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: A driving environment is perceived based on sensor data obtained from a plurality of sensors mounted on the ADV. In response to a request for changing lane from a first lane to a second lane, path planning is performed. The path planning includes identifying a first lane change point for the ADV to change from the first lane to the second lane in a first trajectory of the ADV, determining a lane change preparation distance with respect to the first lane change point, and generating a second trajectory based on the lane change preparation distance, where the second trajectory having a second lane change point delayed from the first lane change point. Speed planning is performed on the second trajectory to control the ADV to change lane according to the second trajectory with different speeds at different point in time.

Abstract: In one embodiment, a set of predetermined driving parameters is determined from a set of driving statistics data collected from a number of vehicles, which may be driven by human drivers. For each pair of the predetermined driving parameters, a distribution of the pair of driving parameters is plotted based on their relationship on a two-dimensional (2D) distribution space. The 2D distribution space is partitioned into a number of grid cells, each grid cell representing a particular pair of driving parameters. For each of the grid cells, a probability is calculated that the pair of driving parameter likely falls in the grid cell. A grid table is generated corresponding to the pair of driving parameters. The grid table can be utilized during the autonomous driving at real-time or during simulation to determine a ride stability of an autonomous driving vehicle (ADV) in view of the pair of driving parameters.

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: In one embodiment, a method of generating control effort to control an autonomous driving vehicle (ADV) includes determining a gear position (forward or reverse) in which the ADV is driving and selecting a driving model and a predictive model based upon the gear position. In a forward gear, the driving model is a dynamic model, such as a “bicycle model,” and the predictive model is a look-ahead model. In a reverse gear, the driving model is a hybrid dynamic and kinematic model and the predictive model is a look-back model. A current and predicted lateral error and heading error are determined using the driving model and predictive model, respectively A linear quadratic regulator (LQR) uses the current and predicted lateral error and heading errors, to determine a first control effort, and an augmented control logic determines a second, additional, control effort, to determine a final control effort that is output to a control module of the ADV to drive 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: 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: 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: 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 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 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: 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: The present disclosure provides a PPPoE packets transmitting method and a PPPoE server. The method comprises: registering, by a PPPoE server, a PPPoE protocol packet sniffer with a Linux kernel and an Internet Protocol Version 4 (IPV4) protocol packet sniffer with a Netfilter framework, and adding a user's IP address and MAC address to authenticated user information, receiving, by the PPPoE server, a packet, and calling, by the PPPoE server, the PPPoE protocol packet sniffer or the IPV4 protocol packet sniffer to process and transmit the packet according to the authenticated user information. In the present disclosure, during a user's dial-up logon or logoff, the creation and deletion of a network interface are not required, which can improve the logon and logoff speeds.