A method for a vehicle maintenance/repair detection system is described. The method includes logging, in a vehicle log, advanced driver assistance system (ADAS) actuations during a road trip. The method also includes analyzing the vehicle log to identify the ADAS actuations during the road trip to correct a trajectory of an ego vehicle. The method further includes analyzing a correction of the trajectory of the ego vehicle to determine a direction of the ADAS actuations to correct the trajectory of the ego vehicle. The method also includes determining a maintenance/repair for the ego vehicle according to the direction and a frequency of the ADAS actuations to correct the trajectory of the ego vehicle.

Systems and methods for determining a maximum phase recovery envelope are disclosed herein. In one example, a system includes a processor and a memory having a vehicle control module. The vehicle control module includes instructions that, when executed by the processor, cause the processor to determine a critical point on a phase plane indicating a maximum defined recovery point a vehicle can recover from, perform forward and reverse simulations from the critical point to define outermost contours of a maximum phase recovery envelope using parameters and state of the vehicle, and cause the vehicle to operate within the maximum phase recovery envelope.

A method for controlling a torque of at least one wheel of a mobile platform. The method includes: providing at least one current slip value of the wheel and at least one current wheel acceleration of the wheel as input values; providing a trained radial basis function network designed to determine, by means of the input values, at least one torque change as an output value for control of the at least one wheel; and determining a current torque change, by means of the trained radial basis function network and the provided input values, for control of the torque.

The disclosed vehicle control device (10) is for controlling outputs of a left driving source and a right driving source, and includes a calculator (21), a storing unit (22), and a controller (23). The calculator (21) calculates an equivalent sum value corresponding to sum of a left target speed and a right target speed, and calculates an equivalent difference value corresponding to a difference between the left target speed and the right target speed. The storing unit (22) stores a sum model and a difference model. The sum model modes motion states of the left driving system, the right driving system, the left driving source, and the right driving source while the vehicle is running straight and is applied with the equivalent sum value to derive a sum instruction torque. The difference model models motion states of the left driving system, the right driving system, the left driving source, and the right driving source while the vehicle is cornering and is applied with the equivalent difference value to derive a difference instruction torque. The controller (23) controls torques of the left driving source and the right driving source, using the sum instruction torque and the difference instruction torque.

Smooth and agile acceleration of an autonomous driving vehicle (ADV) that is being held at a standstill or is otherwise at a stop is important for ADV safety, efficiency, and occupant enjoyment. For example, as a practical matter, an ADV at an uncontrolled intersection should quickly and smoothly drive-off when it is its turn to advanceotherwise, it may get stuck at the intersection. Embodiments herein provide systems and methods to facilitate a pleasant and comfortable ride while having the ADV drive off smoothly (e.g., not a large jerk) and safely (e.g., not slipping) during driving from a stopregardless of whether the ADV is on a slope or on a neutral (i.e., not sloped) surface. In one or more embodiments, the drive-off process may be divided into several stages from a standstill to an acceleration settle stage.

A method for controlling actuators acting on vehicle wheels of a motor vehicle comprises ascertaining a force to be brought about on a reference point of the motor vehicle on the basis of driver specifications, ascertaining wheel forces to be brought about on the vehicle wheels to implement the force to be brought about on the reference point of the motor vehicle by means of a first dynamic allocation by model-based predictive control (MPC), ascertaining setpoint values for wheel parameters from the ascertained wheel forces, and actuating the actuators of the motor vehicle so as to implement the setpoint values of the wheel parameters.

A vehicle includes a method for operation. The vehicle includes a wheel, an actuator for controlling rotation of the wheel, a sensor for detecting a stuck condition for the wheel, and a processor for performing an automated rocking mode at the vehicle. The automated rocking mode includes engaging an engine of the vehicle into a first gear, rotating the wheel via the actuator with the engine in the first gear to create a first motion of the vehicle in a first direction, obtaining a traction indicator of the vehicle with the engine in the first gear, engaging the engine into a second gear when the traction indicator meets a traction threshold, and rotating the wheel via the actuator with the engine in the second gear to create a second motion of the vehicle in a second direction.

A motor vehicle includes a control device that controls such that a first drive machine propels, while a second drive machine operates as a generator, and a load point of the first drive machine is increased by an amount that is bounded by a first limit value chosen such that, when a driving torque of the first drive machine is reduced to zero, while the amount is set at the first limit value and at a same time a motor slip control of the motor vehicle is not active, a destabilization of the motor vehicle occurs in a first test driving situation and no destabilization of the motor vehicle occurs in a second test driving situation, wherein a coefficient of friction of a static friction between tires of the motor vehicle and a roadway is between 0.4 and 0.9, or and the coefficient of friction is between 0.9 and 1.1.

In one aspect, an operating method of an intelligence vehicle driving control system is provided that comprises: a collecting step of collecting big data including a wheel torque and a speed for every vehicle type and traffic information; a torque calculating step of learning the big data using a predetermined machine learning model and inputs a specific desired speed profile to the machine learning model to calculate a motor torque of a driving vehicle; and an optimal speed profile deriving step of calculating an energy consumption required to generate the calculated motor torque using a predetermined dynamic programming method and a reverse vehicle dynamic model and deriving an optimal speed profile in which the energy consumption is minimized.

A vehicle drift control apparatus includes a controller configured to receive data of a plurality of drift state stages respectively corresponding to a plurality of different drift angle ranges, and to change a rear-wheel torque limiting stage of a vehicle as a drift state stage corresponding to a real-time drift angle of the vehicle is changed in a drift state of the vehicle.