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: Systems and methods are disclosed for optimizing values of a set of tunable parameters of an autonomous driving vehicle (ADV). The controllers can be a linear quadratic regular, a “bicycle model,” a model-reference adaptive controller (MRAC) that reduces actuation latency in control subsystems such as steering, braking, and throttle, or other controller (“controllers”). An optimizer selects a set tunable parameters for the controllers. A task distribution system pairs each set of parameters with each of a plurality of simulated driving scenarios, and dispatches a task to the simulator to perform the simulation with the set of parameters. Each simulation is scored. A weighted score is generated from the simulation. The optimizer uses the weighted score as a target objective for a next iteration of the optimizer, for a fixed number of iterations. A physical real-world ADV is navigated using the optimized set of parameters for the controllers in the ADV.

Abstract: In one embodiment, a set of parameters representing a first state of an autonomous driving vehicle (ADV) to be simulated and a set of control commands to be issued at a first point in time. In response, a localization predictive model is applied to the set of parameters to determine a first position (e.g., x, y) of the ADV. A localization correction model is applied to the set of parameters to determine a set of localization correction factors (e.g., ?x, ?y). The correction factors may represent the errors between the predicted position of the ADV by the localization predictive model and the ground truth measured by sensors of the vehicle. Based on the first position of the ADV and the correction factors, a second position of the ADV is determined as the simulated position of the ADV.

Abstract: Secure, integrated, personalized smart information management and exchange systems, methods and devices are disclosed to increase the quality of professional services and reduce the cost of operations. The present invention is smart by providing cognitive augmented interaction based on dynamically updated guidelines, knowledge bases, and/or artificial intelligences. The present invention also is secure for both stored and transmitted information. The present invention focuses on the individual level to provide personalized and data driven feedbacks. The present invention integrates various fields of inputs to provide whole and more accurate services.

Abstract: A data flow classification method includes obtaining, by a packet forwarding device, a plurality of data flows, extracting, by the packet forwarding device, address information and time information of each of the data flows, selecting, by the packet forwarding device, a data flow set that is generated when a first client device accesses a plurality of services, determining, by the packet forwarding device, a service set that includes a first service and a second service and that is accessed by the first client device based on the address information of the data flows, determining, by the packet forwarding device, a correlation between services in the service set based on time information of each data flow in the data flow set, and determining, by the packet forwarding device, that the first service and the second service implement a first application.

Abstract: In an embodiment, a learning-based dynamic modeling method is provided for use with an autonomous driving vehicle. A control module in the ADV can generate current states of the ADV and control commands for a first driving cycle, and send the current states and control commands to a dynamic model implemented using a trained neural network model. Based on the current states and the control commands, the dynamic model generates expected future states for a second driving cycle, during which the control module generates actual future states. The ADV compares the expected future states and the actual future states to generate a comparison result, for use in evaluating one or more of a decision module, a planning module and a control module in the ADV.

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: In one embodiment, an autonomous driving vehicle (ADV) operates in an on-lane mode, where the ADV follows a path along a vehicle lane. In response to determining that the ADV is approaching a dead-end, the ADV switches to an open-space mode. While in the open-space mode, the ADV conducts a three-point turn using a series of steering and throttle commands to generate forward and reverse movements until the ADV is within a) a threshold heading, and b) a threshold distance, relative to the vehicle lane. The ADV can then return to the on-lane mode and resume along the vehicle lane away from the dead-end.

Abstract: Systems and methods are disclosed for identifying time-latency and subsystem control actuation dynamic delay due to second order dynamics that are neglected in control systems of the prior art. Embodiments identify time-latency and subsystem control actuation delays by developing a discrete-time dynamic model having parameters and estimating the parameters using a least-squares method over selected crowd-driving data. After estimating the model parameters, the model can be used to identify dynamic actuation delay metrics such as time-latency, rise time, settling time, overshoot, bandwidth, and resonant peak of the control subsystem. Control subsystems can include steering, braking, and throttling.

Abstract: In one embodiment, a computer-implemented method for optimizing a controller of an autonomous driving vehicle (ADV) includes obtaining several samples, each sample having a set of parameters, iteratively performing, until a predetermined condition is satisfied: determining, for each sample, a score according to a configuration of the controller based on the set of parameters of the sample, applying a machine learning model to the samples and corresponding scores to determine a mean function and a variance function, producing a new sample as a minimum of a function of the mean function and the variance function with respect to an input space of the set of parameters, adding the new sample to the several samples, and outputting the new sample as an optimal sample, where parameters of the optimal sample are utilized to configure the controller to autonomously drive the ADV.

