Abstract: In one embodiment, it is determined that an ADV is about to decelerate based on perception of a driving environment surrounding the ADV. In addition, if there is another vehicle that is following the ADV, a distance between the ADV and the following vehicle, as well as the speed of the following vehicle, is determined. A deceleration rate that is required for the following vehicle to avoid a collision with the ADV is determined based on the distance between the ADV and the following vehicle and the speed of the following vehicle. If the deceleration rate is greater than a predetermined threshold, a brake light and an emergency light of the ADV are turned on to warn the following vehicle that the ADV is about to rapidly decelerate as it is treated as an emergency situation.

Abstract: In one embodiment, an autonomous driving vehicle (ADV) steering control system determines how much and when to apply a steering control to maneuver obstacles of a planned route. The steering control system calculates a first steering angle based on a target directional angle and an actual directional angle of the ADV, a second steering angle based on a target lateral position and an actual lateral position of the ADV to maneuver a planned route, an object, or an obstacle course. The steering control system determines a target steering angle based on the first steering angle and the second steering angles and utilizes the target steering angle to control a subsequent steering angle of the ADV.

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: 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: 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, 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: A set of driving scenarios are determined for different types of vehicles. Each driving scenario corresponds to a specific movement of a particular type of autonomous vehicles. For each of the driving scenarios of each type of autonomous vehicles, a set of driving statistics is obtained, including driving parameters used to control and drive the vehicle, a driving condition at the point in time, and a sideslip caused by the driving parameters and the driving condition under the driving scenario. A driving scenario/sideslip mapping table or database is constructed. The scenario/sideslip mapping table includes a number of mapping entries. Each mapping entry maps a particular driving scenario to a sideslip that is calculated based on the driving statistics. The scenario/sideslip mapping table is utilized subsequently to predict the sideslip under the similar driving environment, such that the driving planning and control can be compensated.

Abstract: According to one embodiment, when an ADV transitions from a manual driving mode to an autonomous driving mode, a first speed reference is determined based on a current position of the ADV. The current position of the ADV is dynamically measured in response to a speed control command issued in a previous command cycle and a target speed of a current command cycle. A second speed reference is determined based on a current target position for a current command cycle. A speed control command is then generated for controlling the speed of the ADV in the autonomous driving mode based on the first speed reference, the second speed reference, and the target speed of the ADV for the current command cycle, such that the ADV operates in a similar acceleration rate or deceleration rate before and after transitioning from the manual driving mode to the autonomous driving mode.

Abstract: In one embodiment, driving statistics of an autonomous vehicle are collected. The driving statistics include driving commands, speeds, and changes of speeds in response to the driving commands at different points in time represented by one or more command cycles. Command delay candidates for the autonomous vehicle are determined, each of the command delay candidates represented by one or more command cycles. For each of the command delay candidates, a percentage is calculated for driving commands that resulted in a response of the autonomous vehicle conforming to the driving commands associated with the command delay candidate. One of the command delay candidates having the highest percentage of conformity is selected as the command delay for the autonomous vehicle. The command delay is utilized to plan and control subsequent operations of the autonomous vehicle.

Abstract: In one embodiment, an autonomous driving vehicle (ADV) speed following system determines how much and when to apply a throttle or a brake control of an ADV to maneuver the ADV around, or to avoid, obstacles of a planned route. The speed following system calculates a first torque force to accelerate the ADV, a second torque force to counteract frictional forces and wind resistances to maintain a reference speed, and a third torque force to minimize an initial difference and external disturbances thereafter between predefined target speed and actual speed of the ADV over a planned route. The speed following system determines a throttle-brake torque force based on the first, second, and third torque forces and utilizes the throttle-brake torque force to control a subsequent speed of the ADV.

Abstract: A set of driving scenarios are determined for different types of vehicles. Each driving scenario corresponds to a specific movement of a particular type of autonomous vehicles. For each of the driving scenarios of each type of autonomous vehicles, a set of driving statistics is obtained, including driving parameters used to control and drive the vehicle, a driving condition at the point in time, and a sideslip caused by the driving parameters and the driving condition under the driving scenario. A driving scenario/sideslip mapping table or database is constructed. The scenario/sideslip mapping table includes a number of mapping entries. Each mapping entry maps a particular driving scenario to a sideslip that is calculated based on the driving statistics. The scenario/sideslip mapping table is utilized subsequently to predict the sideslip under the similar driving environment, such that the driving planning and control can be compensated.

Abstract: In one embodiment, an autonomous driving vehicle (ADV) steering control system determines how much and when to apply a steering control to maneuver obstacles of a planned route. The steering control system calculates a first steering angle based on a target directional angle and an actual directional angle of the ADV, a second steering angle based on a target lateral position and an actual lateral position of the ADV to maneuver a planned route, an object, or an obstacle course. The steering control system determines a target steering angle based on the first steering angle and the second steering angles and utilizes the target steering angle to control a subsequent steering angle of the ADV.

Abstract: In one embodiment, an autonomous vehicle is operated in an autonomous mode, where during the autonomous mode, the autonomous vehicle operates without influence of a human driver riding the autonomous vehicle. An electrical current is detected flowing through a first electrode and a second electrode mounted on a steering wheel of the autonomous vehicle. In response to the detecting the electrical current flowing through the first and second electrodes, the autonomous vehicle is transitioned from the autonomous mode to a manual mode. Thereafter, the autonomous vehicle is operated in the manual mode in response to user actions from the human driver.

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: 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 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: In one embodiment, driving statistics of an autonomous vehicle are collected. The driving statistics include driving commands, speeds, and changes of speeds in response to the driving commands at different points in time represented by one or more command cycles. Command delay candidates for the autonomous vehicle are determined, each of the command delay candidates represented by one or more command cycles. For each of the command delay candidates, a percentage is calculated for driving commands that resulted in a response of the autonomous vehicle conforming to the driving commands associated with the command delay candidate. One of the command delay candidates having the highest percentage of conformity is selected as the command delay for the autonomous vehicle. The command delay is utilized to plan and control subsequent operations of the autonomous vehicle.

Abstract: According to one embodiment, a request to decelerate an ADV from a first location to stop at a second location is received. In response to the request, a first zone and a second zone are determined within a distance between the first location and the second location. The ADV is controlled to decelerate based on a first deceleration rate from a current speed at the first location to a predetermined speed within the first zone. The ADV then decelerates from the predetermined speed based on a second deceleration rate from the predetermined speed to a full stop within the second zone. The first deceleration rate and the second deceleration rate are different. The first deceleration rate is sufficient enough to slow down the vehicle to a degree within the first zone that ensures the vehicle to fully stop at the second location as planned.

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, 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.