Abstract: In one embodiment, a computer-implemented method for calibrating autonomous driving vehicles at a cloud-based server includes receiving, at the cloud-based server, one or more vehicle calibration requests from at least one user, each vehicle calibration request including calibration data for one or more vehicles and processing in parallel, by the cloud-based server, the one or more vehicle calibration requests for the at least one user to generate a calibration result for each vehicle. The method further includes sending, by the cloud-based server, the calibration result for each vehicle to the at least one user.

Abstract: In one embodiment, an autonomous driving system of an autonomous driving vehicle perceives a driving environment surrounding the autonomous driving vehicle traveling along a path, including perceiving an obstacle in the driving environment. The system detects a vertical acceleration of the autonomous driving vehicle based on sensor data obtained from a sensor on the autonomous driving vehicle. The system further calibrates the perceived obstacle based on the vertical acceleration of the autonomous driving vehicle. The system then controls the autonomous driving vehicle to navigate through the driving environment in view of the calibrated perceived obstacle.

Abstract: In one embodiment, a process is performed during controlling Autonomous Driving Vehicle (ADV). A plurality of point confidence scores are determined, each defining a reliability of a corresponding point on a trajectory of a moving obstacle. At least one of the point confidence scores is determined based on a) an overall trajectory confidence score, and b) at least one environmental factor of the obstacle. The ADV is controlled based on the trajectory of the moving obstacle and at least one of the plurality of point confidence scores.

Abstract: According to one embodiment, a driving environment surrounding an ADV is perceived based on sensor data obtained from various sensors mounted on the ADV including detecting one or more obstacles. The obstacles of the detected obstacles are determined and tracked based on the perception process, where the obstacle states of the obstacles may be maintained in an obstacle state buffer associated with the obstacles. When it is detected that a first moving obstacle is blocked by an object by the sensors, the further movement of the first moving obstacle is predicted based on the prior obstacle states of the first moving obstacle, while the first moving obstacle is blocked in view by the object. A trajectory is planned for the ADV in view of the predicted movement of the first moving obstacle while the first moving obstacle is in the blind area.

Abstract: In one embodiment, a system generates a plurality of driving scenarios to train a reinforcement learning (RL) agent and replays each of the driving scenarios to train the RL agent by: applying a RL algorithm to an initial state of a driving scenario to determine a number of control actions from a number of discretized control/action options for the ADV to advance to a number of trajectory states which are based on a number of discretized trajectory state options, determining a reward prediction by the RL algorithm for each of the controls/actions, determining a judgment score for the trajectory states, and updating the RL agent based on the judgment score.

Abstract: In one embodiment, an open space model is generated for a system to plan trajectories for an ADV in an open space. The system perceives an environment surrounding an ADV including one or more obstacles. The system determines a target function for the open space model based on constraints for the one or more obstacles and map information. The system iteratively, performs a first quadratic programming (QP) optimization on the target function based on a first trajectory while fixing a first set of variables, and performs a second QP optimization on the target function based on a result of the first QP optimization while fixing a second set of variables. The system generates a second trajectory based on results of the first and the second QP optimizations to control the ADV autonomously according to the second trajectory.

Abstract: In one embodiment, a system uses an actor-critic reinforcement learning model to generate a trajectory for an autonomous driving vehicle (ADV) in an open space. The system perceives an environment surrounding an ADV. The system applies a RL algorithm to an initial state of a planning trajectory based on the perceived environment to determine a plurality of controls for the ADV to advance to a plurality of trajectory states based on map and vehicle control information for the ADV. The system determines a reward prediction by the RL algorithm for each of the plurality of controls in view of a target destination state. The system generates a first trajectory from the trajectory states by maximizing the reward predictions to control the ADV autonomously according to the first trajectory.

Abstract: In one embodiment, a method of training dynamic models for autonomous driving vehicles includes the operations of receiving a first set of training data from a training data source, the first set of training data representing driving statistics for a first set of features; training a dynamic model based on the first set of training data for the first set of features; determining a second set of features as a subset of the first set of features based on evaluating the dynamic model, each of the second set of features representing a feature whose performance score is below a predetermined threshold. The method further includes the following operations for each of the second set of features: retrieving a second set of training data associated with the corresponding feature of the second set of features, and retraining the dynamic model using the second set of training data.

Abstract: In one embodiment, a computer-implemented method of autonomously parking an autonomous driving vehicle, includes generating environment descriptor data describing a driving environment surrounding the autonomous driving vehicle (ADV), including identifying a parking space and one or more obstacles within a predetermined proximity of the ADV, generating a parking trajectory of the ADV based on the environment descriptor data to autonomously park the ADV into the parking space, including optimizing the parking trajectory in view of the one or more obstacles, segmenting the parking trajectory into one or more trajectory segments based on a vehicle state of the ADV, and controlling the ADV according to the one or more trajectory segments of the parking trajectory to autonomously park the ADV into the parking space without collision with the one or more obstacles.

Abstract: In one embodiment, a computer-implemented method of operating an autonomous driving vehicle (ADV) includes perceiving a driving environment surrounding the ADV based on sensor data obtained from one or more sensors mounted on the ADV, determining a driving scenario, in response to a driving decision based on the driving environment, applying a predetermined machine-learning model to data representing the driving environment and the driving scenario to generate a set of one or more driving parameters, and planning a trajectory to navigate the ADV using the set of the driving parameters according to the driving scenario through the driving environment.

Abstract: An autonomous driving vehicle (ADV) receives instructions for a human test driver to drive the ADV in manual mode and to collect a specified amount of driving data for one or more specified driving categories. As the user drivers the ADV in manual mode, driving data corresponding to the one or more driving categories is logged. A user interface of the ADV displays the one or more driving categories that the human driver is instructed collect data upon, and a progress indicator for each of these categories as the human driving progresses. The driving data is uploaded to a server for machine learning. If the server machine learning achieves a threshold grading amount of the uploaded data to variables of a dynamic self-driving model, then the server generates an ADV self-driving model, and distributes the model to one or more ADVs that are navigated in the self-driving mode.

