Vehicle center of gravity (CoG) height and mass estimation techniques utilize a light detection and ranging (LIDAR) sensor configured to emit light pulses and capture reflected light pulses that collectively form LIDAR point cloud data and a controller configured to estimate the CoG height and the mass of the vehicle during a steady-state operating condition of the vehicle by processing the LIDAR point cloud data to identify a ground plane, identifying a height difference between (i) a nominal distance from the LIDAR sensor to the ground plane and (ii) an estimated distance from the LIDAR sensor to the ground plane using the processed LIDAR point cloud data, estimating the vehicle CoG height as a difference between (i) a nominal vehicle CoG height and the height difference, and estimating the vehicle mass based on one of (i) vehicle CoG metrics and (ii) dampening metrics of a suspension of the vehicle.

An embodiment device for controlling autonomous driving includes a roll angle estimated value calculation device configured to calculate a roll angle estimated value of a vehicle based on a height of a center of gravity of the vehicle, a sprung mass, a spring constant of a suspension, a target speed, and a target turning radius, and a controller configured to compare a roll angle of the vehicle with a preset reference roll angle to adjust the target speed or the target turning radius of the vehicle.

In a vehicle control device, a switching hyperplane generation unit generates a switching hyperplane based on a travel state of a vehicle and cornering stiffness dependent on a travel surface state as a state of a road surface on which the vehicle travels. A deviation computation unit calculates a deviation between a target trajectory and an actual trajectory of the vehicle. A state estimation unit estimates a state to be controlled of the vehicle based on the deviation calculated by the deviation computation unit. A target steering angle and acceleration/deceleration computation unit calculates a target steering angle and a target acceleration/deceleration rate of the vehicle based on the switching hyperplane generated by the switching hyperplane generation unit and an estimated state as the state estimated by the state estimation unit.

A parallel computing method for man-machine coordinated steering control of a smart vehicle based on risk assessment is provided, comprising the following steps: building a lateral kinetic equation model of a vehicle; building a target function by targeting at minimizing an offset distance of a vehicle driving track from a lane center line and making a change in a front wheel steering angle and a longitudinal acceleration as small as possible in a driving process; building a parallel computing architecture of a prediction model and the target function, and employing a triggering parallel computing method; solving and computing a gradient with a manner of back propagation and using a gradient descent method to obtain an optimal control amount of the front wheel steering angle and an optimal control amount of the longitudinal acceleration; and computing a driving weight, obtaining a desired front wheel steering angle and completing real time control.

A method, apparatus, system, vehicle, and/or computer program provides that one or more components of a vehicle are operated as a function of a predetermined pick-up time of the vehicle at a pick-up position of a parking facility in such a way that the vehicle exhibits one or more predetermined states at the pick-up position at the predetermined pick-up time.

To achieve an information processing apparatus and a mobile apparatus that individually calculate an inclination of the mobile apparatus itself and an inclination of a traveling surface. A measurement value of an air pressure sensor that measures an air pressure of a tire of the mobile apparatus is received, and the inclination of the mobile apparatus is calculated on the basis of the tire air pressure. Furthermore, a measurement value of an absolute pressure sensor attached to the mobile apparatus is received, and an angle of the traveling surface on which the mobile apparatus travels and a position of the mobile apparatus are calculated on the basis of a horizontal movement amount of the mobile apparatus and a vertical movement amount that is calculated on the basis of the measurement value of the absolute pressure sensor. Furthermore, a plurality of different state values such as inclination information of the traveling surface that changes with time transition is input to a Kalman filter, and state values that have already been acquired are updated on the basis of the newly input state values to generate and output the latest state values.

A method of calculation a vehicle load comprising a first vehicle load value based at least on air pressures in air springs and height data of suspension of a vehicle axle, determining a second vehicle load value based on a change of track width of the vehicle axle, and calculating the vehicle load based on the first vehicle load value and the second vehicle load value.

Provided are methods, systems, devices, and tangible non-transitory computer readable media for mapping geographical surfaces. The disclosed technology can access image data and sensor data. The image data can include a plurality of images of one or more locations and semantic information associated with the one or more locations. The sensor data can include sensor information associated with detection of one or more surfaces at the one or more locations by one or more sensors. One or more irregular surfaces can be detected based at least in part on the image data and the sensor data. The one or more irregular surfaces can include the one or more surfaces associated with the image data and the sensor data that satisfies one or more irregular surface criteria at each of the one or more locations respectively. Map data including information associated with the one or more irregular surfaces can be generated.

Aspects and implementations of the present disclosure relate to performance and safety improvements for autonomous trucking systems, including techniques of obtaining an identification of an unfavorable condition on a route of an autonomous vehicle (AV), causing the AV to exit the route, and performing one or more waiting loops until the unfavorable condition is resolved, the AV is rerouted, assistance arrives, and the like.

Vehicle center of gravity (CoG) height and mass estimation techniques utilize a light detection and ranging (LIDAR) sensor configured to emit light pulses and capture reflected light pulses that collectively form LIDAR point cloud data and a controller configured to estimate the CoG height and the mass of the vehicle during a steady-state operating condition of the vehicle by processing the LIDAR point cloud data to identify a ground plane, identifying a height difference between (i) a nominal distance from the LIDAR sensor to the ground plane and (ii) an estimated distance from the LIDAR sensor to the ground plane using the processed LIDAR point cloud data, estimating the vehicle CoG height as a difference between (i) a nominal vehicle CoG height and the height difference, and estimating the vehicle mass based on one of (i) vehicle CoG metrics and (ii) dampening metrics of a suspension of the vehicle.