Abstract: Vehicle dynamics related parameter(s) of an autonomous vehicle model can be calibrated so that the autonomous vehicle model can more accurately determine the driving related behaviors of the autonomous vehicle. An example method comprises obtaining, from sensor data, vehicle related parameters; performing a first determination of a slope of a road and a banking angle of the road based on a pitch angle of the vehicle and a roll angle of the vehicle, respectively; performing a second determination of a set of parameters that describe a driving-related operation of the vehicle; performing a third determination that at least one difference between at least one value from the set of parameters and a corresponding parameter from the plurality of vehicle related parameters exceeds at least one threshold value; and obtaining, in response to the third determination, a calibrated longitudinal dynamic-related parameter or a calibrated lateral dynamic-related parameter.

Abstract: Described is a two-level optimal path planning process for autonomous tractor-trailer trucks which incorporates offline planning, online planning, and utilizing online estimation and perception results for adapting a planned path to real-world changes in the driving environment. In one aspect, a method of navigating an autonomous vehicle includes determining, by an online server, a current vehicle state of the autonomous vehicle in a mapped driving area. The method includes receiving, by the online server from an offline path library, a path for the autonomous driving vehicle through the mapped driving area from the current vehicle state to a destination vehicle state, and receiving fixed and moving obstacle information. The method includes adjusting the path to generate an optimized path that avoids the fixed and moving obstacles and ends at a targeted final vehicle state, and navigating the autonomous vehicle based on the optimized path.

Abstract: Devices, systems, and methods for predictive anti-lag braking control for autonomous vehicles are described. An example method of controlling a vehicle includes receiving, from a controller, a first plurality of values indicative of a brake wheel torque demand associated with a deceleration profile of the vehicle, deriving, based on the first plurality of values and a steady-state brake model, a second plurality of values indicative of a brake pressure demand, wherein an input to the steady-state brake model comprises the brake pressure demand and at least one previous brake wheel torque demand, and wherein an output of the steady-state brake system comprises a current brake wheel torque demand, generating, based on the second plurality of values and at least the steady-state model, a plurality of brake control commands, and controlling, using the plurality of brake control commands, the air brake system.

Abstract: Devices, systems, and methods for estimating wheel torque in an autonomous vehicle include a method for controlling a vehicle includes using a dual filter framework to generate an updated estimate of a wheel torque, and controlling, based on the updated estimate of the wheel torque, the vehicle. A first filter of the dual filter framework is configured to generate a blended estimate of the wheel torque by combining a first estimate of the wheel torque that is generated based on a set of mechanical torque transfer models of at least a powertrain and a brake of the vehicle and a second estimate of wheel torque that is generated based on an inverted longitudinal dynamic model, and a second non-linear filter of the dual filter framework is configured to generate the updated estimate of a wheel torque and vehicle velocity based on a torque delivery damping process model on vehicle motion.

Abstract: Techniques are described for analyzing autonomous vehicle driving. An example technique includes receiving, by a computer, a set of data from a software that performs driving related operations for an autonomous vehicle; generating a plurality of frames using the timestamps associated with the set of data; determining, for each frame, that the at least one data indicates information related to the autonomous vehicle and/or the one or more objects; assigning, for each frame, a label associated with the information indicated by the at least one data; and displaying, using a graphical user interface (GUI) and for the test performed with the software, at least one label associated with at least one information in a frame.

Abstract: Devices, systems, and methods for controlling a vehicle are described. An example method for controlling a vehicle includes obtaining planning information relating to an intended operation of the vehicle, the intended operation relating to an intended value of an operation parameter of the vehicle; obtaining, based on the intended operation of the vehicle, context information relating to an environment in which the vehicle is to operate following the planning information; determining a context compensated control instruction based on the planning information and the context information; obtaining feedback relating to a deviation of a real-time value of the operation parameter of the vehicle operating according to the context compensated control instruction from the intended value of the operation parameter relating to the intended operation; determining a corrected control instruction based on the feedback; and operating the vehicle based on the corrected control instruction.

Abstract: Devices, systems, and methods for integrated predictive dynamic control of a vehicle powertrain in an autonomous vehicle are described. An example method for controlling a vehicle includes generating, based on performing an optimization on a blended smooth wheel domain fuel consumption map subject to a modified torque availability constraint, one or more wheel domain control commands, converting the one or more wheel domain control commands to one or more powertrain-executable engine domain control commands, and transmitting the one or more powertrain-executable engine domain control commands to a powertrain of the vehicle, the powertrain configured to operate a plurality of gears, wherein the one or more powertrain-executable engine domain control commands enable the vehicle to track a reference kinematic trajectory associated with a vehicle speed driving plan within a predetermined tolerance.

