Abstract: According to some embodiments, a system selects a number of polynomials representing a number of time segments of a time duration to complete the path trajectory. The system selects an objective function based on a number of cost functions to smooth speeds between the time segments. The system defines a set of constraints to the polynomials to at least ensure the polynomials are smoothly joined together. The system performs a quadratic programming (QP) optimization on the objective function in view of the set of constraints, such that a cost associated with the objective function reaches a minimum while the set of constraints are satisfied. The system generates a smooth speed for the time duration based on the optimized objective function to control the ADV autonomously.

Abstract: According to some embodiments, a system segments a first path trajectory selected from an initial location of the ADV into a number of path segments, where each path segment is represented by a polynomial function. The system selects an objective function in view of the polynomial functions of the path segments for smoothing connections between the path segments. The system defines a set of constraints to the polynomial functions based on adjacent path segments in view of at least a road boundary and an obstacle perceived by the ADV. The system performs a quadratic programming (QP) optimization on the objective function in view of the added constraints, such that an output of the objective function reaches a minimum. The system generates a second path trajectory representing a path trajectory with an optimized objective function based on the QP optimization to control the ADV autonomously.

Abstract: According to some embodiments, a system determines a number of boundary areas having predetermined dimensions centered around each of a number of control points of a first reference line. The system selects a number of two-dimensional polynomials each representing a segment of an optimal reference line between adjacent control points. The system defines a set of constraints to the two-dimensional polynomials to at least ensure the two-dimensional polynomials passes through each of the boundary areas. The system performs a quadratic programming (QP) optimization on a target function such that a total cost of the target function reaches minimum while the set of constraints are satisfied. The system generates a second reference line representing the optimal reference line based on the QP optimization to control the ADV autonomously according to the second reference line.

Abstract: In one embodiment, in response to a route from a source location to a target location, the route is analyzed to identify a list of one or more driving scenarios along the route that match one or more predetermined driving scenarios. The route is segmented into a list of route segments based on the driving scenarios. At least one of the route segments corresponds to one of the identified driving scenarios. A path is generated based on the route segments for driving an autonomous driving vehicle from the source location to the target location. The path includes a number of path segments corresponding to the route segments. At least one of the path segments of the path is determined based on a preconfigured path segment of a predetermined driving scenario associated with the path segment, without having to calculating the same at real time.

Abstract: According to one embodiment, in response to a search query received at a server from a client device for searching content, a search is performed in a content database or via a content server based on one or more search terms of the search query to identify a first list of one or more content items. A search is performed in an image store based on the one or more search terms to identify a list of one or more images. Each content item of the first list is associated with one of the images. A second list of one or more content items having at least a portion of the images integrated therein is generated. The second list of content items is transmitted to the client device, such that each content item of the first list is presented with one of the images.

Abstract: According to some embodiments, a system generates a number of possible decisions for routing the ADV from a first location to a second location based on perception information perceiving a driving environment surrounding the ADV, including one or more obstacles in view of a set of traffic rules. The system calculates a number of trajectories based on a combination of one or more of the possible decisions. The system calculates a total cost for each of the trajectories using a number of cost functions and selects one of the trajectories with a minimum total cost as the driving trajectory to control the ADV autonomously. The cost functions include a path cost function, a speed cost function, and an obstacle cost function.

Abstract: According to some embodiments, a system calculates a first trajectory based on a map and a route information. The system generates a path profile based on the first trajectory, traffic rules, and an obstacle information describing one or more obstacles perceived by the ADV, where for each of the obstacles, the path profile includes a decision to yield or nudge to left or right of the obstacle. The system generates a speed profile based on the path profile in view of the traffic rules. The system performs a gradient descent optimization based on the path profile and the speed profile to generate a second trajectory representing an optimized first trajectory and controls the ADV according to the second trajectory.

Abstract: In one embodiment, a routing request is received for routing an autonomous driving vehicle (ADV) from a source lane to a target lane. One or more road paths are determined from a source road to a target road. The road paths include zero or more intermediate roads in between, where each intermediate road includes one or more intermediate lanes. For each of the road paths, one or more lane paths are determined. Each lane path includes a number of lanes in combination to connect the source lane of the source road to the target lane of the target road via at least one of the intermediate lanes of the intermediate roads. A trajectory is planned from the source plane of the source road to the target lane of the target road using the lane paths to drive the ADV according to the trajectory.

Abstract: According to some embodiments, a system calculates a first trajectory based on a map and a route information. The system generates a path profile based on the first trajectory, traffic rules, and an obstacle information describing one or more obstacles perceived by the ADV. The system generates a speed profile based on the path profile, where the speed profile includes, for each of the obstacles, a decision to yield or overtake the obstacle. The system performs a quadratic programming optimization on the path profile and the speed profile to identify an optimal path with optimal speeds. The system generates a second trajectory based on the optimal path and optimal speeds to control the ADV autonomously according to the second trajectory.