Abstract: Systems and methods are disclosed for reducing second order dynamics delays in a control subsystem (e.g. throttle, braking, or steering) in an autonomous driving vehicle (ADV) and increasing control system bandwidth by accounting for time-latency in a control subsystem actuation system. A control input is received from an ADV's autonomous driving system. The control input is translated into a control command of the control subsystem of the ADV. A reference actuation output and a predicted actuation output are generated corresponding to a by-wire (“real”) actuation action for the control subsystem. A control error is determined between the reference actuation action and the by-wire actuation action. A predicted control error is determined between the predicted actuation action and the between the by-wire actuation action. Adaptive gains are determined and applied to the by-wire actuation action to generate a second by-wire actuation action.

Abstract: In one embodiment, method performed by an autonomous driving vehicle (ADV) that determines, within a driving space, a plurality of routes from a current location of the ADV to a desired location. The method determines, for each route of the plurality of routes, an objective function to control the ADV autonomously along the route and, for each of the objective functions, performs Differential Dynamic Programming (DDP) optimization in view of a set of constraints to produce a path trajectory. The method determines whether at least one of the path trajectories satisfies each constraint and, in response to a path trajectory satisfying each of the constraints, selects the path trajectory for navigating the ADV from the current location to the desired location.

Abstract: In one embodiment, a plurality of obstacles is sensed in an environment of an automated driving vehicle (ADV). One or more representations are formed to represent corresponding groupings of the plurality of obstacles. A vehicle route is determined in view of the one or more representations, rather than each and every one of the obstacles individually.

Abstract: In one embodiment, a control command is generated with an MPC controller, the MPC controller including a cost function with weights associated with cost terms of the cost function. The control command is applied to a dynamic model of an autonomous driving vehicle (ADV) to simulate behavior of the ADV. One or more of the weights are based on evaluation of the dynamic model in response to the control command, resulting in an adjusted cost function of the MPC controller. Another control command is generated with the MPC controller having the adjusted cost function. This second control command can be used to effect movement of the ADV.

Abstract: In one embodiment, static-state curvature error compensation control logic for autonomous driving vehicles (ADV) receives planning and control data associated with the ADV, including a planned steering angle and a planned speed. A steering command is generated based on a current steering angle and the planned steering angle of the ADV. A throttle command is generated based on the planned speed in view of a current speed of the ADV. A curvature error is calculated based on a difference between the current steering angle and the planned steering angle. The steering command is issued to the ADV while withholding the throttle command, in response to determining that the curvature error is greater than a predetermined curvature threshold, such that the steering angle of the ADV is adjusted in view of the planned steering angle without acceleration.

Abstract: In one embodiment, a method determines a route from a first location of an autonomous driving vehicle (ADV) to a second location within an open space, the first location being a current location of the ADV. The method determines an objective function based on the route, the objective function having a set of costs for maneuvering the ADV from the first location to the second location. The method determines environmental conditions of the open space and uses the environmental conditions to determine a set of weights, each weight to be applied to a corresponding cost of the objective function. The method optimizes the objective function in view of one or more constraints, such that an output of the objective function reaches minimum while the one or more constraints are satisfied and generates a path trajectory with the optimized objective function to control the ADV autonomously according to the path trajectory.

Abstract: In one embodiment, control of an autonomous driving vehicle (ADV) includes determining a current scenario of the ADV. Based on the scenario, a control algorithm is selected among a plurality of distinct control algorithms as the active control algorithm. One or more control commands are generated using the active control algorithm, based one or more target inputs. The control commands are applied to effect movement of the ADV.

Abstract: In one embodiment, an ADV is routed by executing a first driving scenario that is active. The first driving scenario is one of a plurality of driving scenario types, each driving scenario type being associated with one or more stages to be executed while a corresponding driving scenario type is active. Based on an environmental condition around the ADV, a second driving scenario is set as active. The ADV is routed by executing the second driving scenario. When the second driving scenario exits, execution of the first driving scenario resumes at the one or more stages of the first driving scenario that remains to be executed.

Abstract: A parking system for autonomous driving vehicles optimizes a solution to a parking problem. The ADV detects a parking lot and selects a parking space. The ADV defines constraints for the parking lot, parking space, and kinematic constraints of the ADV, and generates a plurality of potential parking paths to the parking space, taking into account the constraints of the parking lot, parking space, and kinematics of the ADV, but without taking into any obstacles that may be surrounding the ADV. The ADV determines a cost for traversing each of the parking paths. One or more least cost candidate paths are selected from the parking paths, then one or more candidate paths are eliminated based on obstacles surrounding the ADV. Remaining candidates can be analyzed using a quadratic optimization system. A best parking path can be selected from the remaining candidates to navigate the ADV to the parking space.