Abstract: Methods, systems and apparatus for autonomous vehicle path planning and path navigation are described. One example system includes an offline server configured to generate a library of optimal paths for navigating a geographic area, wherein the geographic area is represented as a grid node map and an orientation grid bin map and wherein the optimal paths correspond to paths between pairs of grid node pairs in the grid node map based on optimization criteria, a storage device on the autonomous vehicle for storing the library of optimal paths, and an online server located on the autonomous vehicle configured to access information from the library of optimal paths from the storage device based on a current position and a current heading of the autonomous vehicle, and navigating the autonomous vehicle through the geographic area based on the information.

Abstract: An example method for controlling a vehicle includes obtaining reference information relating to an operation parameter of the vehicle, the operation parameter describing mission waypoints of the vehicle at respective time points during which the vehicle is to traverse a path, the reference information including reference values of the operation parameter corresponding to the time points; obtaining context information of the vehicle that relates to a state of the vehicle during an operation of the vehicle at the respective time points or an environment enclosing the path; determining tolerable ranges of the operation parameter for the time points based on the reference information and the context information; obtaining penalty information relating to differences between respective tolerable ranges and corresponding values of a constraint at the time points; determining a control instruction based on the tolerable ranges and the penalty information; and operating the vehicle based on the control instruction.

Abstract: Devices, systems, and methods for controlling a vehicle are described. An example method for controlling a vehicle includes obtaining planning information relating to an intended operation of the vehicle over a prediction horizon; inputting the planning information into an uncertainty model to determine uncertainty information, wherein: the uncertainty model is trained using sample driving event data based on a multivariate probability prediction algorithm; and the uncertainty model is configured to predict the uncertainty information that relates to a deviation of an operation of the vehicle according to an intended control instruction from the intended operation, the intended control instruction being determined based on the planning information; generating a control instruction based on the planning information and the uncertainty information; and operating the vehicle based on the control instruction.

Abstract: An example method of controlling a vehicle using a multi-node computational architecture includes receiving, by a first computational node, waypoints and vehicle states, and generating a first control command set for motion of the vehicle in a lateral direction with a first complexity and a second control command set for motion in a longitudinal direction with a second complexity that is less than the first complexity. A second computational node, operating in parallel with the first computational node, is used to generate a third control command set for motion in the longitudinal direction with the first complexity. The method further includes selecting, by a control arbitrator and based on the vehicle states and health status indications of the first and second computational nodes, either the second control command set or the third control command set, and outputting the selected control command set, which is used to control the vehicle.

Abstract: Described is a two-level optimal path planning process for autonomous tractor-trailer trucks which incorporates offline planning, online planning, and utilizing online estimation and perception results for adapting a planned path to real-world changes in the driving environment. In one aspect, a method of navigating an autonomous vehicle includes determining, by an online server, a current vehicle state of the autonomous vehicle in a mapped driving area. The method includes receiving, by the online server from an offline path library, a path for the autonomous driving vehicle through the mapped driving area from the current vehicle state to a destination vehicle state, and receiving fixed and moving obstacle information. The method includes adjusting the path to generate an optimized path that avoids the fixed and moving obstacles and ends at a targeted final vehicle state, and navigating the autonomous vehicle based on the optimized path.

Abstract: Methods, systems, and devices related to a method of controlling an autonomous diesel-engine vehicle. In one example aspect, the method includes determining longitudinal dynamic response properties of the autonomous vehicle. A brake mode is selected for reducing a current speed of the autonomous vehicle to a lower speed, based on a threshold that is determined using the longitudinal dynamic response properties of the vehicle. When a rate of speed reduction is equal to or smaller than the threshold, the brake mode includes only an engine brake in which engine exhaust valve opening is adjusted for reducing the current speed. When the rate of speed reduction is greater than the threshold, the brake mode incudes a combination of the engine brake and the foundation brake.

