A weight profile determination system may be provided that includes a sensor and a controller. The sensor may be disposed along a route and configured to generate a plurality of force measurements of a vehicle system moving on the route relative to the sensor. The force measurements may be obtained at different times and correspond to different locations along a length of the vehicle system. The controller may determine a weight profile for the vehicle system based on the force measurements generated by the sensor. The weight profile can represent a distribution of weight along the length of the vehicle system. The controller may communicate the weight profile to one or more of the vehicle system or an offboard device for controlling movement of the vehicle system based on the weight profile.

A method for providing assistance in driving includes capturing an image of a second moving vehicle when a first moving vehicle is moving and obtaining basic information of the second moving vehicle according to the image thereof, the basic information of the second moving vehicle comprising weight information of the second moving vehicle. Driving information of the first moving vehicle is obtained, and a safe distance between the first moving vehicle and the second moving vehicle is determined according to the driving information of the first moving vehicle and the basic information of the second moving vehicle. The current distance between the first moving vehicle and the second moving vehicle is detected, and a warning is output if the distance between the first moving vehicle and the second moving vehicle is less than the safe distance.

A method for providing assistance in driving includes capturing an image of a second moving vehicle when a first moving vehicle is moving and obtaining basic information of the second moving vehicle according to the image thereof, the basic information of the second moving vehicle comprising weight information of the second moving vehicle. Driving information of the first moving vehicle is obtained, and a safe distance between the first moving vehicle and the second moving vehicle is determined according to the driving information of the first moving vehicle and the basic information of the second moving vehicle. The current distance between the first moving vehicle and the second moving vehicle is detected, and a warning is output if the distance between the first moving vehicle and the second moving vehicle is less than the safe distance.

The present disclosure relates to a vehicle and a parking control method therefor which can prevent a parking curb or a driving system from being damaged during parking due to collision with the parking curb or running over the parking curb according to creep torque imitation by an electric motor in a vehicle equipped with the electric motor. A parking control method includes determining whether a parking situation occurs, applying a creep torque modification coefficient to a creep torque until contact with an object that applies a reaction force to a wheel in a parking direction is sensed to determine a modified creep torque upon determining the parking situation, and variably controlling the creep torque by applying a variable coefficient to the modified creep torque.

The present disclosure relates to a vehicle and a parking control method therefor which can prevent a parking curb or a driving system from being damaged during parking due to collision with the parking curb or running over the parking curb according to creep torque imitation by an electric motor in a vehicle equipped with the electric motor. A parking control method includes determining whether a parking situation occurs, applying a creep torque modification coefficient to a creep torque until contact with an object that applies a reaction force to a wheel in a parking direction is sensed to determine a modified creep torque upon determining the parking situation, and variably controlling the creep torque by applying a variable coefficient to the modified creep torque.

A system and method for classifying a type of interaction between a human user and a mobile communication device within a defined volume, based on multiple sensors. The method may include: determining a position of the mobile communication device relative to a frame of reference of the defined volume, based on: angle of arrival, time of flight, or received intensity of radio frequency (RF) signals transmitted by the mobile communication device and received by a phone location unit located within the defined volume configured to wirelessly communicate with the mobile communication device; obtaining at least one sensor measurement related to the mobile communication device from various non-RF sensors; repeating the obtaining, to yield a time series of sensor readings; and using a computer processor to classify the type of interaction into one of many predefined types of interactions, based on the position and the time series of sensor readings.

A system and method for classifying a type of interaction between a human user and a mobile communication device within a defined volume, based on multiple sensors. The method may include: determining a position of the mobile communication device relative to a frame of reference of the defined volume, based on: angle of arrival, time of flight, or received intensity of radio frequency (RF) signals transmitted by the mobile communication device and received by a phone location unit located within the defined volume configured to wirelessly communicate with the mobile communication device; obtaining at least one sensor measurement related to the mobile communication device from various non-RF sensors; repeating the obtaining, to yield a time series of sensor readings; and using a computer processor to classify the type of interaction into one of many predefined types of interactions, based on the position and the time series of sensor readings.

Described herein are methods and systems for the weight estimation of commercial vehicles and using these estimates to control different vehicle systems such as powertrain, brakes, and suspension. A vehicle comprises a weight estimator, which receives input from different vehicle systems and/or sensors (e.g., suspension, powertrain, speedometer, tire pressure monitoring system) and uses these inputs to determine weight values associated with the vehicle (e.g., total weight, weight distribution per axle, weight distribution per wheel, load distribution). These weight values are then used by the power train, brakes, and/or suspension as additional input for operating these systems besides drivers' input, e.g., provided as pedal positions. For example, different weight values can cause different powertrain outputs for the same accelerator pedal position, e.g., greater output for a heavier vehicle. As such, a driver experiences more uniform vehicle responses for different vehicle loads.

Described herein are methods and systems for the weight estimation of commercial vehicles and using these estimates to control different vehicle systems such as powertrain, brakes, and suspension. A vehicle comprises a weight estimator, which receives input from different vehicle systems and/or sensors (e.g., suspension, powertrain, speedometer, tire pressure monitoring system) and uses these inputs to determine weight values associated with the vehicle (e.g., total weight, weight distribution per axle, weight distribution per wheel, load distribution). These weight values are then used by the power train, brakes, and/or suspension as additional input for operating these systems besides drivers' input, e.g., provided as pedal positions. For example, different weight values can cause different powertrain outputs for the same accelerator pedal position, e.g., greater output for a heavier vehicle. As such, a driver experiences more uniform vehicle responses for different vehicle loads.

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.