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: A display device includes a substrate, subpixel units and a thin film encapsulation structure. The subpixel units are disposed on the substrate, and each of the subpixel units includes a light emitting part and a pixel defining part surrounding the light emitting part. The thin film encapsulation structure covers the subpixel units, and includes a first encapsulation layer and a second encapsulation layer covering and in contact with the first encapsulation layer. The refractive index of the first encapsulation layer is different from that of the second encapsulation layer. The first encapsulation layer includes first microstructures, and the first microstructures are respectively disposed on the pixel defining parts. The second encapsulation layer includes second microstructures, the second microstructures are respectively disposed on the pixel defining parts, and the second microstructures are respectively fitted with the first microstructures to form distributed Bragg reflectors.

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.

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 display element includes a first spacer, a second spacer, at least one first electrode, a second electrode, at least one LED structure, a reflective layer, a first transparent molding layer and a transparent conductive layer. The second spacer is located on one side of the first spacer. The first electrode is surrounded by the first spacer. The second electrode is surrounded by the second spacer. The LED structure is located on the first electrode. The reflective layer is located on a sidewall of the first spacer facing the LED structure. The first transparent molding layer is located on the reflective layer and surrounds the LED structure. The transparent conductive layer is located on the top surface of the second semiconductor layer and the top surface of the first transparent molding layer, and extends to the second electrode.

Abstract: The technologies described herein are generally directed toward retrieving data from streaming storage. In an embodiment, a method can include receiving an application data request that identifies application data to be retrieved from a sequence of stored data chunks that correspond to a stored stream of data. The method can further include, based on the application data request, estimating a first estimated location of the application data, with the first estimated location including an identified chunk of a sequence of chunks. Further, the method can include, based on the application data request and a characteristic of the identified chunk, retrieving, by the system, a first data block that is estimated to comprise the application data, resulting in a first retrieved data block.

Abstract: A display device includes an array substrate, light emitting elements and light shielding units. The light emitting elements are disposed on the array substrate and electrically connected to the array substrate, where each of the light emitting elements has a first surface and a second surface opposite to the first surface. The second surfaces face the array substrate. The light shielding units are disposed on the array substrate and arranged alternately with the light emitting elements, where the light shielding units expose the first surfaces, and each of the light shielding units has a top and a bottom opposite to the top. The bottoms face the array substrate, and a cavity is existed between the bottoms and the array substrate.

Abstract: A display device includes a circuit substrate and at least one light emitting diode (LED) packaging structure electrically connected to the circuit substrate. Each of the at least one LED packaging structure includes a plurality of LEDs, a plurality of transparent packaging structures, a molding layer, a redistribution structure and a common electrode. Each LED includes a first electrode, a semiconductor stack structure and a second electrode stacked with each other. The transparent packaging structures respectively surround the LEDs. The molding layer surrounds the transparent packaging structures. The redistribution structure is located on a first side of the molding layer and is electrically connected to the first electrodes of the LEDs. The common electrode is located on a second side of the molding layer and is electrically connected to the second electrodes of the LEDs.

Abstract: In one embodiment, an exemplary method includes the operations of receiving, at a profiling application, a record file recorded by the ADV for a driving scenario in an area, and a high definition map matching the area; extracting planning messages and perception messages from the record file; and aligning the planning message and the perception messages based on their timestamps. The method further includes calculating an individual performance score for each planning cycle of the ADV for the driving scenario based on the planning messages; calculating a weight for each planning cycle based on the perception messages and the high definition map; and then calculating a weighted score for the driving scenario based on individual performance scores and their corresponding weights.

Abstract: Systems, methods, and media for factoring localization uncertainty of an ADV into its planning and control process to increase the safety of the ADV. The uncertainty of the localization can be caused by sensor inaccuracy, map matching algorithm inaccuracy, and/or speed uncertainty. The localization uncertainty can have negative impact on trajectory planning and vehicle control. Embodiments described herein are intended to increase the safety of the ADV by considering localization uncertainty in trajectory planning and vehicle control. An exemplary method includes determining a confidence region for an ADV that is automatically driving on a road segment based on localization uncertainty and speed uncertainty; determining that an object is within the confidence region, and a probability of collision with the ADV based on a distance of the object to the ADV; and planning a trajectory based on the probability of collision, and controlling the ADV based on the probability of collision.

Abstract: Disclosed are performance metrics for evaluating the accuracy of a dynamic model in predicting the trajectory of ADV when simulating the behavior of the ADV under the control commands. The performance metrics may indicate the degree of similarity between the predicted trajectory of the dynamic model and the actual trajectory of the vehicle when applied with identical control commands. The performance metrics measure deviations of the predicted trajectory of the dynamic model from the actual trajectory based on the ground truths. The performance metrics may include cumulative or mean absolute trajectory error, end-pose difference (ED), two-sigma defect rate (?2?), the Hausdirff Distance (HAU), the longest common sub-sequence error (LCSS), or dynamic time warping (DTW). The two-sigma defect rate represents the ratio of the number of points with true location error falling out of the 2? range of the predicted location error over the total number of points in the trajectory.

Abstract: An obstacle is detected based on sensor data obtained from a plurality of sensors of the ADV. A distribution of a plurality of positions of the obstacle at a point of time may be predicted. A range of positions of the plurality of positions of the obstacle may be determined based on a confidence level of the range. A modified shape with a modified length of the obstacle may be determined based on the range of positions of the obstacle. A trajectory of the ADV based on the modified shape with the modified length of the obstacle may be planned. The ADV may be controlled to drive according to the planned trajectory to drive safely to avoid a collision with the obstacle.

Abstract: According to some embodiments, described herein is a method and a system for guaranteeing safety at a control level of an ADV when at least a portion of a planned path generated by a planning module of the ADV is uncertain due to traffics and/or road condition changes. The planning module, when generating a path, also generate a confidence level of each segment of the path based on one or more of perception data, map information, or traffic rules. The confidence levels are decreasing further away from the ADV. When the control module of the ADV obtains the path and the associated confidence levels, the control module issue control commands to track only one or two segments whose confidence levels exceeds a threshold hold, and issue default control commands for the rest of the path.

Abstract: According to various embodiments, systems, methods, and media for evaluating an open space planner in an autonomous vehicle are disclosed. In one embodiment, an exemplary method includes receiving, at a profiling application, a record file recorded by the ADV while driving in an open space using the open space planner, and a configuration file specifying parameters of the ADV; extracting planning messages and prediction messages from the record file, each extracted message being associated with the open space planner. The method further includes generating features from the planning message and the prediction messages in view of the specified parameters of the ADV; and calculating statistical metrics from the features. The statistical metrics are then provided to an automatic tuning framework for tuning the open space planner.

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: 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 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.