Abstract: When an ADV is detected to transition from a manual driving mode to an autonomous driving mode, a first pedal value corresponding to a speed of the ADV at a previous command cycle during which the ADV was operating in the manual driving mode is determined. A second pedal value is determined based on a target speed of the ADV at a current command cycle during which the ADV is operating in an autonomous driving mode. A pedal value represents a pedal percentage of a maximum pedal pressure or maximum pedal pressed distance of a throttle pedal or brake pedal from a neutral position. A speed command is generated and issued to the ADV based on the first pedal value and the second pedal value, such that the ADV runs in a similar acceleration before and after switching from the manual driving mode to the autonomous driving mode.

Abstract: In one embodiment, when speed control command (e.g., throttle, brake commands) is issued based on a target speed, a first feedback parameters is determined based on an expected speed and an actual speed of the ADV in response to the speed control command. A second feedback parameter is determined by applying a speed control parameter adjustment (SCPA) model to a set of input parameters that are captured or measured at the point in time. The set of input parameters represents a driving environment of the ADV at the point in time. One or more control parameters of a speed controller of the ADV is adjusted based on the first feedback parameter and the second feedback parameter, where the speed controller is configured to generate and issue speed control commands. Subsequent speed control commands can be generated based on the adjusted speed control parameters of the speed controller.

Abstract: In one embodiment, a driving scenario is determined for a next route segment or route for an autonomous vehicle. In response, a lookup operation is performed in a scenario/delay mapping table to locate a mapping entry that matches the determined driving scenario. The scenario/delay mapping table includes a number of mapping entries. Each mapping entry maps a particular driving scenario to a system delay that has been calculated for that particular driving scenario. Thereafter, the autonomous vehicle is controlled and driven in view of the system delay corresponding to the driving scenario at the point in time, including issuing control commands according to timing compensating the system delay under the circumstances.

Abstract: According to one embodiment, when perception data is received that perceives a driving environment of an ADV, the lane configuration of one or more lanes of a road is determined based on the perception data. A speed of the ADV and a lane location of the ADV within a lane in which the ADV is driving are determined based on the lane configuration. A driving scenario is derived based on the lane configuration, the speed of the ADV, and the lane location of the ADV. The door locks of one or more doors of the ADV are locked or unlocked based on the driving scenario. Whether to lock or unlock a door of the ADV may be determined according to a set of door lock control rules in view of the driving scenario at the particular point in time.

Abstract: In one embodiment, a request is received to turn the autonomous driving vehicle (ADV) from a first direction to a second direction. In response to the request, a number of segment masses of a number of segments of the ADV are determined. The segment masses are located on a plurality of predetermined locations within a vehicle platform of the ADV. A location of a mass center for an entire ADV is calculated based on the segment masses of the segments of the ADV, where the mass center represents a center of an entire mass of the entire ADV. A steering control command based on the location of the mass center of the entire ADV for steering control of the ADV.

Abstract: It is determined an ADV is inevitable to collide with at least one of the objects surrounding the ADV based on the relative positions and speeds between the ADV and the objects in view of the physical dimensions of the ADV. An object may be another vehicle, a pedestrian, a bicycle, a static obstacle. For each of the objects, a collision cost between the ADV and the object is calculated using a set of one or more cost functions or cost algorithms. A collision cost represents an amount of potential damage if the collision is to occur. One of the objects is selected whose collision cost is the least amongst the objects. A path or trajectory is planned for the ADV leading or towards to the selected object to minimize the potential damage when the ADV potentially collide with the selected object compared to the remaining objects.

Abstract: In one embodiment, planning data is received, for example, from a planning module, to drive an autonomous driving vehicle (ADV) from a starting location and a destination location. In response, a series of control commands are generated based on the planning data, where the control commands are to be applied at different points in time from the starting location to the destination location. A cost is calculated by applying a cost function to the control commands, a first road friction to be estimated in a current trip, and a second road friction estimated during a prior trip from the starting location to the destination location. The first road friction of the current trip is estimated using the cost function in view of a prior termination cost of the prior trip, such that the cost reaches minimum.

Abstract: Methods and systems for operating an autonomous driving vehicle (ADV) are disclosed. A current state of an ADV is sampled at a first time to obtain a set of parameters. A cost, from a cost function that reflects desired control goals, is generated for a future time horizon based at least in part on the set of parameters. The cost is minimized with one or more constraints to obtain target control input values. For each of the target control input values, a lookup operation is performed using the control input value to locate a first mapping entry that approximately corresponds to the control input value. A first control command is derived from the first mapping entry. The ADV is controlled using the derived first control command.

Abstract: In one embodiment, a lane departure detection system detects at a first point in time that a wheel of an ADV rolls onto a lane curb disposed on an edge of a lane in which the ADV is moving. The system detects at a second point in time that the wheel of the ADV rolls off the lane curb of the lane. The system calculates an angle between a moving direction of the ADV and a lane direction of the lane based on the time difference between the first point in time and the second point in time in view of a current speed of the ADV. The system then generates a control command based on the angle to adjust the moving direction of the ADV in order to prevent the ADV from further drifting off the lane direction of the lane.

Abstract: According to one embodiment, an ADV is controlled according to a first trajectory planned during a first planning cycle. A control error is determined which represents a drifting error at a first location of the ADV at a first point in time at the end of the first planning cycle. A second point in time is selected on the first trajectory. A second trajectory is generated from a second location on the first trajectory corresponding o the second point in time as a starting location of the second trajectory for a second planning cycle as a next planning cycle. A segment of the first trajectory between the first point in time and the second point in time is combined with the second trajectory to generate a third trajectory for the second planning cycle. The ADV is driven and controlled according to the third trajectory corresponding to the second planning cycle.

