A drive and control system for a lawn tractor includes a CAN-Bus network, a plurality of controllers, a pair of electric transaxles controlled by the plurality of controllers, and one or more steering and drive input devices coupled to respective sensor(s) for sensing user steering and drive inputs. The plurality of controllers communicate with one or more vehicle sensors via the CAN-Bus network. The plurality of controllers receive the user's steering and drive inputs and posts on the CAN-Bus network and generate drive signals to obtain the desired speed and direction of motion of the lawn tractor.

A vehicle includes a transmission, an engine, a clutch, and a controller. The transmission has an input. The engine is configured to generate and deliver torque to the input. The clutch is configured to connect and disconnect the engine from the input, and to crank the engine during an engine start. The controller is programmed to, in response to a command to adjust a torque of the clutch during an engine start and a presence of first condition of the clutch, drive a clutch actuator pressure to a first desired value based on a first transfer function. The controller is further programmed to, in response to a command to adjust the torque of the clutch during the engine start and a presence of a second condition of the clutch, drive the clutch actuator pressure to a second desired value based on a second transfer function.

A user gesture is detected based on received data from one or more motion sensors. User gesture attributes are identified including at least one of hand vectoring, wrist articulation, and finger articulation from the gesture including respective movements of each of a plurality of a user's fingers. Based on the gesture attributes, a user and an action to be performed in a vehicle are identified. The action is performed in the vehicle to control at least one vehicle component based on the gesture.

A system and method for a real-time prognosis of a vehicle comprising a personal communication device comprising an arbitrarily oriented three-axis accelerometer configured to capture a pitch motion and/or roll motion of the vehicle and an onboard diagnostics system communicably connected with the personal communication device enabling bi-directional communication. The personal communication device comprising a processor configured for geometric mapping of a three dimensional Cartesian coordinate of the three-axis accelerometer with the vehicle. The processor virtually orients the coordinates of three-axis accelerometer to coincide with the coordinates of the vehicle. The arbitrarily oriented three-axis accelerometer is configured to capture a road condition and a driver behavior using a sampling rate between 4 Hertz (Hz) to 10 Hertz (Hz). The system for the real-time prognosis of the vehicle, wherein the real-time prognosis utilizes at least one predictive analysis model to determine real-time prognosis for the said vehicle.

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.

Embodiments of the present application disclose a positioning method and apparatus, an autonomous driving vehicle, an electronic device and a storage medium, relating to the field of autonomous driving technologies, comprising: collecting first pose information measured by an inertial measurement unit within a preset time period, and collecting second pose information measured by a wheel tachometer within the time period; generating positioning information according to the first pose information, the second pose information and the adjacent frame images; controlling driving of the autonomous driving vehicle according to the positioning information. The positioning information is estimated by combining the first pose information and the second pose information corresponding to the inertial measurement unit and the wheel tachometer respectively. Compared with the camera, the inertial measurement unit and the wheel tachometer are not prone to be interfered by the external environment.

A posture estimation device estimates a posture of a movable body based on acceleration information based on a posture change of the movable body and angular velocity information based on the posture change of the movable body. The posture estimation device includes a storage unit that stores the acceleration information, the angular velocity information, and a plurality of posture parameters related to a movement of the movable body, a parameter control unit that selects a selection posture parameter from the plurality of posture parameters, and a posture calculation unit that estimates the posture of the movable body by using the acceleration information, the angular velocity information, and the selection posture parameter.

A vehicle state estimation device for a vehicle provided with an inertial measurement sensor and a wheel speed sensor includes: a vehicle state estimation unit that estimates a vehicle state including a vehicle velocity based on an acceleration and an angular velocity acquired by the inertial measurement sensor and a wheel speed acquired by the wheel speed sensor; and a determination unit that determines whether a wheel is slipping. The estimation unit estimates a steady-state vehicle velocity based on the wheel speed and calculates a transient vehicle velocity by time integration based on the acceleration and the angular velocity. When the wheel is slipping, the estimation unit decides an estimated value of the vehicle velocity to be close to the transient vehicle velocity, and when the wheel is not slipping, the estimation unit decides the estimated value of the vehicle velocity to be close to the steady-state vehicle velocity.

In an abnormality determination device for determining presence or absence of an abnormality of a 6-axis inertial measurement sensor installed in a vehicle to detect a forward-backward acceleration, a lateral acceleration, a vertical acceleration, a roll rate, a pitch rate, and a yaw rate of the vehicle, the abnormality determination device includes: a 3-axis inertial measurement sensor that detects the forward-backward acceleration, the lateral acceleration, and the yaw rate; and an abnormality determination unit that determines presence or absence of an abnormality of the 6-axis inertial measurement sensor, wherein the abnormality determination unit determines the presence or absence of an abnormality of the 6-axis inertial measurement sensor by comparing the forward-backward acceleration, the lateral acceleration, and the yaw rate acquired by the 6-axis inertial measurement sensor with the forward-backward acceleration, the lateral acceleration, and the yaw rate acquired by the 3-axis inertial measurement sensor, respectively.

A method for augmenting transport services using event detection is provided. The method includes collection of first sensor data generated by various sensors associated with a plurality of vehicles. The first sensor data includes sensor outputs that indicate a plurality of rash driving events. The sensor outputs are augmented based on angular rotation to obtain augmented sensor outputs. A prediction model is trained based on the augmented sensor outputs. Target sensor data associated with a target vehicle is provided as input to the trained prediction model, and based on an output of the trained prediction model an occurrence of a rash driving event is detected in real-time or near real-time. Based on a count of rash driving events associated with the target driver within a cumulative driving distance, a driver score of the target driver is determined.