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: A system perceives an environment of an autonomous driving vehicle (ADV) based on a plurality of sensors and map data. The system determines an obstacle in the perceived environment to be a moving vehicle and the moving vehicle is to a left lane, to a right lane, or in front of the ADV. The system performs an inference on the obstacle using a neural network model to determine whether a behavior of the obstacle is anomalous. The system determines the obstacle is anomalous based on the performed inference.

Abstract: According to some embodiments, systems, methods and media for operating an autonomous driving vehicles (ADV) in an unforeseen scenario are disclosed. In one embodiment, an exemplary method includes determining that the ADV has entered an unforeseen scenario; and identifying one or more surrounding vehicles that are navigating the unforeseen scenario. The method further includes generating a trajectory by mimicking driving behaviors of one or more of the one or more surrounding vehicles; and operating the ADV to follow the trajectory to navigate the unforeseen scenario.

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: 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: 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 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 some embodiments, described herein is a method and a system for guaranteeing safety at a control level of an ADV when at least a portion of a planned path generated by a planning module of the ADV is uncertain due to traffics and/or road condition changes. The planning module, when generating a path, also generate a confidence level of each segment of the path based on one or more of perception data, map information, or traffic rules. The confidence levels are decreasing further away from the ADV. When the control module of the ADV obtains the path and the associated confidence levels, the control module issue control commands to track only one or two segments whose confidence levels exceeds a threshold hold, and issue default control commands for the rest of the path.

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: An obstacle is detected based on sensor data obtained from a plurality of sensors of the ADV. Multiple trajectories of the obstacle are predicted with corresponding probabilities including a first predicted trajectory of the obstacle with a highest probability and a second predicted trajectory of the obstacle with a second highest probability. A cautionary trajectory of the ADV is planned based on at least one of a difference between the highest probability and the second highest probability or a consequence of the second trajectory. The ADV is to drive with a speed lower than a speed limit and prepare to stop in the cautionary trajectory. The ADV is controlled to drive according to the cautionary trajectory.

Abstract: Described herein are a method of training a learning-based critic for tuning a rule-based motion planner of an autonomous driving vehicle, a method of tuning a motion planner using an automatic tuning framework that with the learning-based critic. The method includes receiving training data that incudes human driving trajectories and random trajectories derived from the human driving trajectories; training a learning-based critic using the training data; identifying a set of discrepant trajectories by comparing a first set of trajectories, and a second set of trajectories; and refining, at the neural network training platform, the learning-based critic based on the set of discrepant trajectories. The automatic tuning framework can remove human efforts in tedious parameter tuning, reduce tuning time, while retaining the physical and safety constraints of the ruled-based motion planner.

Abstract: Embodiments of the invention are intended to evaluate the performance of a planning module of the ADV in terms of decision consistency in addition to other metrics, such as comfort, latency, controllability, and safety. In one embodiment, an exemplary method includes receiving, at an autonomous driving simulation platform, a record file recorded by the ADV that was automatically driving on a road segment; simulating operations of a dynamic model of the ADV in the autonomous driving simulation platform during one or more driving scenarios on the road segment based on the record file. The method further includes performing a comparison between each planned trajectory generated by a planning module of the dynamic model after an initial period of time with each trajectory stored in a buffer; and modifying a performance score generated by a planning performance profiler in the autonomous driving simulation platform based on a result of the comparison.

Abstract: In one embodiment, an exemplary method includes receiving, at a simulation platform, a record file recorded by a manually-driving ADV on a road segment, the simulation platform including a first encoder, a second encoder, and a performance evaluator; simulating automatic driving operations of a dynamic model of the ADV on the road segment based on the record file, the dynamic model including an autonomous driving module to be evaluated. The method further includes: for each trajectory generated by the autonomous driving module during the simulation: extracting a corresponding trajectory associated with the manually-driving ADV from the record file, encoding the trajectory into a first semantic map and the corresponding trajectory into a second semantic map, and generating a similarity score based on the first semantic map and the second semantic map. The method also includes generating an overall performance score based on each similarity score.

Abstract: Systems, methods, and media for factoring localization uncertainty of an ADV into its planning and control process to increase the safety of the ADV. The uncertainty of the localization can be caused by sensor inaccuracy, map matching algorithm inaccuracy, and/or speed uncertainty. The localization uncertainty can have negative impact on trajectory planning and vehicle control. Embodiments described herein are intended to increase the safety of the ADV by considering localization uncertainty in trajectory planning and vehicle control. An exemplary method includes determining a confidence region for an ADV that is automatically driving on a road segment based on localization uncertainty and speed uncertainty; determining that an object is within the confidence region, and a probability of collision with the ADV based on a distance of the object to the ADV; and planning a trajectory based on the probability of collision, and controlling the ADV based on the probability of collision.

Abstract: One or more outputs from a planning module of an ADV are received. Data of a driving environment of the ADV is received. A performance of the planning module is evaluated by determining a score of the performance of the planning module based on the data of the driving environment and the one or more outputs from the planning module. Whether the one or more outputs from the planning module violates at least one of a set of safety rules is determined. The score is determined being larger than a predetermined threshold in response to determining that the one or more outputs from the planning module violate at least one of the set of safety rules. Otherwise, the score is determined based on a machine learning model. The planning module is modified by tuning a set of parameters of the planning module based on the score.

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.