Abstract: In one embodiment, a driving scenario is identified for a next movement for an autonomous vehicle, where the driving scenario is represented by a set of one or more predetermined parameters. A first next movement is calculated for the autonomous vehicle using a physical model corresponding to the driving scenario. A sideslip predictive model is applied to the set of predetermined parameters to predict a sideslip of the autonomous vehicle under the driving scenario. A second next movement of the autonomous vehicle is determined based on the first next movement and the predicted sideslip of the autonomous vehicle. The predicted sideslip is utilized to modify the first next movement to compensate the sideslip. Planning and control data is generated for the second next movement and the autonomous vehicle is controlled and driven based on the planning and control data.

Abstract: In one embodiment, motion planning and control data is received, where the motion planning and control data indicates that an autonomous vehicle is to move from a first point to a second point of a path within a predetermined route. In response to the motion planning and control data, the path from the first point to the second point is segmented into multiple path segments. For each of the path segments, one of predetermined driving scenes is identified that matches motion characteristics of the corresponding path segment. The motion planning and control data associated with the path segments is modified based on predetermined motion settings of the path segments. The autonomous vehicle is driven through the path segments of the path based on the modified motion planning and control data.

Abstract: In one embodiment, a steering control delay is measured, where the steering delay represents the delay between the time of issuing a steering control command and the time of a response from one or more wheels of an autonomous vehicle. A speed control delay is measured between the time of issuing a speed control command and the time of a response from one or more wheels of the autonomous vehicle or the time of supplying pressure to the gas pedal or brake pedal. In response to a given route subsequently, an overall system delay is determined based on the steering control delay and the speed control delay using a predetermined algorithm. Planning and control data is generated in view of the system delay for operating the autonomous vehicle.

Abstract: According to one embodiment, a content item is received that was identified in response to a search query received from a client. It is determined whether the search query is within a predetermined percentage of total search queries that yield the content item. In response to determining that the search query is within the predetermined percentage of the total search queries, a search is performed in an image store based on one or more keywords associated with the search query to identify a list of one or more images and one of the identified images is selected to be matched with the content item. Otherwise, a predetermined preferred image is selected to be matched with the content item. The content item is incorporated with the selected image to generate a composite content item to be transmitted to the client.

Abstract: According to one embodiment, a content analysis module receives content information of a content item, the content information including a plurality of first keywords associated with the content item. The content analysis module performs an analysis on the first keywords associated with the content item. A keyword classifier categories the first keywords into a plurality of categories based on the analysis, each category including one or more second keywords selected from the first keywords. For each of the categories, one or more images are identified based on the corresponding second keywords. A category/image mapping module generates a category/image mapping table for the content item to map each of the categories to the corresponding identified images. The category/image mapping table is utilized to match the content item to one of the images based on a subsequent category determined in response to a search query.

Abstract: According to one embodiment, in response to a search query received from a client device for searching content, one or more first keywords are determined from the search query. A lookup operation is performed in an image-to-keyword (image/keyword) mapping table based on the first keywords. The image/keyword mapping table includes a plurality of entries, each entry mapping an image identifier (ID) identifying an image to one or more second keywords. For each of images identified in the image/keyword mapping table, a matching degree is determined between the first keywords and the second keywords. A ranking process is performed based on matching degrees on a plurality of images identified in the image/keyword mapping table whose second keywords match at least a portion of the first keywords.

Abstract: According to one embodiment, a first set of one or more images are identified as a list of image candidates to be matched with a content item that is identified in response to a search query, where the content item is provided by a first content provider. A first lookup operation is performed in an image whitelist table to identify a second set of one or more images that are associated with a second content provider other than the first content provider. Any image candidate that is included in the second set of the images is removed from the list of image candidates. One of the candidate images is then selected to be associated with the content item.

Abstract: In one embodiment, a number of steering rate candidates are determined for a steering control command of operating an autonomous vehicle. For each of the steering rate candidates, a steering rate cost is calculated for the steering rate candidate by applying a predetermined cost function, including calculating a first cost for the steering rate candidate based on a difference between a target steering position and a current steering position of the autonomous vehicle using a first predetermined cost function. One of the steering rate candidates having a lowest steering rate cost is selected as a target steering rate. A steering control command is generated based on the selected steering rate candidate to control a steering wheel of the autonomous vehicle.

Abstract: Described is a system and method that provides the ability for an autonomous driving vehicle (ADV) to determine (or estimate) one or more control characteristics for the ADV. In order to determine these control characteristics, the system may perform one or more driving maneuvers such as an acceleration or deceleration maneuver, and a constant velocity maneuver. By performing these maneuvers using various known forces, the system may then perform various calculations to obtain one or more unknown characteristics. For example, the system may determine as estimated mass of the ADV, and as a result, adjust (or tune) various controls of the ADV based on the estimated mass.

Abstract: According to one embodiment, a first keyword-to-image (keyword/image) mapping table is provided. The first keyword mapping table includes a number of entries, each entry mapping a keyword to one or more image identifiers (IDs) identifying one or more images. For each of the keywords of the first keyword/image mapping table, an analysis is performed on the keyword to determine one or more related keywords that are related to the keyword. One or more additional entries corresponding to the one or more related keywords are generated to be incorporated into the first keyword/image mapping table to generate a second keyword/image mapping table. The second keyword/image mapping table is utilized to associated a particular image to a particular content item related to a particular keyword.