A control system for a vehicle includes an internal vehicle reference model that determines reference states for the vehicle that represent an expected vehicle response, sensors that determine measured states for the vehicle, and a vehicle motion control system that determines desired states for the vehicle. A stability determining module identifies a reference deviation between the reference states and the measured states, identifies a desired deviation between the desired states and measured states, and outputs a command for reducing the reference deviation and the desired deviation. Actuators are operable to reduce the reference deviation and the desired deviation in response to the command.

A method for determining the lean angle of a two-wheeler in which the axle load on at least one wheel is ascertained and the lean angle is calculated as a function of the axle load.

A motion planner of an autonomous vehicle's computer system uses reconfigurable collision detection architecture hardware to perform a collision assessment on a planning graph for the vehicle prior to execution of a motion plan. For edges on the planning graph, which represent transitions in states of the vehicle, the system sets a probability of collision with a dynamic object in the environment based at least in part on the collision assessment. Depending on whether the goal of the vehicle is to avoid or collide with a particular dynamic object in the environment, the system then performs an optimization to identify a path in the resulting planning graph with either a relatively high or relatively low potential of a collision with the particular dynamic object. The system then causes the actuator system of the vehicle to implement a motion plan with the applicable identified path based at least in part on the optimization.

Systems and methods are provided to enhance the driving performance of a leanable vehicle such as a motorcycle. The system includes a leanable vehicle interface to receive input from a driver (e.g., a human or a robotic driver) and a sensor interface to receive inputs from sensors on the leanable vehicle. The system also includes a computing module to use the sensor data in combination with data from the leanable vehicle interface to calculate the driver behavior to produce a future desired performance, based on a specified aggressiveness, so that the performance of the leanable vehicle is optimized. The calculation may be done using a machine learning method, a rule based method, or both.

In a method for the automatic adjustment of the speed of a motorcycle during a turning maneuver, the current position of incline of the motorcycle is determined and is compared with a target position of incline, the speed being reduced or increased as a function of the difference.

A controller for a vehicle can have a processor configured to determine a target route for the vehicle. At least one terrain feature can be identified along the target route. Based on the at least one terrain feature identified along the target route, the processor can estimate a traction torque for propelling the vehicle along at least a portion of the target route. Either or both a torque control signal and a steering control signal can be generated based on the estimated traction torque.

A device includes a body operatively connected to a plurality of wheels, with the plurality of wheels being positioned relative to a banked surface defining a bank angle (). A suspension system includes at least one suspension sensor configured to provide suspension displacement data. A controller is in communication with the at least one suspension sensor and has a processor and tangible, non-transitory memory on which is recorded instructions. The controller is configured to obtain the suspension displacement data and determine a roll angle () based at least partially on the suspension displacement data. The bank angle () is determined based at least partially on the roll angle (), a yaw rate (r), a longitudinal velocity (V.sub.x) and a plurality of predetermined parameters. Operation of the device is controlled based partly on at least one of the roll angle () and the bank angle ().

Systems and methods are provided for mitigating motion sickness. When motion sickness is predicted, the motion sickness mitigation system alters vehicle performance, cabin conditions, and/or alerts the occupant to upcoming vehicle actions, to avoid or alleviate motion sickness. A controller includes a processor that receives an occupant profile and traffic information for an upcoming trip of the vehicle on a route. Using the occupant profile and the traffic information, the processor calculates whether the occupant will experience motion sickness when the vehicle travels on the route. A vehicle performance signal correlated to the calculation, is delivered by the processor to initiate motion sickness mitigation. The vehicle performance signal varies operation of a steering actuator, an acceleration actuator, and/or a brake actuator to implement the motion sickness mitigation.

A method for operating a motor vehicle. A deviation of a current driving position of a driver of the motor vehicle from an expected reference driving position of the driver is determined, and the motor vehicle is controlled depending on the determined deviation.

A method for improving the safety and comfort of a vehicle driving over a railroad track, cattle guard, or the like. The method may include receiving, by a computer system, one or more inputs corresponding to one or more forward looking sensors. The computer system may also receive data characterizing a motion of the vehicle. The computer system may estimate, based on the one or more inputs and the data, a motion of a vehicle with respect to a railroad track, cattle guard, or the like extending across a road ahead of the vehicle. Accordingly, the computer system may change a suspension setting, steering setting, or the like of the vehicle to more safely or comfortably drive over the railroad track, cattle guard, or the like.