Abstract: Disclosed are embodiments for facilitating restarting operations for recovery processes in autonomous systems. In some aspects, an embodiment includes determining that an autonomous vehicle (AV) is experiencing a failure condition; identifying a plurality of restart operations to apply to one or more components of the AV contributing to the failure condition; prior to applying a restart operation of the plurality of restart operations, determining that safety conditions corresponding to the restart operation are satisfied; and applying the plurality of restart operations to the AV in accordance with an increasing order of disruptiveness of each of the restart operations to operations of the AV.

Abstract: Apparatus and methods are provided for using a self-illuminating harp to calibrate for distortion. In particular, apparatus, methods and systems are provided for imaging and analyzing self-illuminated harp in order to correct for lens distortion. In various implementations, an array of media is strung plumb which resembles a harp. The media can be light fibers or other suitable material which can be strung in a straight line. The cores of translucent fibers are illuminated. These are configured to scatter light throughout their bulk which escapes the core by egressing along the length of fiber. This light produces plumb lines which can be imaged, analyzed and model. In some implementations, imaging is performed by rotation to include a full FOV.

Abstract: Aspects of the subject technology relate to systems, methods, and computer-readable media for controlling operation of an autonomous vehicle (AV) based on changing weather conditions. A weather state that is changing in an environment during operation of an AV can be identified based on data gathered by the AV operating in the environment. An extent that the weather state has changed in the environment can be determined from the data gathered by the AV. Operation of the AV can be controlled based on both characteristics of the weather state that is changing and the extent that the weather state has changed.

Abstract: Disclosed are embodiments for facilitating real-time autonomous vehicle (AV) fleet parking availability. In some aspects, an embodiment includes determining, based on a first set of perception sensor inputs of an AV, sections of roadway that are used for vehicle parking; determining, based on a second set of perception sensor inputs, whether the sections of roadway are allowable for parking and are available for parking; transmitting parking location data comprising identification of parking location objects corresponding to the sections of roadway and signals indicating that the parking location object is used for vehicle parking, indicating whether the parking location object is allowable for parking, and indicating whether the parking location object is available for parking, wherein the parking location data is aggregated with other parking location data from other AVs into aggregated parking location data; and identifying a location for parking of the AV based on the aggregated parking location data.

Abstract: Disclosed are embodiments for facilitating a multi-inertial measurement unit (IMU) combination unit. In some aspects, an embodiment includes receiving, at a multi-IMU combination unit (MICU), sensor data from a plurality of inertial data sensors of a same sensor type; for each inertial data sensor, calibrating and transforming the respective sensor data using a calibration estimate for the inertial data sensor, where the calibration estimate is based on pre-integration methods that provide individual kinematic feedback that is compared to fused kinematic feedback from a main filter; combining the calibrated and transformed sensor data from the plurality of inertial data sensors into a fused output for the same sensor type; sampling the fused output to provide a single inertial data measurement for the plurality of inertial data sensors; and providing the fused kinematic feedback for the calibration estimate, the fused kinematic feedback generated from the sampling of the single fused output.

Abstract: Planning, or path finding is one of many tasks performed by an autonomous vehicle (AV). Planning involves searching for and updating an optimal plan from a current pose to an end pose while avoiding obstacles. Planning can be particularly challenging in driving scenarios involving complex driving maneuvers, such as parking, making a U-turn, making a K-turn, etc. Searching for an optimal plan may take more time, e.g., a few seconds, in certain driving scenarios. Consumers of the plan may have a low tolerance for delay in receiving the optimal plan. A relay can be implemented to publish the latest plan to the consumers at a frequency. The relay can check whether the delay violates a dynamic threshold and raise an error accordingly. The searching process can be improved to reduce the delay in finding an optimal plan.

Abstract: Sensor data obtained from vehicles driving through a particular environment (e.g., a particular city or region being mapped) is used to identify and label traffic control features. Location, speed, and/or acceleration of mapping vehicles can be used to identify intersections that may have traffic control features. Environmental data, such as image data captured by one or more cameras, and point cloud data collected by lidar and/or radar sensors, is used to automatically detect traffic control features at the intersections.

Abstract: Disclosed are embodiments for facilitating autonomous vehicle simulated mileage data collection-guided operational design domain testing and test generation. In some aspects, an embodiment includes receiving performance results corresponding to simulated mileage accumulation of simulated autonomous vehicles (AVs); determining, from the performance results, performance metrics and corresponding confidence intervals for the performance metrics; identifying at least one of the performance metrics that does not satisfy a performance metric threshold or at least one of the corresponding confidence intervals that does not satisfy a confidence interval threshold; determining an identified domain corresponding to the at least one of the performance metrics or the at least one of the corresponding confidence intervals; and causing at least one of on-road supervised driving of one or more AVs to be initiated or one or more synthetic tests to be generated in accordance with the identified domain.

Abstract: Systems and techniques are described for improving the accuracy of autonomous vehicle simulations by dynamically assigning friction coefficients to road features based on sensor data. A method of the disclosed technology can include steps for associating a predetermined friction coefficient to road features; receiving sensor data from a sensor on an autonomous vehicle; identifying, from the sensor data, the presence of a road feature; assigning in a simulation, a friction coefficient corresponding to the road feature based on the predetermined friction coefficient associated with road feature; and generating, based on the assigned friction coefficient, a simulation of a trip by the autonomous vehicle as the autonomous vehicle traverses the road feature.

