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: 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: 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: 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: Systems and methods for generating labelled audio data and onboard validation of the labelled audio data utilizing an autonomous driving vehicle (ADV) while the ADV is operating within a driving environment are disclosed. The method includes recording a sound emitted by an object within the driving environment of the ADV, and converting the recorded sound into audio samples. The method further includes labelling the audio samples, and refining the labelled audio samples to produce refined labelled audio data. The refined labelled audio data is utilized to subsequently train a machine learning algorithm to recognize a sound source during autonomous driving of the ADV. The method further includes generating a performance profile of the refined labelled audio data based at least on the audio samples, a position of the object, and a relative direction of the object. The position of the object and the relative direction of the object are determined by a perception system of the ADV.

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: 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.