A power assist device may include a motor, a motor driving circuit, sensors to output signals in accordance with a rotational speed of the wheel, a memory, and a signal processor. The memory stores information of a parameter defining a transfer function that interrelates a total torque to be input to the vehicle and a rotational speed of the wheel, and an inverse transfer function thereof. Based on the inverse transfer function, the signal processor determines an estimated value of total torque from a detected value of rotational speed of the wheel. Moreover, based on the transfer function, the signal processor updates at least a portion of the information of the parameter so that, for example, an error between a detected value of rotational speed of the wheel and an estimated value of rotational speed of the wheel as determined from the estimated value of total torque is reduced.

A driveline system includes a drive axle coupled to a load, an electric machine, and a control system. The electric machine is responsive to a commanded torque, has a rotor shaft coupled to the axle, and produces an output torque that rotates the axle and load to produce driveline oscillation at a high resonant frequency. The control system generates the commanded torque using a nested control loop architecture in which an outer control loop operates at a sampling rate that is below a critical rate necessary for controlling the resonant frequency, and an inner control loop operates at a sampling rate that is above the critical rate. The inner loop determines a modified torque command and acceleration value in response to a commanded torque from the outer loop. The electric machine is thereafter controlled via the commanded torque.

This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, a method is provided to train a machine learning model using image data and position data to identify contact points and ground surface normal vectors. Image data is received that depicts an object, and position data for the object is also received, such as point cloud position information for various points along the object's exterior surface. Two sets of labels may then be determined based on the position data, with one set identifying where the object contacts a ground surface and another identifying at least one normal vector for the ground surface. The machine learning model may then be trained based on both sets of labels to determine three-dimensional bounding boxes, normal maps, or combinations thereof. Other aspects and features are also claimed and described.

Methods and systems for operating a hybrid driveline that includes an engine and an electric machine are presented. In one non-limiting example, the engine and electric machine are operated according to a solution of a Hamiltonian that includes a first co-state and a second co-state, an engine fuel flow parameter, a rate of change of battery state of charge parameter, and an emissions flow rate parameter.

A contextual communication system includes an external device including a communication receiver and an electronic control unit (ECU). The ECU includes data processing hardware and memory hardware that stores actions and action settings. The data processing hardware includes a contextual communication feature that has a telematics system configured to detect the external device and a contextual event. The contextual communication feature is configured to selectively issue a contextual alert based on the detected external device and in response to the contextual event.

Disclosed herein are an apparatus and method for adaptive autonomous driving control. The apparatus includes memory in which at least one program is recorded and a processor for executing the program. The program may perform control of a target vehicle by converting a theoretical control value based on a vehicle control algorithm into a hardware-dependent control value, which is dependent on the platform or hardware of the target vehicle, and may modify at least one parameter or a conversion equation for conversion of the hardware-dependent control value such that an error is minimized based on the difference between a response value according to the control of the target vehicle and a control value.

In an embodiment a control apparatus includes a processor configured to calculate a target curvature depending on a target path of a vehicle, calculate a first lateral control value based on a feedforward control by using the target curvature, calculate a second lateral control value based on a feedback control by using vehicle information collected from a sensing device of the vehicle, estimate a disturbance by using the vehicle information collected from the sensing device of the vehicle, and calculate a final lateral control command value by summing the first lateral control value, the second lateral control value, and the disturbance and a storage configured to store data and algorithms driven by the processor.

Aspects and implementations of the present disclosure address shortcomings of existing technology by enabling autonomous vehicle simulations based on retro-reflection optical data. The subject matter of this specification can be implemented in, among other things, a method that involves initiating a simulation of an environment of an autonomous driving vehicle, the simulation including a plurality of simulated objects, each having an identification of a material type of the respective object. The method can further involve accessing simulated reflection data based on the plurality of simulated objects and retro-reflectivity data for the material types of the simulated objects, and determining, using an autonomous vehicle control system for the autonomous vehicle, a driving path relative to the simulated objects, the driving path based on the simulated reflection data.

A tree search may comprise a cost function that may be used to determine, from multiple controller profiles, a controller profile to implement a transition between vehicle actions. A controller profile may limit the controls output by a controller sufficient to track from a first vehicle action to a second vehicle action. The controller controls may result in a trajectory achieved by the vehicle and the controller profile may limit the controls of the controller such that the trajectory is limited by a maximum jerk and/or maximum acceleration.