AUTOMATIC TEST GENERATION FOR HIGHLY COMPLEX EXISTING SOFTWARE

    公开(公告)号:US20230087569A1

    公开(公告)日:2023-03-23

    申请号:US17612670

    申请日:2021-03-11

    Abstract: Techniques are disclosed for the generation of automatic software tests for complex software systems, such as operating systems (OS) and/or systems that may be implemented as part of an autonomous vehicle (AV) or advanced driving assistance system (ADAS). The technique generates tests using a tool, such as a stressor, which stresses a particular system under test in multiple ways. For every run of the stressor, the functions of the system that are invoked during the test are captured. A check is then performed to determine if this set of functions corresponds to one of the test scenarios for which testing is desired. If the set of functions that were invoked matches the set of functions that defines the test, then the configuration of the stressor is stored, and this stressor configuration is considered as the test for a particular scenario.

    APPLICATION OF MEAN TIME BETWEEN FAILURE (MTBF) MODELS FOR AUTONOMOUS VEHICLES

    公开(公告)号:US20240367680A1

    公开(公告)日:2024-11-07

    申请号:US18253166

    申请日:2022-08-16

    Abstract: To receive authority certification for mass deployment of autonomous vehicles (AVs), manufacturers need to justify that their AVs operate safer than human drivers. This in turn creates the need to estimate and model the collision rate (failure rate) of an AV taking all possible errors and driving situations into account. In other words, there is the strong demand for comprehensive Mean Time between Failure (MTBF) models for AVs. The disclosure describes such a generic and scalable model that creates a link between errors in the perception system to vehicle-level failures (collisions). Using this model, requirements for the perception quality may then be derived based on the desired vehicle-level MTBF, or vice versa, to obtain an MTBF value given a certain mission profile and perception quality.

    EVALUATING A FLOATING-POINT ACCURACY OF A COMPILER

    公开(公告)号:US20230297497A1

    公开(公告)日:2023-09-21

    申请号:US17623351

    申请日:2021-03-31

    CPC classification number: G06F11/3696 G06F8/47

    Abstract: A mechanism for evaluating a floating-point accuracy of a vehicle driving compatible compiler includes testing code compiled by a vehicle driving compatible compiler with code compiled by a testing environment compatible compiler, executing the vehicle driving compatible compiled code involves executing addition type floating points operations to provide a first floating point result, executing the testing environment compatible compiled code to perform addition type floating points operations to provide a second floating point result that corresponds to the first floating point result, comparing the first floating point result to the second floating point results to provide a comparison result, and determining the floating-point accuracy of vehicle driving compatible compiler based on the comparison result.

    SECURE SYSTEM THAT INCLUDES AN OPEN SOURCE OPERATING SYSTEM

    公开(公告)号:US20200298870A1

    公开(公告)日:2020-09-24

    申请号:US16823104

    申请日:2020-03-18

    Abstract: A method of implementing safety mechanisms in a safety-critical system, the method comprising: receiving, at a safety mechanism configured to provide a first level of safety, a message or command from a calling process operating at second level of safety, the first level of safety having more restrictive requirements than the second level of safety; and initiating by the safety mechanism, a resultant process based on the message or command, the resultant process configured to operate at the first level of safety.

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