any host vehicle404. The host vehicles404may communicate with the model training system402via wireless communications such as a WiFi connection or a cellular connection (e.g., 4G, 5G). The model training system402, similar to the model training system122-2described with reference toFIG.2-2, includes a machine learning module406that stores instructions to train a radar model using a label data set408as label data and a radar data set410as input to the machine learning module406.

In the example implementation400, the host vehicles404-1are traveling in a metropolitan environment412-1with dense traffic, and the host vehicles404-2are traveling in rural environment412-2with less traffic density than the environment412-1. The environments412can represent any environments with many different levels of traffic density and driving conditions. For example, the environments412may represent interstate travel versus traveling on city streets. As the host vehicles404travel in their respective environments412, they can encounter other host vehicles404and other vehicles that include V2V systems (not illustrated) but are not host vehicles.

The host vehicles404are able to communicate with the model training system402. The host vehicles404can collect V2V communications data and radar data from one another and from the other vehicles. The collected V2V communications data and radar data can be transmitted to the model training system402. In some aspects, the data is transmitted from the host vehicles to the model training system after a quantity of data has been collected that exceeds a threshold. The model training system402can receive the data from each of the host vehicles to use as a label data set408and a radar data set410. The label data set408is derived from the collected V2V communications data from each of the host vehicles404, and the radar data set410is derived from the collected radar data from each of the host vehicles404. By collecting data from a plurality of host vehicles404, the label data set408and the radar data set410may each grow to be large. Additionally, because the host vehicles404are traveling in different environments and under different driving conditions, the label data set408and the radar data set410can include a wide variety of traffic conditions.

After the machine learning module406updates a radar model (used by the radar systems of the host vehicles) using the label data set408and the radar data set410, the model training system402can deploy the updated radar model to each of the host vehicles404. The updated radar model can include traffic scenarios not normally traveled by each of the host vehicles404. In other words, the updated radar model can include rural traffic conditions for the host vehicles404-1that may not generally travel in rural areas, and the updated radar model can include metropolitan travel conditions for the host vehicles404-2that may not generally travel in dense traffic. The updated radar model may include a wide variety of traffic conditions and densities. Thus, the updated radar model may provide more reliability and safety for each of the host vehicles404, regardless of the environment in which they may encounter.

Example Methods

FIG.5illustrates an example flowchart500for improving accuracy of predictions on radar data using V2V technology, in accordance with techniques of this disclosure. The operations (or steps)502through514are performed but are not necessarily limited to the order or combinations in which the operations are shown herein. Further, any of one or more of the operations may be repeated, combined, or reorganized to provide other operations.

At step502, V2V communications data and radar data related to an object are received by a host vehicle. The V2V communications data can include classification, location, heading, and speed of the object (e.g., another vehicle). The radar data can include range, range rate, and azimuth of the object.

At step504, the V2V communications data and the radar data are collected and retained in a CRM. The V2V communications data and the radar data are retained until a minimum quantity of V2V communications data and the radar data is collected.

At step506, the quantity of V2V communications data and the radar data is checked. If the quantity exceeds a threshold, then the V2V communications data and the radar data may be used in step508. If the quantity of V2V communications data and the radar data is below the threshold, then more V2V communications data and radar data are collected and the quantity can be rechecked.

At step508, a radar model is updated by training the radar model. The training may be performed using machine learning techniques. The V2V communications are used as truth data (e.g., a label data set). The radar data is used as input for training the model.

At step510, the updated radar model is tested for accuracy. A safety check is performed on the updated radar model with respect to a production model. The safety check includes comparing the updated radar model and the production model for maximum deviation. If the updated radar model outperforms the production model, then the updated model passes the safety check.

At step512, if the updated radar model passes the safety check, the updated model is deemed deployable. If the updated radar model fails the safety check, then the process is repeated from step502until an updated radar model passes the safety check.

At step514, the updated radar model is deployed to the host vehicle. By using a large data set (e.g., the V2V communications data and the radar data) the updated radar model may accurately predict the path and speed of objects in the same environment as the host vehicle. The radar model may enable automotive systems of the host vehicle to make safe driving decisions as it travels in an environment.

FIG.6illustrates an example method for improving accuracy of predictions on radar data using V2V technology, in accordance with techniques of this disclosure. The operations (or steps)602through608are performed but are not necessarily limited to the order or combinations in which the operations are shown herein. Further, any of one or more of the operations may be repeated, combined, or reorganized to provide other operations.

At step602, V2V communications data from one or more other vehicles in a vicinity of a host vehicle is received by the host vehicle. The V2V communications data is generally considered to be accurate and precise data and can include location data, heading data, velocity data, and classification of each of the one or more other vehicles.

At step604, sensor data related to the one or more other vehicles is received by the host vehicle. This disclosure generally describes the sensor data in context to a radar sensor; however, the sensor may be any sensor used by a vehicle to safely travel through an environment. Some examples of other sensors include lidar, cameras, and ultrasonic sensors.

At step606, a model, used by the sensor, is updated to generate an updated model. The model is updated by applying steps606-1through606-3. At step606-1, the V2V communications data is input as label data for the sensor data. At step606-2, the sensor data is input into the model. At step606-3, the model is trained based on the sensor data and the V2V communications data. In some aspects, the model is trained on a dedicated ECU integrated within the host vehicle. A model trained in this manner may include traffic-related information for the environments in which the host vehicle travels. In other aspects, the model is trained remotely to the host vehicle. The V2V communications data and the sensor data used to train the model can include data received by multiple host vehicles traveling in a variety of environments. By including data of multiple host vehicles, the data set (e.g., the label data set and the sensor data set) used to train the model can be very large compared to other available data sets.

