# How Autonomous Cars Process Sensor Data for Driving > Intel's 2020 patent describes a system for autonomous vehicles that cleans and standardizes data from various sensors before using it to perceive the environment and make driving decisions. - **Patent:** US 20220161815 - **Original title:** Autonomous vehicle system - **Owner:** Intel - **Status:** Active - **Times cited:** 72 - **Field:** automotive, consumer_electronics, software, ai_ml ## What it does This patent details an apparatus, likely a computer system within an autonomous vehicle, designed to handle the raw information gathered by the car's sensors, like cameras and lidar. The system first receives this 'sensor data' through an interface. Then, its 'processing circuitry' (the car's computer brain) applies an 'abstraction process' to this data. This process involves cleaning up the data by normalizing its values, correcting distortions through warping, or removing noise via filtering. The goal is to create 'abstracted scene data' that is reliable and consistent. This cleaned-up data is then used in the 'perception phase' of the car's control system, helping it understand what's around it, like other cars, pedestrians, and road signs, to make safe driving decisions. ## What it does NOT cover - Does not cover the specific hardware components of the sensors themselves. - Does not cover the final decision-making or actuation (steering, braking) of the autonomous vehicle, only the data processing leading up to it. - Does not cover systems that do not abstract sensor data before the perception phase. - Does not cover methods of abstracting sensor data that do not involve normalization, warping, or filtering. - Does not cover sensor data from non-autonomous vehicles. ## The clever bit The innovation lies in creating a standardized 'sensor abstraction process' that can handle data from different types of sensors (like cameras and lidar) and even multiple sensors of the same type. This abstraction layer ensures that the perception system receives consistent, high-quality data, regardless of the original sensor's quirks or environmental conditions. ## Real-world examples 1. Autonomous vehicle sensor data processing systems 2. Advanced Driver-Assistance Systems (ADAS) 3. Self-driving car perception modules ## Why it matters This patent is significant because it addresses a fundamental challenge in autonomous driving: making sense of diverse and potentially noisy data from multiple sensors. Standardizing and cleaning this data is crucial for reliable perception, which underpins the safety and functionality of self-driving cars. It represents Intel's effort to provide foundational technology for the burgeoning autonomous vehicle industry. ## Frequently asked questions ### What does How Autonomous Cars Process Sensor Data for Driving cover? Intel's 2020 patent describes a system for autonomous vehicles that cleans and standardizes data from various sensors before using it to perceive the environment and make driving decisions. ### Who owns patent US 20220161815? This patent is owned by Intel. ### When does this patent expire? This patent is expected to expire on March 27, 2040, when the invention enters the public domain. ### What is patent US 20220161815 cited by? This patent has been cited by 72 later patents that build on its ideas. ### What problem does this patent solve? This patent is significant because it addresses a fundamental challenge in autonomous driving: making sense of diverse and potentially noisy data from multiple sensors. Standardizing and cleaning this data is crucial for reliable perception, which underpins the safety and functionality of self-driving cars. It represents Intel's effort to provide foundational technology for the burgeoning autonomous vehicle industry. ### What does this patent NOT cover? Does not cover the specific hardware components of the sensors themselves. **Full plain-English explainer:** https://patentbrief.org/patent/us/20220161815/autonomous-vehicle-system **Original patent:** https://patents.google.com/patent/US20220161815 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [Nvidia's Method for Training Self-Driving Car AI in Simulations](https://patentbrief.org/patent/us/11436484/alphazero) — Nvidia's 2022 patent describes how to train AI for self-driving cars by using simulated environments and virtual sensors, then matching the simulated data format to real-world sensor data for AI processing. - [How Uber Manages Data Connections for Self-Driving Car Fleets](https://patentbrief.org/patent/us/10050760/uber-eats) — A system that splits network traffic for autonomous vehicles by sending heavy data over cheap channels while reserving a highly reliable channel specifically for delivery confirmations. - [Automated AI for Adapting to New Data Without Retraining](https://patentbrief.org/patent/us/20230162023/system-and-method-for-automated-transfer-learning-with-domain-disentanglement) — This patent describes an automated system that builds artificial intelligence models capable of adapting to new, different data without needing full retraining, by learning to ignore irrelevant changes. - [How AI Learns to Control Game Characters Based on Their Surroundings](https://patentbrief.org/patent/us/10607134/artificially-intelligent-systems-devices-and-methods-for-learning-andor-using-an-avatars-circumstances-for-autonomous-avatar-operation) — A system that allows digital characters to automatically perform actions by matching their current environment to previously learned experiences stored in a database. - [How the iRobot Roomba Automatically Cleans Floors Without Falling Down Stairs](https://patentbrief.org/patent/us/6883201/roomba-autonomous-vacuum) — A 2002 patent describing an autonomous floor-cleaning robot that uses sensors to navigate rooms, avoid cliffs like stairs, and sweep debris into a removable bin.