Abstract: In one embodiment, a process is performed during controlling Autonomous Driving Vehicle (ADV). A confidence level associated with a sensed obstacle is determined. If the confidence level is below a confidence threshold, and a distance between the ADV and a potential point of contact with the sensed obstacle is below a distance threshold, then performance of a driving decision is delayed. Otherwise, the driving decision is performed to reduce risk of contact with the sensed obstacle.

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 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: An obstacle state evolution of a spatial position of a moving obstacle over a period of time is determined. A lane-obstacle relation evolution of the moving obstacle with each of one or more lanes near the moving obstacle over the period of time is further determined. An intended movement of the moving obstacle is predicted based on the obstacle state evolution and the lane-obstacle evolution. Thereafter, a trajectory of the ADV is planned to control the ADV to avoid a collision with the moving obstacle based on the predicted intended movement of the moving obstacle. The above process is iteratively performed for each of the moving obstacles detected within a predetermined proximity 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.

Abstract: This application discloses an avatar livestreaming method. The method includes monitoring a selection of an avatar as an online streamer by a user; starting, by a live process, a rendering process in response to detecting the selection; creating, by the rendering process, a shared texture configured to enable the live process to obtain a rendering result; creating, by the live process, a shared memory through which the live process and the rendering process exchange data except the rendering result; collecting in real time, by the live process, facial data indicating movements or expressions associated with a face or a head of the user and synchronizing the facial data to the rendering process; rendering, by the rendering process, the avatar in the shared texture based on the facial data; and obtaining, by the live process, the rendering result in the shared texture.

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: A method of navigating an autonomous driving vehicle (ADV) includes determining a target function for an open space model based on one or more obstacles and map information within a proximity of the ADV, then iteratively performing first and second quadratic programming (QP) optimizations on the target function. Then, generating a second trajectory based on results of the first and second QP optimizations to control the ADV autonomously using the second trajectory. The first QP optimization is based on fixing a first set of variables of the target function. The second QP optimization is based on maximizing a sum of the distances from the ADV to each of the obstacles over a plurality of points of the first trajectory, and minimizing a difference between a target end-state of the ADV and a determined final state of the ADV using the first trajectory.

Abstract: A simulation of an autonomous driving vehicle (ADV) includes capturing first data that includes a control command output by an autonomous vehicle controller of the ADV, and capturing second data that includes the control command being implemented at a control unit of the ADV. The control command, for example, a steering command, a braking command, or a throttle command, is implemented by the ADV to affect movement of the ADV. A latency model is determined based on comparing the first data with the second data, where the latency model defines time delay and/or amplitude difference between the first data and the second data. The latency model is applied in a virtual driving environment.

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, 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: 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 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: 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 various embodiments, described herein is a method of creating a simulation environment with multiple simulation obstacle vehicles, each with a different human-like driving style. Training datasets with different driving styles can be collected from individual human drivers, and can be combined to generate mixed datasets, each mixed dataset including only data of a particular driving style. Multiple learning-based motion planner critics can be trained using the mixed datasets, and can be used to tune multiple motion planners. Each tuned motion planner can have a different human-like driving style, and can be installed in one of multiple simulation obstacle vehicles. The simulation obstacle vehicles with different human-like driving styles can be deployed to the simulation environment to make the simulation environment more resemble a real-world driving environment.

Abstract: This application provides an online streamer image picture transmission method and computing device in live interaction. The method applied to a server, the method includes: receiving an online streamer image picture sent by a first online streamer end, and obtaining a target obfuscation key; performing encryption processing on the online streamer image picture based on the target obfuscation key and picture information of the online streamer image picture, to obtain an encrypted online streamer image picture; and in response to determining that a live interaction request for the first online streamer end and a second online streamer end is received, sending the encrypted online streamer image picture and the target obfuscation key to the second online streamer end.

Abstract: This application provides an online-streamer image model file transmission method and computing device in co-hosting during livestreaming. The method includes: receiving an online-streamer image model file sent by a first online streamer end, and obtaining a target obfuscation key and a target encryption type; determining to-be-encrypted bytes in the online-streamer image model file based on the target encryption type, and encrypting the to-be-encrypted bytes based on the target obfuscation key to obtain an encrypted online-streamer image file; and receiving an interaction request between the first online streamer end and a second online streamer end, and sending the encrypted online-streamer image file to the second online streamer end based on the interaction request.