Aurora Labs Limited

Other

Zohar Fox

Carmit Sahar

Oren Sokoler

Kfir Ben Shimon

Disclosed embodiments relate to reporting Electronic Control Unit (ECU) errors or faults to a remote monitoring server. Operations may include receiving operational data from a plurality of ECUs in the vehicle, the operational data being indicative of a plurality of runtime attributes of the plurality of ECUs; generating, through a machine learning process, a statistical model of the operational data; receiving live, runtime updates from the plurality of ECUs in the communications network of the vehicle; identifying an ECU error associated with an ECU in the communications network of the vehicle, the ECU error being determined by a comparison of the live, runtime updates with the statistical model of the operational data to identify at least one deviation from the operational data; and wirelessly sending a report to the remote monitoring server based on the live, runtime updates, the report identifying the ECU and the identified ECU error.

Orchestrator reporting of probability of downtime from machine learning process

Disclosed embodiments relate to perform operations for receiving and integrating a delta file in a vehicle. Operations may include receiving, at an Electronic Control Unit (ECU) in the vehicle, a delta file, the delta file comprising a plurality of deltas corresponding to a software update for software on the ECU and startup code for executing the delta file in the ECU; executing the delta file, based on the startup code, in the ECU; and updating memory addresses in the ECU to correspond to the plurality of deltas from the delta file.

Using data deltas in controllers and managing interdependencies between software versions in controllers using tool chain

Disclosed embodiments relate to identifying Electronic Control Unit (ECU) anomalies in a vehicle. Operations may include monitoring, in the vehicle, data representing real-time processing activity of the ECU; accessing, in the vehicle, historical data relating to processing activity of the ECU, the historical data representing expected processing activity of the ECU; comparing, in the vehicle, the real-time processing activity data with the historical data, to identify at least one anomaly in the real-time processing activity of the ECU; and implementing a control action for the ECU when the at least one anomaly is identified.

Detecting anomalies online using controller processing activity

Disclosed embodiments relate to automatically providing updates to at least one vehicle. Operations may include receiving, at a server remote from the at least one vehicle, Electronic Control Unit (ECU) activity data from the at least one vehicle, the ECU activity data corresponding to actual operation of the ECU in the at least one vehicle; determining, at the server and based on the ECU activity data, a software vulnerability affecting the at least one vehicle, the software vulnerability being determined based on a deviation between the received ECU activity data and expected ECU activity data; identifying, at the server, an ECU software update based on the determined software vulnerability; and sending, from the server, a delta file configured to update software on the ECU with a software update corresponding to the identified ECU software update.

Self-healing learning system for one or more controllers

Disclosed herein are techniques for efficiently providing controller data as part of a maintenance or update process. Techniques include receiving, from a first remote computing device, a message associated with at least one controller extracting, based on the received message, an image of software associated with the at least one controller; accessing, based on the extracted image, a delta file; and transmitting the accessed delta file to a second remote computing device.

Efficient controller data generation and extraction

Disclosed herein are techniques for analyzing control-flow integrity based on functional line-of-code behavior and relation models. Techniques include receiving data based on runtime operations of a controller; constructing a line-of-code behavior and relation model representing execution of functions on the controller based on the received data; constructing, based on the line-of-code behavioral and relation model, a dynamic control flow integrity model configured for the controller to enforce in real-time; and deploying the dynamic control flow integrity model to the controller.

Verifying integrity of controller software updates

Disclosed embodiments relate to adjusting vehicle Electronic Control Unit (ECU) software versions. Operations may include receiving a prompt to adjust an ECU of a vehicle from executing a first version of ECU software to a second version of ECU software; configuring, in response to the prompt and based on a delta file corresponding to the second version of ECU software, the second version of ECU software on the ECU in the vehicle for execution; and configuring, in response to the prompt, the first version of ECU software on the ECU in the vehicle to become non-executable.

Roll back of data delta updates

Disclosed herein are techniques for visualizing and configuring controller function sequences. Techniques include identifying at least one executable code segment associated with a controller; analyzing the at least one executable code segment to determine at least one function and at least one functional relationship associated with the at least one code segment; constructing, a software functionality line-of-code behavior and relation model visually depicting the determined at least one function and at least one functional relationship; displaying the software functionality line-of-code behavior and relation model at a user interface; receiving a first input at the interface; in response to the received first input, animating the line-of-code behavior and relation model to visually depict execution of the at least one executable code segment on the controller; receiving a second input at the interface; and in response to the received second input, animating an update to the line-of-code behavior and relation model.

Visualization of code execution through line-of-code behavior and relation models

Disclosed herein are techniques for identifying software dependencies based on functional line-of-code behavior and relation models. Techniques include accessing a first line-of-code behavior and relation model representing execution of functions of a first portion of executable code, the first portion of executable code being associated with a first symbol; detecting a change to the first portion of executable code; constructing, based on the changed first portion of executable code, a second line-of-code behavior and relation model representing execution of functions of the changed first portion of executable code; determining, based on the constructed second model, a dependency between (i) the changed first portion of executable code or the first symbol and (ii) a second symbol; and generating, based on the determined difference, a report identifying the dependency.

Identifying software dependencies using controller code models

Disclosed herein are techniques for analyzing software delta changes based on functional line-of-code behavior and relation models. Techniques include identifying a prompt to change a first version of code on a controller to a second version of code; constructing, based on the identified prompt, a line-of-code behavior and relation model representing execution of functions of the controller based on the second version of code; performing a signature operation on the generated line-of-code behavior and relation model to produce a signature value; and sending the signature value to the controller; wherein the controller is configured to compare the signature value to a computed signature value that the controller is configured to compute based on the second version of code and determine, based on the comparison, whether to validate the second version of code.

Disclosed herein are techniques for using a line-of-code behavior and relation model to determine software functionality changes. Techniques include identifying a first portion of executable code and a second portion of executable code; accessing a first line-of-code behavior and relation model representing execution of functions of the first portion of executable code; constructing, based on the second portion of executable code, a second line-of-code behavior and relation model representing execution of functions of the second portion of executable code; performing a functional differential comparison of the first line-of-code behavior and relation model to the second line-of-code behavior and relation model; determining, based on the functional differential comparison, a status of functional equivalence between the first portion of executable code and the code portion of executable code; and generating, based on the determined difference, a report identifying the status of functional equivalence.

Proving whether software functionality has changed following a software change

Disclosed herein are techniques for identifying software interdependencies based on functional line-of-code behavior and relation models. Techniques include identifying a first portion of executable code associated with a first controller; accessing a functional line-of-code behavior and relation model representing functionality of the first portion of executable code and a second portion of executable code; determining, based on the functional line-of-code behavior and relation model, that the second portion of executable code is interdependent with the first portion of executable code; and generating, based on the determined interdependency, a report identifying the interdependent first portion of executable code and second portion of executable code.

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Tel Aviv, Israel

Zohar Fox