Abstract: A sensor aggregation framework for autonomous driving vehicles is disclosed. In one embodiment, sensor data is collected from one or more sensors mounted on an autonomous driving vehicle (ADV) while the ADV is moving within a region of interest (ROI) that includes a number of obstacles. The sensor data includes obstacle information of the obstacles and vehicle data of the ADV. Each of the vehicle data is timestamped with a current time at which the vehicle data is captured to generate a number of timestamps that correspond to the vehicle data. The obstacle information, the vehicle data, and the corresponding timestamps are aggregated into training data. The training data is used to train a set of parameters that is subsequently utilized to predict at least in part future obstacle behaviors and vehicle movement of the ADV.

Abstract: In one embodiment, a system receives vehicle information from one or more ADVs. The system determines a location and a heading of each ADV from the vehicle information of the ADV. For each of the ADVs, the system determines if the ADV is approaching a traffic light junction based on the location and the heading of the ADV. The system sends the vehicle information of the ADVs to a traffic light control system in response to determining the ADV is approaching the traffic light junction, where the vehicle information is used by the traffic light control system to direct a traffic flow at the traffic light junction, including adjusting a time duration of a light signal at one or more traffic lights disposed at the traffic light junction in advance of the ADVs arriving at the traffic light junction.

Abstract: In one embodiment, a system of an ADV perceives a driving environment surrounding the ADV using a plurality of sensors mounted on the ADV. The system identifies a blind spot based on the perceived driving environment surrounding the ADV. The system in response to identifying the blind spot, receives an image having the blind spot from an image capturing device disposed within a predetermined proximity of the blind spot. In some embodiments, the system receives the image having the blind spot from a remote server communicatively coupled to the image capturing device. The system identifies an obstacle of interest at the blind spot of the ADV based on the image. The system generates a trajectory based on the obstacle of interest at the blind spot to control the ADV to avoid the obstacle of interest.

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 real time map can be generated by an autonomous driving vehicle (ADV) based on a navigation guideline for a lane and associated lane boundaries for the lane on a particular segment of a road. When travelling in the lane on the road segment, the ADV can use the navigation guideline as a reference line and use the lane boundaries as boundaries. The navigation guideline can be derived from manual driving path data collected by a manned vehicle that has travelled multiple times on the particular segment of the road.

Abstract: In one embodiment, one or more first data items associated with a planned trip of a user riding in an autonomous driving vehicle (ADV) are displayed on a display device within the ADV. Each of the first data items is associated with a user selectable option to indicate whether the user wishes or allows the ADV to store each of the first data items in a persistent storage device. User inputs are received via a user interface such as touch screen of the display device, including a first selection indicating that the user wishes to store a first subset of the first data items. In response to the first selection, the first subset of the data items is stored in the persistent storage device of the ADV.

Abstract: According to one embodiment, a driving environment surrounding an ADV is perceived based on sensor data obtained from various sensors mounted on the ADV including detecting one or more obstacles. The obstacles of the detected obstacles are determined and tracked based on the perception process, where the obstacle states of the obstacles may be maintained in an obstacle state buffer associated with the obstacles. When it is detected that a first moving obstacle is blocked by an object by the sensors, the further movement of the first moving obstacle is predicted based on the prior obstacle states of the first moving obstacle, while the first moving obstacle is blocked in view by the object. A trajectory is planned for the ADV in view of the predicted movement of the first moving obstacle while the first moving obstacle is in the blind area.

Abstract: According to one embodiment, during a first planning cycle, a first lane boundary of a driving environment perceived by an ADV is determined using a first lane boundary determination scheme (e.g., current lane boundary), which has been designated as a current lane boundary determination scheme. A first trajectory is planned based on the first lane boundary to drive the ADV to navigate through the driving environment. The first trajectory is evaluated against a predetermined set of safety rules (e.g., whether it will collide or get too close to an object) to avoid a collision with an object detected in the driving environment. In response to determining that the first trajectory fails to satisfy the safety rules, a second lane determination boundary of the driving environment is determined using a second lane boundary determination scheme and a second trajectory is planned based on the second lane boundary to drive the ADV.

Abstract: In one embodiment, a system sends current location information of the ADV to an alert service over a network, where the alert service is communicatively coupled to a number of ADVs. The system receives a broadcasted alert signal from the alert service, where the alert service has determined that the ADV is or will be located in an alert area, and the location of the alert area is determined based on a location of a dispatched vehicle having a higher priority of traveling. In response to receiving the broadcast alert signal, the system examines a current state and the current location of the ADV in view of the alert area to determine whether the ADV should overtake or yield the alert area based on a set of rules. The system generates a trajectory to control the ADV to navigate the alert area based on the examination.

Abstract: A new cost design is disclosed for evaluating candidate path curves for navigating an autonomous driving vehicle (ADV) through a segment of a route which may include an obstacle. Each point on each candidate path curve has a plurality of attributes having logical values and an associated priority of evaluation, and at least one numeric attribute having an associated priority of evaluation. A cost for each path curve is determined using the attributes and priorities, and a least cost path curve is selected using the attributes and priorities. By comparing attribute values in accordance with priority, and utilizing logical values, the efficiency of determining path curve cost and selecting a least cost path curve is substantially improved.

