Provided are methods, systems, and computer program products for vehicle dynamics classification for collision and loss of control detection. Some methods described also include obtaining sensor data associated with dynamics of a vehicle, wherein the dynamics characterize motion of the vehicle and the vehicle is associated with a dynamics event classification. The methods include obtaining predicted dynamics, wherein the predicted dynamics are based on control signals and feedback on control signals from a previous time instance. Additionally, the methods include determining the dynamics event classification of the vehicle based on the dynamics and the predicted dynamics and controlling operation of the vehicle according to the dynamics event classification.

A system for associating a physical identity and a virtual identity of a target vehicle includes a data processor, including a wireless communication module, positioned within an ego vehicle, and a plurality of perception sensors, positioned within the ego vehicle and adapted to collect data related to a physical identity of the target vehicle and to communicate the data related to the physical identity of the target vehicle to the data processor via a communication bus, the data processor adapted to receive, via a wireless communication channel, data related to a virtual identity of the target vehicle and to associate the physical identity of the target vehicle with the virtual identity of the target vehicle.

A system for associating a physical identity and a virtual identity of a target vehicle includes a data processor, including a wireless communication module, positioned within an ego vehicle, and a plurality of perception sensors, positioned within the ego vehicle and adapted to collect data related to a physical identity of the target vehicle and to communicate the data related to the physical identity of the target vehicle to the data processor via a communication bus, the data processor adapted to receive, via a wireless communication channel, data related to a virtual identity of the target vehicle and to associate the physical identity of the target vehicle with the virtual identity of the target vehicle.

A trailer angle identification system includes an imaging device configured to capture an image. A controller is configured to receive steering angle data corresponding to a steering angle of a vehicle, receive vehicle speed data corresponding to a vehicle speed, and estimate an angle of the trailer relative to the vehicle by processing the image, the steering angle data, and the vehicle speed data in at least one neural network.

A trailer angle identification system includes an imaging device configured to capture an image. A controller is configured to receive steering angle data corresponding to a steering angle of a vehicle, receive vehicle speed data corresponding to a vehicle speed, and estimate an angle of the trailer relative to the vehicle by processing the image, the steering angle data, and the vehicle speed data in at least one neural network.

A method for operating a drive-train of a vehicle, such as a municipal or agricultural utility vehicle, having at least one drive machine, a vehicle transmission with at least two gears, at least one drive axle and at least one auxiliary power take-off. The method includes controlling or regulating a supply of normal power from the drive machine as a function of a normal torque characteristic. Depending on the operating situation, supplying additional power to the at least one drive axle and/or to the at least one auxiliary power take-off. The supplied additional power is controlled or regulated as a function of operating situation dependent torque characteristics which are called up as a function of the selected gear at the time.

A method for operating a drive-train of a vehicle, such as a municipal or agricultural utility vehicle, having at least one drive machine, a vehicle transmission with at least two gears, at least one drive axle and at least one auxiliary power take-off. The method includes controlling or regulating a supply of normal power from the drive machine as a function of a normal torque characteristic. Depending on the operating situation, supplying additional power to the at least one drive axle and/or to the at least one auxiliary power take-off. The supplied additional power is controlled or regulated as a function of operating situation dependent torque characteristics which are called up as a function of the selected gear at the time.

A personalized adaptive cruise control (P-ACC) system and associated algorithm are disclosed for determining a driver's preferred following gap in relation to vehicle speed based on periods of steady-state operation of a vehicle. While the P-ACC system is activated, vehicle transition states initiated by driver manual interventions such as takeover or overwrite events are used to identify subsequent periods of vehicle steady-state operation. Vehicle dynamics data captured during periods of steady-state operation is stored as steady-state data, which is then used to train a machine learning model to learn the driver's preferred following gap. This learned relationship is fed into second-order vehicle dynamics to determine a target acceleration for achieving the desired following gap while the P-ACC system is activated. Upon achieving the desired following gap, the vehicle speed may be held constant to maintain the following gap unless a change in lead vehicle speed necessitates updating the following gap.

A personalized adaptive cruise control (P-ACC) system and associated algorithm are disclosed for determining a driver's preferred following gap in relation to vehicle speed based on periods of steady-state operation of a vehicle. While the P-ACC system is activated, vehicle transition states initiated by driver manual interventions such as takeover or overwrite events are used to identify subsequent periods of vehicle steady-state operation. Vehicle dynamics data captured during periods of steady-state operation is stored as steady-state data, which is then used to train a machine learning model to learn the driver's preferred following gap. This learned relationship is fed into second-order vehicle dynamics to determine a target acceleration for achieving the desired following gap while the P-ACC system is activated. Upon achieving the desired following gap, the vehicle speed may be held constant to maintain the following gap unless a change in lead vehicle speed necessitates updating the following gap.

An example operation includes one or more of sensing from at least one sensor, a longitudinal acceleration and a lateral acceleration, receiving from the at least one sensor, a longitudinal acceleration signal based on the longitudinal acceleration and a lateral acceleration signal based on the lateral acceleration, filtering via at least one logic, the longitudinal acceleration signal and the lateral acceleration signal based on an interquartile range of the longitudinal acceleration signal and the lateral acceleration signal, yielding a plurality of filtered signals and determining via the at least one logic, a mobility index of a transport based on the filtered signals.