Abstract: Applications supporting operations of an autonomous vehicle fleet can be implemented on and supported by cluster infrastructure. These applications have endpoints where data traffic runs in and out of these applications. Securing access to these endpoints can prevent unauthenticated and unauthorized access to these endpoints and the protected resources accessible through these endpoints. Securing access to these endpoints, managing entitlements and security policies, and maintaining security systems that can enforce the security policies are not trivial tasks. One solution addresses some of these challenges by offering a simple frontend for users to define the entitlements and security policies, leveraging an open source security solution, and ensuring backwards compatibility to other security solutions in the cluster infrastructure.

Abstract: Systems and techniques are provided for simulating movement of an articulated vehicle. An example method can include generating a simulation environment for an autonomous vehicle, wherein the simulation environment includes at least one articulated vehicle that includes a tractor coupled to a first trailer using a first pivot joint; determining a first velocity of the first pivot joint based on a first simulated movement associated with the tractor; and determining a second simulated movement associated with the first trailer based on the first velocity of the first pivot joint and a first distance between the first pivot joint and a first point on the first trailer.

Abstract: Aspects of the subject technology relate to systems, methods, and computer-readable media for image classification through a two-stage classifier. Raw image data of an image gathered by a sensor associated with an AV during operation of the AV is accessed. A first stage of a two-stage classifier is applied. The first stage is trained by first raw AV data captured at varying values of one or more capture parameters associated with one or more sensors of the AV in capturing the first raw AV data. A second stage of the two-stage classifier is applied to the raw image data to generate a final classification output. The second stage of the two-stage classifier if formed by a plurality of image calibration classifiers that are trained by second raw AV data at varying values of one or more image calibration parameters.

Abstract: Systems and techniques are provided for expanding a scope of coverage of test scenarios for training an autonomous vehicle (AV). An example method can include identifying a maneuver of an AV; receiving, from a test repository, a plurality of tests that includes the maneuver; identifying one or more segments on a map of an operational design domain (ODD) that include a driving environment for the maneuver; determining a similarity between a driving scene of each of the plurality of tests and the one or more segments on the map of the ODD; and determining a degree of test coverage for each of the one or more segments for the maneuver based on the determined similarity.

Abstract: Build, execution, and testing on software may be performed remotely on cluster infrastructure. Tests can be scheduled on workers in the cluster infrastructure, and results of the tests are reported to the developer. If a test fails, the developer may receive an exit code from the failed test. For tests that are flaky, an exit code provides little to no benefit to identify the cause of the flakiness. To make it easier for the developer to debug flaky tests, a test identified to be flaky can be run many times in parallel, one or more parallel executions that result in failure can be paused, and one or more remote debugging sessions can be created for the one or more parallel executions. A developer can access the remote debugging session to inspect the paused execution of the test equipped with debugging tools to determine the cause of the flakiness.

Abstract: Systems and methods for dynamically suppressing noise at electric vehicle charging sites. In particular, systems and methods are provided for measuring the ambient noise at a charging site and adjusting electric vehicle chargers to reduce noise pollution. In some examples, electric vehicle charger cooling fans potentially generate a high level of noise, and the power output of a charger can be decreased to decrease the heat generated by the chargers, thereby reducing the need for fans. In various examples, reduction of noise pollution can be especially important in specified geographies (e.g., residential neighborhoods) and/or during selected timeframes (e.g., overnight). In various examples, the system interfaces with a central computer service (e.g., a dispatch service) to intelligently route autonomous vehicles to charging sites based on noise levels at various available charging sites.

Abstract: Systems, methods, and computer-readable media are provided for maintaining cleanliness of an autonomous vehicle. In some examples, an autonomous vehicle fleet management device may receive cleanliness data corresponding to at least one autonomous vehicle. In some aspects, the autonomous vehicle fleet management device may determine, based on the cleanliness data, one or more parameters corresponding to at least one cleanliness issue associated with the at least one autonomous vehicle. In some cases, a remediation plan for the at least one cleanliness issue may be determined based on the one or more parameters. In some instances, at least one driving instruction may be sent to the at least one autonomous vehicle that is based on the remediation plan.

Abstract: Examples of the present disclosure provide a computer-implemented system, comprising instructions for performing operations including: estimating locations in a region of a map where features associated with water, snow or ice are likely to be formed under certain adverse weather conditions; simulating one of the adverse weather conditions; generating the features associated with the simulated one of the adverse weather conditions at the locations; determining a response of a perception stack of an autonomous vehicle (AV) to the adverse weather conditions observed at the locations; determining a reaction of the AV to the features generated in the region, the reaction of the AV being a function of a configuration of the AV; in response to the reaction, updating the configuration; repeating the determining the reaction and the updating the configuration until a desired reaction is obtained; and exporting a final configuration corresponding to the desired reaction to a physical AV.

Abstract: The subject disclosure relates to estimating a grade of a road and calibrating sensors of an autonomous vehicle based on the grade. A process of the disclosed technology can include effectuating an autonomous vehicle to perform one or more maneuvers that cause the autonomous vehicle to face different directions along a road, obtaining a first set of sensor measurements of an autonomous vehicle in a first pose, wherein the first set of sensor measurements includes a first measured specific force, obtaining a second set of sensor measurements of the autonomous vehicle in a second pose, wherein the second set of sensor measurements includes a second measured specific force, determining a direction of gravity based on the first measured specific force and the second measured specific force, and calibrating at least one sensor of the autonomous vehicle based on the determined direction of gravity.

Abstract: A method is described and includes acquiring sensor data produced by sensors of a plurality of vehicles traversing an area including a location of a user, wherein the vehicles traversing the area comprise a subset of a fleet of vehicles for providing rideshare services; processing the acquired sensor data to determine a category of the user; selecting a vehicle from the fleet of vehicles based on the category of the user, wherein the selected vehicle comprises at least one accommodation corresponding to the category of the user; and dispatching the selected vehicle to a pick-up location designated by the user.