Abstract: According to one embodiment, in response to a request to park an ADV into a parking lot, a remote server is accessed over a network (e.g., a VX2 link) to obtain a list of parking spaces that appear to be available in the parking lot. Based on the list of available parking spaces and the map associated with the parking lot, a route is generated to navigate through at least the available parking spaces. The ADV is driven according to the route to locate at least one of the available parking spaces and to park the ADV into the located available parking space. The centralized server is configured to periodically receive signals from a number of parking lots indicating which of the parking spaces of the parking lots are apparently available.

Abstract: A method of determining a smooth reference line for navigating an autonomous vehicle in a manner similar to human driving is disclosed. A high density map is used to generate a centerline for a lane of roadway. Using the centerline, a number of sample points is generated that is related to a curvature of the centerline. Adjustment points are generated at each sample point, a few on either side of the centerline at each sample point. Candidate points at a sample point include the adjustment points and sample point. A least cost path is determined through each of the candidate points at each of the sample points. Path cost is based an angle of approach and departure through a candidate point, and a distance of the candidate point from the centerline.

Abstract: A dynamic elastic one-way water-guiding sportswear is formed from a basic cloth. The basic cloth is made from a porous material having capable of dynamically adsorbing and transferring liquid water, and the dynamic transferring and guiding capability for liquid water of the basic cloth is determined based on the stretching ratio of the covered skin. The sportswear comprises an elastic stretchable bands (1_1, 1_2) and a pressure fastening area (2), wherein the elastic stretchable bands (1_1, 1_2) and the pressure fastening area (2) have different mechanics transmission properties. The elastic stretchable bands (1_1, 1_2) are used to provide elastic protection for twisting and stretching driven by the shoulders and legs, and the pressure fastening area (2) is used to fasten and protect the soft tissues of the back and abdomen by providing a pressure thereupon.

Abstract: A leg-protecting apparatus is arranged in close contact with a body surface of a wearer to cover a thigh, a knee, and a calf of the wearer, including a biomechanically protecting strap (A) arranged to correspond with structural locations and paths of tendons and ligaments of the knee and muscles proximal thereto during an exercise process. The strap comprises a cruciate ligament protecting strap (A-1), a patellar tendon protecting strap (A-2), a thigh muscle group protecting strap (A-3), and a calf muscle group protecting strap (A-4). Elastic moduli of the cruciate ligament protecting strap (A-1) and the patellar tendon protecting strap (A-2) has a step-change upon a change in a fabric tensile ratio caused by the knee bending of the wearer, wherein the step-change occurs after an initial low tensile modulus stage, during a tensile sudden-change stage, and before a high tensile modulus stage transitioning between the stages as knee angle decreases.

Abstract: A fully fashion knitted outer covering made by using a method for generation of contour fit 3D fully fashion knitted outer covering pattern based on 3D exterior data of an object. The method comprises the following steps: digitizing an individual to create a 3D object data cloud; automatically and/or manually recognizing object's exterior landmarks; extracting the object's exterior measurements; calculating the outer covering pattern block of the digitized surface of the object according to the extracted object's exterior measurements including geodesic measurements; transforming the outer covering block to 3D weft knitted outer covering pattern by introducing horizontal and/or vertical darts; and translating the modified knitted outer covering pattern to knitting diagrams and/or instructions, which can then be transferred to knitwear CAD system to control the automatic knitting machine to knit the required knitted outer covering.

Abstract: A fully fashion knitwear made by using a method for generation of contour fit three-dimensional (3D) fully fashion knitwear pattern based on 3D body data of an individual. The method comprises the following steps: digitizing an individual to create a 3D body data cloud; automatically recognizing body landmarks; extracting the body measurements; calculating the garment pattern block of the digitized surface of the individual according to the extracted body measurements including geodesic (minimal distance) measurements; transforming the garment block to 3D weft knitwear pattern by introducing horizontal and/or vertical darts; and translating the modified knitwear pattern to knitting diagrams and/or instructions, which can then be transferred manually to knitwear CAD system to control the automatic knitting machine to knit the required knitwear.

Abstract: A fully fashion knitted outer covering made by using a method for generation of contour fit 3D fully fashion knitted outer covering pattern based on 3D exterior data of an object. The method comprises the following steps: digitizing an individual to create a 3D object data cloud; automatically and/or manually recognizing object's exterior landmarks; extracting the object's exterior measurements; calculating the outer covering pattern block of the digitized surface of the object according to the extracted object's exterior measurements including geodesic measurements; transforming the outer covering block to 3D weft knitted outer covering pattern by introducing horizontal and/or vertical darts; and translating the modified knitted outer covering pattern to knitting diagrams and/or instructions, which can then be transferred to knitwear CAD system to control the automatic knitting machine to knit the required knitted outer covering.

Abstract: A fully fashion knitwear made by using a method for generation of contour fit three-dimensional (3D) fully fashion knitwear pattern based on 3D body data of an individual. The method comprises the following steps: digitizing an individual to create a 3D body data cloud; automatically recognizing body landmarks; extracting the body measurements; calculating the garment pattern block of the digitized surface of the individual according to the extracted body measurements including geodesic (minimal distance) measurements; transforming the garment block to 3D weft knitwear pattern by introducing horizontal and/or vertical darts; and translating the modified knitwear pattern to knitting diagrams and/or instructions, which can then be transferred manually to knitwear CAD system to control the automatic knitting machine to knit the required knitwear.