At step608, the updated model is deployed to the host vehicle. By receiving large quantities of data and training the model with the large quantities of data, the updated model may accurately predict object-tracking in a variety of driving environments. The host vehicle may use the updated model to safely travel in an environment.

Additional Examples

Example 1: A method comprising: receiving, from a V2V communications platform of a host vehicle, V2V communications data from one or more other vehicles in a vicinity of the host vehicle; receiving sensor data generated by a sensor system of the host vehicle indicative of the one or more other vehicles; updating a model to generate an updated model by: inputting the V2V communications data from the one or more other vehicles as label data for the sensor data; inputting the sensor data to the model; and training the model based on the sensor data and the V2V communications data; and deploying the updated model to the host vehicle for detecting and tracking objects in the vicinity of the host vehicle.

Example 2: The method of example 1, further comprising: operating the host vehicle in an autonomous or semi-autonomous mode based on the updated model.

Example 3: The method of any one of the preceding examples, wherein the updated model is generated by the host vehicle.

Example 4: The method of any one of the preceding examples, wherein updating the model to generate the updated model is performed: utilizing a first processor and a first computer-readable storage media, the first processor and the computer-readable storage media being different from a second processor and a second computer-readable storage media, the second processor and the second computer-readable storage being used for sensor operations of the host vehicle.

Example 5: The method of any one of the preceding examples, wherein: the host vehicle represents a plurality of host vehicles; the updated model is trained, using a common artificial neural network to each sensor system of each host vehicle of the plurality of host vehicles, remotely in relation to the plurality of host vehicles; and the updated model is deployed to the plurality of host vehicles.

Example 6: The method of any one of the preceding examples, wherein the updated model is deployed to the plurality of host vehicles via an over-the-air update.

Example 7: The method of any one of the preceding examples, wherein the updated model is also trained at each of the plurality of host vehicles.

Example 8: A system comprising: one or more processors configured to: receive, from a V2V communications platform of a first vehicle, V2V communications data from one or more other vehicles in a vicinity of the first vehicle; receive, from a sensor system of the first vehicle, sensor data related to the one or more other vehicles; update a model to generate an updated model by: inputting, to the model, the V2V communications data from the one or more other vehicles as label data for the sensor data; inputting the sensor data to the model; and training the model based on the sensor data and the V2V communications data; and deploy the updated model to the first vehicle for detecting and tracking objects in the vicinity of the first vehicle.

Example 9: The system of any one of the preceding examples, wherein: the first vehicle represents a plurality of host vehicles; the updated model is trained remotely in relation to the plurality of host vehicles; and the updated model is deployed to the plurality of host vehicles.

Example 10: The system of any one of the preceding examples, wherein the updated model is deployed to the plurality of host vehicles via an over-the-air update.

Example 11: The system of any one of the preceding examples, wherein the updated model is also trained on a separate system installed on each of the plurality of host vehicles.

Example 12: The system of any one of the preceding examples, wherein at least a first group of host vehicles of the plurality of host vehicles operates in a first environment different from a second environment in which at least a second group of host vehicles of the plurality of host vehicles operates.

Example 13: The system of any one of the preceding examples, wherein the model is updated based on a quantity of the V2V communications data received and the sensor data received exceeding a threshold.

Example 14: The system of any one of the preceding examples, wherein the one or more processors are further configured to: compare the updated model to a production model; and responsive to the updated model outperforming the production model, deploy the updated model.

Example 15: The system of any one of the preceding examples, wherein the V2V communications data for each respective other vehicle comprises: location data; heading data; and velocity data.

Example 16: The system of any one of the preceding examples, wherein the model was previously trained, and wherein the model is continuously trained.

Example 17: The system of any one of the preceding examples, wherein the sensor system comprises a radar system.

Example 18: The system of any one of the preceding examples, wherein the sensor data comprises: range data; range rate data; and azimuth data.

Example 19: A system comprising: one or more processors configured to: receive, from a V2V communications platform of a host vehicle, V2V communications data from one or more other vehicles in a vicinity of the host vehicle; receive, from a sensor system of the host vehicle, sensor data related to the one or more other vehicles; update a model to generate an updated model by: inputting, to the model, the V2V communications data from the one or more other vehicles as label data for the sensor data; inputting the sensor data to the model; and training the model based on the sensor data and the V2V communications data; and output the updated model to the host vehicle for detecting and tracking objects in the vicinity of the host vehicle.

Example 20: The system of any one of the preceding examples, wherein the system is part of the host vehicle.

Example 21: A system comprising means for performing the method of any of the preceding examples.

Example 22: A system comprising at least one processor configured to perform the method of any of the preceding examples.

Example 23: A computer-readable storage media comprising instructions that, when executed, cause a processor to perform the method of any of the preceding examples.

CONCLUSION

While various embodiments of the disclosure are described in the foregoing description and shown in the drawings, it is to be understood that this disclosure is not limited thereto but may be variously embodied to practice within the scope of the following claims. From the foregoing description, it will be apparent that various changes may be made without departing from the spirit and scope of the disclosure as defined by the following claims. Problems associated with collecting large data sets for model training can occur in other systems. Therefore, although described as a way to improve accuracy of predictions on radar data using V2V technology, the techniques of the foregoing description can be applied to other systems that would benefit from building large data sets to be used to train models. Further, these techniques may also be applied to other communications data, such as V2I, V2P, and V2X.