Abstract: System and techniques for vehicle operation safety model (VOSM) grade measurement are described herein. A data set of parameter measurements—defined by the VOSM—of multiple vehicles are obtained. A statistical value is then derived from a portion of the parameter measurements. A measurement from a subject vehicle is obtained that corresponds to the portion of the parameter measurements from which the statistical value was derived. The measurement is then compared to the statistical value to produce a safety grade for the subject vehicle.

Abstract: System and techniques for vehicle operation safety model (VOSM) compliance measurement are described herein. A subject vehicle is tested in a vehicle following scenario against VOSM parameter compliance. The test measures the subject vehicle activity during phases of the following scenario in which a lead vehicle slows and produces log data and calculations that form the basis of a VOSM compliance measurement.

Abstract: System and techniques for test verification of a control system (e.g., a vehicle safety system) with a vehicle operation safety model (VOSM) such as Responsibility Sensitive Safety (RSS) are described. In an example, using test scenarios to measure performance of VOSM includes: defining safety condition parameters of a VOSM for use in a test scenario configured to test performance of a safety system; generating a test scenario, using the safety condition parameters, the test scenario generated as a steady state test, a dynamic test, or a stress test; executing the test scenario with a test simulator, to produce test results for the safety system; measuring real-time kinematics of the safety system, during execution of the test scenario, based on compliance with the safety condition parameters; and producing a parameter rating for performance of the safety system with the VOSM, based on the test results and the measured real-time kinematics.

Abstract: The application provides an apparatus and a method for procedural video assessment. The apparatus includes: interface circuitry; and processor circuitry coupled to the interface circuitry and configured to: perform an action segmentation process for a procedural video received via the interface circuitry to obtain a plurality of action features associated with the procedure video; transform the plurality of action features into a plurality of action-procedure features based on an action-procedure relationship learning module for discovering a relationship between the plurality of action features and a plurality of scoring oriented procedures associated with the procedure video; and perform a procedure classification process to infer the plurality of scoring oriented procedures from the plurality of action-procedure features.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for scale recovery from monocular video. An example non-transitory computer readable medium comprises instructions that, when executed, cause a machine to at least segment an input image from a monocular video to detect an object in the camera field, estimate camera parameters from the segmented input image, iteratively refine the estimated camera parameters using known object heights, calculate a scale for the video, iteratively refine the scale based on a user input, and report the scaling results for visualization.

Abstract: Provided is a device and a method for route planning. The route planning device (100) may include a data interface (128) coupled to a road and traffic data source (160); a user interface (170) configured to display a map and receive a route planning request from a user, the route planning request including a line of interest on the map; a processor (110) coupled to the data interface (128) and the user interface (170). The processor (110) may be configured to identify the line of interest in response to the route planning request; acquire, via the data interface (128), road and traffic information associated with the line of interest from the road and traffic data source (160); and calculate, based on the acquired road and traffic information, a navigation route that matches or corresponds to the line of interest and meets or satisfies predefined road and traffic constraints.

Abstract: System and techniques for test verification of a control system (e.g., a vehicle safety system) with a vehicle operation safety model (VOSM) such as Responsibility Sensitive Safety (RSS) are described. In an example, using test scenarios to measure performance of VOSM includes: defining safety condition parameters of a VOSM for use in a test scenario configured to test performance of a safety system; generating a test scenario, using the safety condition parameters, the test scenario generated as a steady state test, a dynamic test, or a stress test; executing the test scenario with a test simulator, to produce test results for the safety system; measuring real-time kinematics of the safety system, during execution of the test scenario, based on compliance with the safety condition parameters; and producing a parameter rating for performance of the safety system with the VOSM, based on the test results and the measured real-time kinematics.

Abstract: System and techniques for vehicle operation safety model (VOSM) grade measurement are described herein. A data set of parameter measurements-defined by the VOSM-of multiple vehicles are obtained. A statistical value is then derived from a portion of the parameter measurements. A measurement from a subject vehicle is obtained that corresponds to the portion of the parameter measurements from which the statistical value was derived. The measurement is then compared to the statistical value to produce a safety grade for the subject vehicle.

Abstract: Various aspects of methods, systems, and use cases for critical scenario identification and extraction from vehicle operations are described. In an example, an approach for lightweight analysis and detection includes capturing data from sensors associated with (e.g., located within, or integrated into) a vehicle, detecting the occurrence of a critical scenario, extracting data from the sensors in response to detecting the occurrence of the critical scenario, and outputting the extracted data. The critical scenario may be specifically detected based on a comparison of the operation of the vehicle to at least one requirement specified by a vehicle operation safety model. Reconstruction and further data processing may be performed on the extracted data, such as with the creation of a simulation from extracted data that is communicated to a remote service.

Abstract: Various aspects of methods, systems, and use cases for safety logging in a vehicle are described. In an example, an approach for data logging in a vehicle includes use of logging triggers, public and private data buckets, and defined data formats, for data provided during autonomous vehicle operation. Data logging operations may be triggered in response to safety conditions, such as detecting a dangerous situation from a failure of the vehicle to comply with safety criteria of a vehicle operational safety model. Data logging operations may include logging data in response to detection of the dangerous situation, including storage of a first portion of data in a public data store, and storage of a second portion of privacy-sensitive data in a private data store, where the data stored in the private data store is encrypted, and where access to the private data store is controlled.

Abstract: A sensor calibrator comprising one or more processors configured to receive sensor data representing a calibration pattern detected by a sensor during a period of relative motion between the sensor and the calibration pattern in which the sensor or the calibration pattern move along a linear path of travel; determine a calibration adjustment from the plurality of images; and send a calibration instruction for calibration of the sensor according to the determined calibration adjustment. Alternatively, a sensor calibration detection device, comprising one or more processors, configured to receive first sensor data detected during movement of a first sensor along a route of travel; determine a difference between the first sensor data and stored second sensor data; and if the difference is outside of a predetermined range, switch from a first operational mode to a second operational mode.

Abstract: System and techniques for test scenario verification, for a simulation of an autonomous vehicle safety action, are described. In an example, measuring performance of a test scenario used in testing an autonomous driving safety requirement includes: defining a test environment for a test scenario that tests compliance with a safety requirement including a minimum safe distance requirement; identifying test procedures to use in the test scenario that define actions for testing the minimum safe distance requirement; identifying test parameters to use with the identified test procedures, such as velocity, amount of braking, timing of braking, and rate of acceleration or deceleration; and creating the test scenario for use in an autonomous driving test simulator. Use of the test scenario includes applying the identified test procedures and the identified test parameters to identify a response of a test vehicle to the minimum safe distance requirement.

Abstract: System and techniques for vehicle operation safety model (VOSM) compliance measurement are described herein. A subject vehicle is tested in a vehicle following scenario against VOSM parameter compliance. The test measures the subject vehicle activity during phases of the following scenario in which a lead vehicle slows and produces log data and calculations that form the basis of a VOSM compliance measurement.

Abstract: System and techniques for vehicle operation safety model (VOSM) compliance measurement are described herein. A subject vehicle is tested in a vehicle following scenario against VOSM parameter compliance. The test measures the subject vehicle activity during phases of the following scenario in which a lead vehicle slows and produces log data and calculations that form the basis of a VOSM compliance measurement.