Abstract: A server may determine whether traffic control devices in an environment have changed, based on one or more report messages and sensor data. If the traffic control devices in an environment have changed, the server may generate updated map data and transmit the updated map data to an autonomous driving vehicle (ADV).

Abstract: System and method for training a machine learning model are disclosed. In one embodiment, for each of the driving scenarios, responsive to sensor data from one or more sensors of a vehicle and the driving scenario, driving statistics and environment data of the vehicle are collected while the vehicle is driven by a human driver in accordance with the driving scenario. Upon completion of the driving scenario, the driver is requested to select a label for the completed driving scenario and the selected label is stored responsive to the driver selection. Features are extracted from the driving statistics and the environment data based on predetermined criteria. The extracted features include some of the driving statistics and some of the environment data collected at the different points in time during the driving scenario.

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 system/method generates a driving trajectory for an autonomous driving vehicle (ADV). The system perceives an environment of an autonomous driving vehicle (ADV). The system determines one or more bounding conditions based on the perceived environment. The system generates a first trajectory using a neural network model, wherein the neural network model is trained to generate a driving trajectory. The system evaluates/determines if the first trajectory satisfies the one or more bounding conditions. If the first trajectory satisfies the one or more bounding conditions, the system controls the ADV autonomously according to the first trajectory. Otherwise, the system controls the ADV autonomously according to a second trajectory, where the second trajectory is generated based on an objective function, where the objective function is determined based on at least the one or more bounding conditions.

Abstract: In one embodiment, a system monitors states of an autonomous driving vehicle (ADV) using a number of sensors mounted on the ADV. The system perceives a driving environment surrounding the ADV using at least a portion of the sensors. The system analyzes the states in view of the driving environment to determine a real-time traffic condition at a point in time. The system determines whether the real-time traffic condition of the driving environment matches at least a predetermined traffic condition. The system transmits data concerning the real-time traffic condition to a remote server over a network to allow the remote server to generate an updated map having real-time traffic information, in response to determining the real-time traffic condition is unknown. In response to receiving the updated map, the system plans and controls the ADV based on the updated map.

Abstract: In one embodiment, instead of using map data, a relative coordinate system is utilized to assist perception of the driving environment surrounding an ADV for some driving situations. One of such driving situations is driving on a highway. Typically, a highway has fewer intersections and exits. The relative coordinate system is utilized based on the relative lane configuration and relative obstacle information to control the ADV to simply follow the lane and avoid potential collision with any obstacles discovered within the road, without having to use map data. Once the relative lane configuration and obstacle information have been determined, regular path and speed planning and optimization can be performed to generate a trajectory to drive the ADV. Such a perception system is referred to as a relative perception system based on a relative coordinate system.

Abstract: A social driving style learning framework or system for autonomous vehicles is utilized, which can dynamically learn the social driving styles from surrounding vehicles and adopt the driving style as needed. Each of the autonomous vehicles within a particular driving area is equipped with the driving style learning system to perceive the driving behaviors of the surrounding vehicles to derive a set of driving style elements. Each autonomous vehicle transmits the driving style elements to a centralized remote server. The server aggregates the driving style elements collected from the autonomous vehicles to determine a driving style corresponding to that particular driving area. The server transmits the driving style back to each of the autonomous vehicles. The autonomous vehicles can then decide whether to adopt the driving style, for example, to follow the traffic flow with the rest of the vehicles nearby.

Abstract: According to some embodiments, a system receives a first control command and a speed measurement of the ADV. The system determines an expected acceleration of the ADV based on the speed measurement and the first control command. The system receives an acceleration measurement of the ADV. The system determines a feedback error based on the acceleration measurement and the expected acceleration. The system updates a portion of the calibration table based on the determined feedback error. The system generates a second control command to control the ADV based on the calibration table having the updated portion to control the ADV autonomously according to the second control command.

Abstract: An autonomous driving vehicle (ADV) may determine a predicted path for a moving obstacle and speeds for different portions of the path. The ADV use multiple threads in parallel to determine the path and speeds for the different portions of the path.

Abstract: In some implementations, a method is provided. The method includes determining a path for an autonomous driving vehicle. The path is located within a first lane of an environment in which the autonomous driving vehicle is currently located. The method also includes obtaining sensor data. The sensor data indicates a set of speeds for a set of moving obstacles located in a second lane of the environment and wherein the second lane is adjacent to the first lane. The method further includes determining whether the set of speeds is lower than a threshold speed. The method further includes determining a new speed for the autonomous driving vehicle in response to determining that the set of speeds is lower than the threshold speed. The method further includes controlling the autonomous driving vehicle based on the path and the new speed.

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: 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: 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: According to one embodiment, perception data describing a set of trajectories driven by a number of vehicles is received at a server from the vehicles or data collection agents over a network. The vehicles were driving through a road segment of a road over a period of time and their driving trajectories were captured. A trajectory analysis is the performed on the perception data using a set of rules to determine driving behaviors of the corresponding vehicles. A lane configuration of the road segment is then determined based on the driving behaviors. A map segment of a navigation map is then updated based on the lane configuration of one or more lanes within the road segment. A higher definition map can be generated based on the updates of the navigation map and the lane configuration, which can be utilized to autonomously drive an ADV subsequently.

Abstract: In one embodiment, a method, apparatus, and system may predict behavior of environmental objects using machine learning at an autonomous driving vehicle (ADV). One or more yield/overtake decisions are made with respect to one or more objects in the ADV's surrounding environment using a data processing architecture comprising at least a first, a second, and a third neural networks, the first, the second, and the third neural networks having been trained with a training data set. Driving signals are generated based at least in part on the yield/overtake decisions to control operations of the ADV.

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, 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: 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 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, 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.