Abstract: Provided is an analysis method for determining gas-bearing situation of an unknown shale reservoir, includes: S1, selecting a shale reservoir of a target interval of a place; and collecting data of parameters of cores of each of a known gas-bearing shale reservoir A and a known water-bearing shale reservoir B; S2, calculating average values of the parameters of each of the reservoirs A and B respectively; S3, calculating average differences of the parameters of each of the reservoirs; S4, calculating covariance values of the parameters of each of the reservoirs; S5, establishing, according to the covariance values, an equation group and resolving discriminant coefficients; S6, establishing a discriminant equation according to the discriminant coefficients and solving a discriminant index; and S7, obtaining values of parameters of cores of a sample of the unknown shale reservoir, calculating a discriminant value, and determining gas-bearing situation of the unknown shale reservoir.

Abstract: In one example, a system for executing applications can include a main processor to initialize a virtual machine to execute an application. The main processor can also determine a main utilization indicator of the main processor is above a threshold and an auxiliary utilization indicator of an auxiliary processor is below a threshold, wherein the auxiliary processor is based on an auxiliary instruction set architecture. Additionally, the main processor can transmit an instruction from the application to the auxiliary processor for execution and update context data for the application in response to receiving an execution result from the auxiliary processor.

Abstract: In one embodiment, a microcontroller unit (MCU) receives an expected state of an autonomous driving vehicle (ADV) from a controller of the ADV, where the controller controls motions of the ADV using a control algorithm. The MCU receives sensor data from one or more sensors of the ADV. The MCU determine an actual state of the ADV based on the sensor data. The MCU determines a performance metric of the control algorithm based on the expected state and the actual state. In response to determining the performance metric has satisfied a predetermined condition, the MCU determines a plurality of weight values for the control algorithm. The MCU sends the plurality of weight values to the control system to tune one or more weight parameters of the control algorithm using the plurality of weight values, where the controller controls the ADV using the tuned control algorithm.

Abstract: A computing device may obtain one or more raw images captured from sensors of an autonomous driving vehicle (ADV). The computing device may apply one or more signal processing parameters to one or more raw images, resulting in one or more processed images. The computing device may apply an object detection algorithm of the ADV to the one or more processed images. The computing device may determine a score of the object detection algorithm as applied to the one or more processed images. One or more optimized signal processing parameters may be determined based on the score of the object detection algorithm and the signal processing parameter. The one or more optimized signal processing parameters may be used to configure the ADV for use during autonomous driving.

Abstract: According to some embodiments, systems, methods, and media for operating an autonomous driving vehicle (ADV) encountered with small objects are described. According to a method, the ADV, when detecting an object in a lane in which the ADV is travelling, can determine whether the ADV can safely drive over the object based on attributes of the object and attributes of the ADV, and if so, can generate one or more planned trajectories that each enable the ADV to drive over the object. From the one or more planned trajectories, the ADV can select a planned trajectory that enables the ADV to drive over the object along the centerline of the ADV without causing the ADV to drive out of the lane. If no such planned trajectory exists, the ADV can bypass the object, or stop within a predetermined distance of the object.

Abstract: In one embodiment, a system configures an image signal processor (ISP) of an autonomous driving vehicle (ADV) with a first set of ISP configuration parameters, where the ISP is used to process raw image data of an image sensor of the ADV based on the first set of ISP configuration parameters. The system determines whether one or more criteria is satisfied, where the one or more criteria corresponds to an expected change in a characteristic of ambient light being perceived by the image sensor of the ADV. In response to determining that the one or more criteria is satisfied, the system configures the ISP of the ADV with a second set of ISP configuration parameters, where the ISP is used to apply an image processing algorithm to raw image data based on the second set of ISP configuration parameters to generate an image.

Abstract: The disclosure describes an autotuning framework for tuning an image signal processing (ISP) module of an autonomous driving vehicle (ADV). The autotuning framework can generate different sets of parameter values using a variety of optimization algorithms to configure the ISP module. Each processed image generated by the ISP module configured with the different sets of parameter values is compared by an ISP module testing device with a reference image stored therein to generate an objective core measuring one or more differences between each of the processed images and the reference image. The objective scores and the corresponding sets of parameter values are stored in a database. Different sets of optimal parameter values for different environments can be selected from the database, and uploaded to a cloud database for use by an ADV, which can select a different set of ISP parameter values based on an environment that the ADV is travelling in.

Abstract: In one embodiment, a system generates an image using either a first or a second image signal processing (ISP) algorithm, where the first or second ISP algorithm is applied to raw image data of a camera of an autonomous driving vehicle (ADV) to generate the image. The system applies a machine learning model to the image to identify a representation of an obstacle, where the machine learning model is generated by a few shots learning algorithm that contrasts labeled data of a positive training sample from images corresponding to the first and second ISP algorithms to labeled data of a negative training sample from images corresponding to the first and second ISP algorithm. The system determines a classification and a location of the obstacle based on the representation of the obstacle. The system plans a motion control of the ADV based on the classification and location of the detected object.

