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.

The disclosed technology relates to systems and methods for weighing transportation vehicles and cargo and determining their load state. The disclosed technology can include a sensor controller configured to received one or more sensor readings from weight sensors on a cargo vehicle. The sensor controller can determine an inclination and/or deflection of the cargo vehicle and modify the sensor readings based on the inclination and/or deflection. The sensor controller can calculate a weight of cargo carried by the cargo vehicle based on the modified sensor readings.

The disclosed technology relates to systems and methods for weighing transportation vehicles and cargo and determining their load state. The disclosed technology can include a sensor controller configured to received one or more sensor readings from weight sensors on a cargo vehicle. The sensor controller can determine an inclination and/or deflection of the cargo vehicle and modify the sensor readings based on the inclination and/or deflection. The sensor controller can calculate a weight of cargo carried by the cargo vehicle based on the modified sensor readings.

Methods, apparatus, systems and articles of manufacture to calibrate a weight estimation are disclosed herein. An example apparatus comprises memory including stored instructions, and a processor to execute the instructions to determine, via a first sensor, a first load estimation of a vehicle corresponding to when the vehicle under a load condition, determine, via a second sensor, a second load estimation corresponding to when the vehicle is moving and under the load condition, and calibrate, based on a difference between the second load estimation and the first load estimation, an error of the first load estimation.

Methods, apparatus, systems and articles of manufacture to calibrate a weight estimation are disclosed herein. An example apparatus comprises memory including stored instructions, and a processor to execute the instructions to determine, via a first sensor, a first load estimation of a vehicle corresponding to when the vehicle under a load condition, determine, via a second sensor, a second load estimation corresponding to when the vehicle is moving and under the load condition, and calibrate, based on a difference between the second load estimation and the first load estimation, an error of the first load estimation.

A first processor determines a loaded state of a bucket, makes a determination that a traveling apparatus has performed an operation for shifting from a rearward movement state to a state (forward movement or stop) other than the rearward movement state in the loaded state, and calculates a load weight of the bucket from a detection value of a work implement sensor (first hydraulic pressure detectors and a first angle detector) based on the determination.

A first processor determines a loaded state of a bucket, makes a determination that a traveling apparatus has performed an operation for shifting from a rearward movement state to a state (forward movement or stop) other than the rearward movement state in the loaded state, and calculates a load weight of the bucket from a detection value of a work implement sensor (first hydraulic pressure detectors and a first angle detector) based on the determination.

A system for monitoring payload of a vehicle includes at least one wireless transmitter comprising a piezo-resistive pressure sensor, a transceiver and a battery unit, encapsulated in an insulating casing, the at least one wireless transmitter being configured to be attached to an axle beam of the vehicle and to emit a wireless signal in response to a mechanical strain of the axle beam, a wireless receiver configured to receive the wireless signal from the at least one wireless transmitter.

A system for monitoring payload of a vehicle includes at least one wireless transmitter comprising a piezo-resistive pressure sensor, a transceiver and a battery unit, encapsulated in an insulating casing, the at least one wireless transmitter being configured to be attached to an axle beam of the vehicle and to emit a wireless signal in response to a mechanical strain of the axle beam, a wireless receiver configured to receive the wireless signal from the at least one wireless transmitter.

A system for handling a lost item in an autonomous vehicle and a server. The vehicle includes a vision sensor configured to respectively capture a first vehicle interior image and a second vehicle interior image when a passenger boards and alights from the vehicle. The vehicle is configured to transmit the captured first vehicle interior image and second vehicle interior image to the server. The server is configured to compare the first vehicle interior image with the second vehicle interior image and transmit lost item information to the passenger when the lost item in the vehicle is sensed.