Abstract: Described is a system (and method) for providing a flexible decision system for autonomous driving. The system may include a framework that allows a decision system to switch between a deliberate rule-based decision framework and an intuitive machine-learning model-based decision framework. The system may invoke the appropriate framework (or subsystem) based on a particular set of driving conditions or environment. Accordingly, the system described herein may provide an efficient and adaptable decision system for autonomous driving.

Abstract: According to some embodiments, a system operates an ADV. In one embodiment, the system perceives a driving environment surrounding the ADV based on sensor data obtained from a plurality of sensors, including perceiving a moving obstacle that is moving relative to the ADV. The system projects the moving obstacle as a figure onto a station-time (ST) coordinate system, wherein the ST coordinate system indicates a distance between the figure and a reference point at different points in time. And the system, for each of a plurality of predetermined processing time intervals, determines two points of the figure in the ST coordinate system to represent a shape of the figure, wherein the shape of the figure is utilized to plan a trajectory to drive the ADV to avoid colliding with the moving obstacle.

Abstract: In one embodiment, sensor data are collected from one or more sensors mounted on an autonomous driving vehicle (ADV) while the ADV is moving within a region of interest (ROI) that includes a number of obstacles. The collected sensor data are operated on to obtain obstacle data associated with the obstacles, location data, and a number of timestamps that correspond to the obstacle data and the location data. For each of the timestamps, positions of the obstacles are mapped to some of the obstacle data that correspond to the timestamp based on the location data, thereby generating mapped information of the obstacles. The mapped information is automatically labelled to generate labelled data, where the labelled data is utilized to subsequently train a machine learning algorithm to recognize obstacles during autonomous driving of an ADV.

Abstract: In one embodiment, a method, apparatus, and system for automatically detecting and notifying a user of a traffic rule violation using sensors and a perception module of an autonomous driving vehicle is disclosed. The operations performed comprise: capturing surrounding road traffic information at an autonomous driving vehicle (ADV) based on sensor data obtained from a plurality of sensors of the ADV; automatically detecting at the ADV a traffic rule violation of a first vehicle within a perception range of the ADV based on the surrounding road traffic information and a set of pre-configured traffic rules maintained by the ADV, including analyzing the surrounding road traffic information in view of the pre-configured traffic rules; and generating an alert in response to the detected traffic rule violation.

Abstract: In one embodiment, systems and methods are disclosed for a planning-driven framework for an driving vehicle (ADV) driving decision system. Driving decisions are classified into at least seven categories, including: conservative decision, aggressive decision, conservative parameters, aggressive parameters, early decision, late decision, and non-decision problem. Using the outputs of an ADV decision planning module, an ADV driving decision problem is identified, categorized, and diagnosed. A local driving decision improvement can be determined and executed in a short time frame on the ADV. For a long term solution, if needed, the driving decision problem can be uploaded to an analytics server. The driving decision problems from a large plurality of ADVs can be aggregated and analyzed for improving the ADV decisions system for all ADVs.

Abstract: In one embodiment, a method, apparatus, and system for trajectory planning for autonomous driving in an autonomous driving vehicle equipped with a low accuracy localization and perception module is disclosed.

Abstract: A real-time perception adjustment and correction and driving adaption method for autonomous driving is provided. The system will analyze the driving behaviors of surrounding vehicles surrounding an ADV, which is utilized to improve its original perception based on the analysis and to adapt to the updated driving environment. In one embodiment, in addition to the perception information provided by the sensors, the system analyzes the behaviors of the surrounding vehicles based on the perception information. Based on the behaviors of the surrounding vehicles, the system may detect there is may be an obstacle that has not been detected based on the perception information. Alternatively, the system may detect that an obstacle determined based on the perception information actually may not exist based on the behaviors of the surrounding vehicles. The paths created for the ADV may then adjusted accordingly to improve the autonomous driving of the ADV.

Abstract: According to various embodiments, systems and methods described in the disclosure combine mapped features with point cloud features to improve object detection precision of an autonomous driving vehicle (ADV). The map features and the point cloud features can be extracted from a perception area of the ADV within a particular angle view at each driving cycle based on a position of the ADV. The map features and the point cloud features can be concatenated and provided to a neutral network for object detections.

Abstract: In response to sensor data received from sensors mounted on an autonomous vehicle, a surrounding environment is perceived based on the sensor data. The surrounding environment includes multiple sub-environments. For each of the sub-environments, one of a plurality of driving scenario handlers associated with the sub-environment is identified, each driving scenario handler corresponding to one of a plurality of driving scenarios. The identified driving scenario handler is invoked to determine an individual driving condition within the corresponding sub-environment. An overall driving condition for the surrounding environment is determined based on the individual driving conditions provided by the identified driving scenario handlers. A route segment is planned based on the overall driving condition of the surrounding environment, the route segment being one of a plurality of route segments associated with a route. The autonomous vehicle is controlled and driven based on the planned route segment.

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).