Abstract: Disclosed is a microfluidic detection device including a circuit substrate and a transparent substrate. The circuit substrate is provided with at least one first light-emitting device used to emit a detection beam, a photodetector used to receive the detection beam and send out a sensing signal, and a control circuit electrically connected to the first light-emitting device and the photodetector. The transparent substrate overlaps the circuit substrate and is provided with a microfluidic channel and a light guide structure. The light guide structure has a light incident surface disposed corresponding to the first light-emitting device and a light exiting surface disposed corresponding to the photodetector. The light guide structure extends from each of the light incident surface and the light exiting surface to the microfluidic channel and is adapted to transmit the detection beam into and out of the microfluidic channel.

Abstract: In an embodiment, an autonomous driving system (ADS) determines that a corresponding autonomous driving vehicle (ADV) has stopped on a gradient. The ADS determines a first brake hold pressure based on a first gradient value of the ADV measured at a first point in time. The ADS then applies the first brake hold pressure to a brake system in the ADV. Then, the ADS determines a second brake hold pressure based on a second gradient value of the ADV measured at a second point in time. The ADS then applies the second brake hold pressure to the brake system accordingly.

Abstract: Embodiments of the invention are provided to automatically generate corner simulation scenarios. In an embodiment, an exemplary method includes performing the following operations for a predetermined number of iterations for each set of predefined parameters. The operations include generating a set of parameter values for the set of predefined parameters; determining whether the set of parameter values is valid or invalid based on a set of predefined metrics; and if the set of parameter values is valid, performing a simulation task to simulate a trajectory planner of the ADV in a simulation scenario configured by the set of parameter values. The method further includes calculating a performance score for the simulation task; and if the performance score of the simulation task is below a predetermined threshold, saving the set of parameter values in a storage, wherein the set of parameter values is used for re-tuning the trajectory planner.

Abstract: According to some embodiments, systems, methods and media for operating an autonomous driving vehicles (ADV) in an unforeseen scenario are disclosed. In one embodiment, an exemplary method includes determining that the ADV has entered an unforeseen scenario; and identifying one or more surrounding vehicles that are navigating the unforeseen scenario. The method further includes generating a trajectory by mimicking driving behaviors of one or more of the one or more surrounding vehicles; and operating the ADV to follow the trajectory to navigate the unforeseen scenario.

Abstract: A system perceives an environment of an autonomous driving vehicle (ADV) based on a plurality of sensors and map data. The system determines an obstacle in the perceived environment to be a moving vehicle and the moving vehicle is to a left lane, to a right lane, or in front of the ADV. The system performs an inference on the obstacle using a neural network model to determine whether a behavior of the obstacle is anomalous. The system determines the obstacle is anomalous based on the performed inference.

Abstract: Among the various aspects of the present disclosure are the provision of an image alignment and registration system and a breast cancer risk prediction system.

Abstract: According to various embodiments, the disclosure discloses systems, methods and media for formulating training datasets for learning-based components in an autonomous driving vehicle (ADV). In an embodiment, an exemplary method includes allocating training datasets for training a learning-based model in the ADV, each training dataset being allocated to one of multiple predefined driving scenarios; determining a weight of each training dataset out of the training datasets; and optimizing the weight of each training dataset in one or more iterations according to a predetermined algorithm until a performance of the learning-based model reaches a predetermined threshold. The predetermined algorithm is one of a random search algorithm, a grid search algorithm, or a Bayesian algorithm.

Abstract: A trajectory of an obstacle is predicted by a prediction module of the ADV. A trajectory of the ADV is determined based on the trajectory of the obstacle by a planning module of the ADV. A loss function of an analysis model of the prediction module is decomposed to multiple components with multiple weightings to generate a weighted loss function based on the trajectory of the ADV. A performance of the prediction module is evaluated based on the weighted loss function to improve the performance of the prediction module to increase a safety and comfort of the ADV.

Abstract: A plurality of trajectories of a plurality of obstacles are predicted, at an autonomous driving simulation platform, by a prediction module of an autonomous driving vehicle (ADV). A trajectory of the ADV is planned, at the autonomous driving simulation platform, by a planning module of the ADV based on the plurality of trajectories of the plurality of obstacles. A performance of the planning module is determined based on one or more evaluation metrics regarding the trajectory of the ADV. A performance of the prediction module is evaluated based on the performance of the planning module to improve the performance of the prediction module to deploy the prediction module to the ADV to drive autonomously.

Abstract: The present disclosure provides methods and techniques for evaluating and improving algorithms for autonomous driving planning and control (PNC), using one or more metrics (e.g., similarity scores) computed based on expert demonstrations. For example, the one or more metrics allow for improving PNC based on human, as opposed to or in addition to optimizing certain oversimplified properties, such as the least distance or time, as an objective. When driving in certain scenarios, such as taking a turn, people may drive in a distributed probability pattern instead of in a uniform line (e.g., different speeds and different curvatures at the same corner). As such, there can be more than one “correct” control trajectory for an autonomous vehicle to perform in the same turn. Safety, comfort, speeds, and other criteria may lead to different preferences and judgment as to how well the controlled trajectory has been computed.

Abstract: According to some embodiments, systems, methods and media for dynamically generating scenario parameters for an autonomous driving vehicles (ADV) are described. In one embodiment, when an ADV enters a driving scenario, the ADV can invoke a map-based scenario checker to determine the type of scenario, and invokes a corresponding neural network model to generate a set of parameters for the scenario based on real-time environmental conditions (e.g., traffics) and vehicle status information (e.g., speed). The set of scenario parameters can be a set of extra constraints for configuring the ADV to drive in a driving mode corresponding to the scenario.