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: In one embodiment, a lane assistant system is configured to provide lane assistance to a driver of an autonomous driving vehicle (ADV) based on the driver's intention determined at the point in time by capturing and analyzing user actions and driving environment surrounding the ADV. By analyzing the user actions and the driving environment surrounding the vehicle, the driver's intention can be determined. The driver's intention can be utilized to interpret whether the driver indeed intends to change lane. Based on the driver's intention, the lane assistance can be provided, either allowing the vehicle change or exit the current lane, or automatically providing warning or correction of the moving direction of the vehicle.

Abstract: In one embodiment, a first position associated with a set of rear wheels of an autonomous vehicle is determined based on global positioning system (GPS) data received from a GPS source. A moving direction of the autonomous vehicle is determined based on directional data received from an inertial measurement unit (IMU) onboard. A second position associated with a set of front wheels of the autonomous vehicle is calculated based on the first position and the moving direction of the autonomous vehicle. A route segment of a route is planned based on the second position as a current position of the autonomous vehicle. Planning and control data is generated for the route segment. The autonomous vehicle is controlled and driven based on the planning and control data.

Abstract: In one embodiment, when an ADV is driving on a road segment, a driving parameter is recorded in response to a first control command. A difference between the first driving parameter and a target driving parameter corresponding to the first control command is determined. In response to determining that the difference exceeds a predetermined threshold, a second control command is issued to compensate the difference and cause the ADV to drive with a second driving parameter closer to the target driving parameter. A slope status of the road segment is derived based on at least the second control command. Map data of a map corresponding to the road segment of the road is updated based on the derived slope status. The updated map can be utilized to generate and issue proper control commands in view of the slope status of the road when the ADV drives on the same road subsequently.

Abstract: In one embodiment, motion planning and control data is received, indicating that an autonomous vehicle is to move from a first point to a second point of a path. The motion planning and control data describes a plurality of routes from the first point to the second point within the path. For each of the routes, a simulation of the route is performed in view of physical characteristics of the autonomous vehicle to generate a simulated route. A controlling error is calculated, the controlling error representing a discrepancy between the route and the simulated route. One of the routes is selected based on controlling errors between the routes and associated simulated routes. The autonomous vehicle is operated to move from the first point to the second point according to the selected route.

Abstract: Driving parameters (e.g., speed, heading direction) that an autonomous driving vehicle (ADV) likely utilize as target driving parameters are grouped into a number of ranges and one of the driving parameters in each range is selected as a driving parameter representative or a target driving parameter representing the respective range or segment. For each of the target driving parameters representing the ranges, a particle swarm optimization method is utilized to derive a set of most optimized coefficients for a controller (e.g., speed controller, steering controller) for controlling an ADV. A driving parameter to coefficient (parameter/coefficient) mapping table is generated to map a particular driving parameter representing a range of driving parameter to a set of one or more coefficients of a particular controller. The parameter/coefficient mapping table is utilized at real-time to configure a controller in response to a particular target driving parameter using the corresponding coefficients.

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 computer system generates a first vehicular path based on map information. The system collects data points representing geographical coordinates of vehicles that drove on a vehicular path lane at different points in time. The system segments the first vehicular path into path segments based on the collected data points. For each of the path segments, the system applies a smoothing function to select a subset of the data points that are within a predetermined proximity of the corresponding path segment and calculates a segment reference point to represent the path segment by combining the selected data points. The segment reference points of the path segments of the first vehicular path are interpolated to generate a second vehicular path such that the second vehicular path is used as a reference line to control ADVs driving on the first vehicular path.

Abstract: State of an autonomous driving vehicle (ADV) is measured and stored for a location and speed of the ADV. Later, the state of the ADV is measured for the location and speed corresponding to a previously stored state of the ADV at the same location and speed. Fields of the measured stored states of the ADV are compared. If one or more differences between the measured and stored ADV states exceeds a threshold, then one or more control input parameters of the ADV is adjusted, such as steering, braking, or throttle. Differences may be attributable to road conditions or to state of servicing of the ADV. Differences between measured and stored states of the ADV can be passed to a service module. Service module can access crowd sourced data to determine whether one or more control input parameters for a driving state of one or more ADVs should be updated.

Abstract: A lane departure detection system detects that an autonomous driving vehicle (ADV) is departing from the lane in which the ADV is driving based on sensor data captured when the ADV contact a deceleration curb such as a speed bump laid across the lane. When the ADV contacts the deceleration curb, the lane departure detection system detects and calculates an angle of a moving direction of the ADV vs a longitudinal direction of the deceleration curb. Based on the angle, the system calculates how much the moving direction of the ADV is off compared to a lane direction of the lane. The lane direction is typically substantially perpendicular to the longitudinal direction of the deceleration curb. A control command such as a speed control command and/or a steering control command is generated based on the angle to correct the moving direction of the ADV.

Abstract: When generating a control command of an autonomous driving vehicle (ADV), a pitch status and/or a roll status of the road is determined. The control command is adjusted based on the pitch status and the roll status. For example, when an ADV is driving on an uphill or downhill road, a pitch status of the road is determined and a speed control command will be generated based on the pitch status of the road, such that the ADV have a similar acceleration rate as of driving on a flat road. Similarly, when the ADV is driving on a road that is tilted or rolled left or right, a roll status of the road is determined and a steering control command will be generated in view of the roll status of the road, such that the ADV have a similar heading direction as of driving on a flat road.