IP Verse

Estimating Object Properties Using Visual Image Data

Patent No. US10956755 (titled "Estimating Object Properties Using Visual Image Data") was filed by Tesla Inc on Feb 19, 2019.

What is this patent about?

’755 is related to the field of autonomous driving systems, specifically addressing the challenge of reducing the number and cost of sensors required for accurate environmental perception. Traditional autonomous vehicles rely on a suite of sensors, including cameras, radar, and lidar, to gather data about their surroundings. The patent aims to minimize the need for expensive emitting distance sensors like lidar by leveraging machine learning techniques.

The underlying idea behind ’755 is to train a machine learning model to estimate the distance of objects from a vehicle using only image data captured by a camera. This is achieved by initially training the model with data from both a camera and an emitting distance sensor (e.g., radar). The correlated output of the distance sensor is used as the ground truth to teach the model how to infer distance directly from visual cues in the camera images.

The claims of ’755 focus on a system and method where image data from a vehicle's camera is fed into a trained machine learning model. The model, having been trained with paired camera and emitting distance sensor data, then outputs the distance to an object in the image. Crucially, the model is designed to output the distance using only the image data , effectively replacing the need for a dedicated distance sensor during operation.

In practice, the system involves a two-stage process. First, a training phase uses a vehicle equipped with both a camera and a distance sensor (like radar) to collect synchronized data. This data is then used to train a machine learning model, such as a convolutional neural network, to associate visual features in the camera images with corresponding distance measurements from the radar. Once trained, this model can be deployed to other vehicles that only have a camera, allowing them to estimate object distances without needing the radar sensor.

This approach differentiates itself from prior solutions by reducing reliance on costly and complex sensor setups. Instead of directly measuring distance with dedicated sensors, the system learns to infer distance from visual information. This can lead to lower vehicle production costs, reduced sensor maintenance, and potentially lower bandwidth requirements for data processing. The trained model can also serve as a redundant distance data source , improving accuracy and fault tolerance even when used in conjunction with a dedicated distance sensor.

How does this patent fit in bigger picture?

Technical landscape at the time

In the late 2010s when ’755 was filed, autonomous driving systems commonly relied on a combination of vision sensors (cameras) and emitting distance sensors (radar, lidar, ultrasonic) to perceive the environment. At a time when sensor fusion was typically implemented using relatively simple data association techniques, hardware and software constraints made it non-trivial to efficiently process and integrate the increasing volume of data from multiple sensors in real-time.

Novelty and Inventive Step

The examiner approved the application because the claims were considered allowable over the prior art of record. The reasons for allowance are those stated in the Applicant's Amendment/Argument filed on October 09, 2020, and the Examiner's previous Non-Final Action mailed out July 09, 2020.

Claims

This patent contains 23 claims, with independent claims numbered 1, 17, 19, and 23. The independent claims are generally directed to a system, a computer program product, and methods for determining the distance to an object using image data and a trained machine learning model. The dependent claims generally elaborate on specific features, components, or steps related to the system, computer program product, and methods described in the independent claims.

Key Claim Terms New

Definitions of key terms used in the patent claims.

Term (Source)Support for SpecificationInterpretation
Distance estimate
(Claim 19)		“Using auxiliary sensor data, such as radar and lidar results, the auxiliary data is associated with objects identified from the vision data to accurately estimate object properties such as object distance. Instead, the training data can be automatically generated and used to train a machine learning model to predict object properties with a high degree of accuracy.”An estimation of the distance of an identified object, extracted from received distance data.
Emitting distance sensor
(Claim 1, Claim 17, Claim 19, Claim 23)		“Emitting distance sensors may emit a signal (e.g., radio signal, ultrasonic signal, light signal, etc.) in detecting a distance of an object from the sensor. For example, a radar sensor mounted to a vehicle emits radar to identify the distance and direction of surrounding obstacles. The distances are then correlated to objects identified in a training image captured from the vehicle's camera.”A sensor that emits a signal to detect the distance of an object from the sensor, and provides a correlated output used to train the machine learning model.
Trained machine learning model
(Claim 1, Claim 17, Claim 19, Claim 23)		“The trained machine learning model can be deployed to vehicles for accurately predicting object properties, such as distance, direction, and velocity, using only vision data. For example, once the machine learning model has been trained to be able to determine an object distance using images of a camera without a need of a dedicated distance sensor, it may become no longer necessary to include a dedicated distance sensor in an autonomous driving vehicle.”A machine learning model that has been trained to output a distance from the vehicle to an object using only image data.
Training image
(Claim 1, Claim 17, Claim 19, Claim 23)		“The associated training image is annotated with the distance measurements and used to train a machine learning model. In some embodiments, the model is used to predict additional properties such as an object's velocity. For example, the velocity of objects determined by radar is associated with objects in the training image to train a machine learning model to predict object velocities and directions.”An image used to train the machine learning model, annotated with a distance estimate extracted from distance data.

Litigation Cases New

US Latest litigation cases involving this patent.

Case NumberFiling DateTitle
2:25-cv-00742Jul 23, 2025Perceptive Automata Llc V. Tesla, Inc.

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US10956755

TESLA INC
Application Number
US16279657
Filing Date
Feb 19, 2019
Status
Granted
Expiry Date
May 11, 2039
External Links
Slate, USPTO, Google Patents