Abstract: Methods, systems, and devices related to a method of controlling an autonomous vehicle, in particular, an autonomous diesel-engine truck are disclosed. In one example aspect, the method includes determining an available engine brake torque generation mechanism for reducing a current speed of the autonomous vehicle to a lower speed and selecting a brake mode corresponding to the engine brake torque availability. In case a rate of speed reduction is equal to or smaller than a threshold, the brake mode includes only an engine brake in which engine exhaust valve opening is adjusted for reducing the current speed. The threshold determined in part based on the available engine brake torque, gear position of the transmission, and the online estimated vehicle longitudinal dynamic model. In case the rate of speed reduction is greater than the threshold, the brake mode incudes a combination of the engine brake and the foundation brake.

Abstract: Techniques are described for performing vehicle operation that comprises determining, by a computer located in a vehicle, a set of values of a predicted trajectory along which the vehicle is predicted to operate in a driving mode; performing a first determination that the vehicle is operable in the driving mode in response to determining that the set of values of the driving mode is within maximum and minimum values associated with another set of values of a trajectory along which the vehicle is expected to be operated; and causing, in response to the first determination, the vehicle to operate in the driving mode by sending one or more driving related instructions to a device in the vehicle to cause the device to perform cruise control related operations.

Abstract: Techniques are described for performing vehicle operation that comprises determining, by a computer located in a vehicle and for each of a first driving mode and a second driving mode, a set of values of a predicted trajectory along which the vehicle is predicted to operate in a driving mode; performing a first determination that the vehicle is not operable in the first driving mode; performing a second determination that the vehicle is operable in the second driving mode; and causing, in response to the second determination, the vehicle to operate in the second driving mode by sending one or more driving related commands to one or more apparatus in the vehicle.

Abstract: Vehicle dynamics related parameter(s) of an autonomous vehicle model can be calibrated so that the autonomous vehicle model can more accurately determine the driving related behaviors of the autonomous vehicle. An example method comprises obtaining, from sensor data, vehicle related parameters; performing a first determination of a slope of a road and a banking angle of the road based on a pitch angle of the vehicle and a roll angle of the vehicle, respectively; performing a second determination of a set of parameters that describe a driving-related operation of the vehicle; performing a third determination that at least one difference between at least one value from the set of parameters and a corresponding parameter from the plurality of vehicle related parameters exceeds at least one threshold value; and obtaining, in response to the third determination, a calibrated longitudinal dynamic-related parameter or a calibrated lateral dynamic-related parameter.

Abstract: Devices, systems, and methods for predictive anti-lag braking control for autonomous vehicles are described. An example method of controlling a vehicle includes receiving, from a controller, a first plurality of values indicative of a brake wheel torque demand associated with a deceleration profile of the vehicle, deriving, based on the first plurality of values and a steady-state brake model, a second plurality of values indicative of a brake pressure demand, wherein an input to the steady-state brake model comprises the brake pressure demand and at least one previous brake wheel torque demand, and wherein an output of the steady-state brake system comprises a current brake wheel torque demand, generating, based on the second plurality of values and at least the steady-state model, a plurality of brake control commands, and controlling, using the plurality of brake control commands, the air brake system.

Abstract: Devices, systems, and methods for estimating wheel torque in an autonomous vehicle include a method for controlling a vehicle includes using a dual filter framework to generate an updated estimate of a wheel torque, and controlling, based on the updated estimate of the wheel torque, the vehicle. A first filter of the dual filter framework is configured to generate a blended estimate of the wheel torque by combining a first estimate of the wheel torque that is generated based on a set of mechanical torque transfer models of at least a powertrain and a brake of the vehicle and a second estimate of wheel torque that is generated based on an inverted longitudinal dynamic model, and a second non-linear filter of the dual filter framework is configured to generate the updated estimate of a wheel torque and vehicle velocity based on a torque delivery damping process model on vehicle motion.

Abstract: Devices, systems, and methods for integrated predictive dynamic control of a vehicle powertrain in an autonomous vehicle are described. An example method for controlling a vehicle includes generating, based on performing an optimization on a blended smooth wheel domain fuel consumption map subject to a modified torque availability constraint, one or more wheel domain control commands, converting the one or more wheel domain control commands to one or more powertrain-executable engine domain control commands, and transmitting the one or more powertrain-executable engine domain control commands to a powertrain of the vehicle, the powertrain configured to operate a plurality of gears, wherein the one or more powertrain-executable engine domain control commands enable the vehicle to track a reference kinematic trajectory associated with a vehicle speed driving plan within a predetermined tolerance.