A method for determining an optimized torque distribution to the wheels of a road vehicle comprising the steps of determining a table of distribution of the torque between a front axle and a rear axle; determining a second table and a third table of distribution of the torque between a right wheel and a left wheel of the rear axle and of the front axle, respectively; detecting the current longitudinal dynamics; using the first, the second and the third table to determine a current value of the first, of the second and of the third distribution factor, respectively, based on the current longitudinal speed and on the current longitudinal acceleration of the road vehicle.

A system, method, and computer-readable medium having instructions stored thereon to enable an ego vehicle having an autonomous driving function to estimate and traverse a curved segment of highway utilizing radar sensor data. The radar sensor data may comprise stationary reflections and moving reflections. The ego vehicle may utilize other data, such as global positioning system data, for the estimation and traversal. The estimation of the curvature may be refined based upon a lookup table or a deep neural network.

A method for providing a prediction of a radius of a motorcycle turn, the method may include determining that the motorcycle is about to turn; predicting values of multiple radius of turn impacting (RTI) parameters; wherein the multiple RTI parameters are selected out of a group of parameters, wherein the selection was made during a machine learning training process, and the group of parameters comprises motorcycle kinematic parameters; determining, based on the determined values of the multiple RTI parameters, the estimated radius of the motorcycle turn; and performing a driving related operation based on the estimated radius of the motorcycle turn.

Provided is a notification control apparatus for a vehicle configured to determine, based on surrounding information and an operation state of an operation unit, whether there exists a possibility that an own vehicle turns toward a specific direction while an oncoming vehicle is approaching, to determine, when affirmative determination is made, based on vehicle information including at least one of a steering input value or presence/absence of a brake operation and a vehicle speed, whether a possibility that the own vehicle completes crossing of an opposing lane or an intersection after elapse of a reference period is high, and when affirmative determination is made, to execute the notification control when a virtual passing period required for the own vehicle to virtually pass the oncoming vehicle is a lower limit period or longer and an upper limit period or shorter, the upper limit period being the reference period or shorter.

Methods and systems are provided for controlling an autonomous vehicle. In one embodiment, a method includes: identifying, by a processor, at least one constraint on a longitudinal dimension of an upcoming road; defining, by the processor, constraint activation logic based on a type of the at least one constraint; performing, by the processor, the constraint activation logic to determine a state of the constraint to be at least one of active and inactive; when the state of the constraint is active, validating, by the processor, a motion plan of the autonomous vehicle based on the constraint; and selectively controlling the autonomous vehicle based on the validating of the motion plan.

An electric vehicle controls turning of the electric vehicle in accordance with the orientation of the wheels and skid steering to match the path and turning radius as indicated by the steering wheel. A processing circuit detects the position of the steering wheel and determines the direction of the turn and the resulting path and turning radius of the electric vehicle. The processing circuit either measures the orientation of the wheels or captures data regarding the turning radius of the electric vehicle. The processing circuit controls the traction motors of the electric vehicle so that the actual path and turning radius of the electric vehicle matches the path and turning radius indicated by the steering wheel. Further, the processing circuit may further control controls the traction motors to attempted to maintain the speed of the electric vehicle as indicated by the throttle.

A trajectory for a vehicle can be generated using a lateral offset bias. The vehicle, such as an autonomous vehicle (AV), may be directed to follow reference trajectory for through an environment. The AV may determine a segment associated with the reference trajectory based on curvatures of the reference trajectory, determine a lateral offset bias associated with the segment based at least in part on, for example, one or more of a speed or acceleration of the vehicle, and determine a candidate trajectory for the autonomous vehicle based at least in part on the lateral offset bias. The candidate trajectory may then be used to control the autonomous vehicle.

A device for stabilizing a vehicle after a collision against a lateral carriageway boundary, includes a lane recognition system, with which information relating to the course of the lane is determined or detected. A collision detection unit identifies a collision of the vehicle against the lateral lane carriageway boundary on the basis of signals from at least one sensor or on the basis of a driving state variable. The device also includes a steering actuator for steering a steering system and a brake actuator for controlling one or more wheel brakes. A target path determination unit determines a target path for the vehicle on the basis of the course of the lane determined or detected before or at the time of the collision. A controller guides the vehicle onto the target path and/or stabilizes the vehicle via a steering intervention and/or individual wheel brake interventions.

A cruise control device 10 includes a cutting-in/deviation determination unit 12 for performing cutting-in determination and deviation determination of another vehicle. The cutting-in/deviation determination unit 12 calculates a lateral position that is a position in a vehicle width direction of a forward vehicle 51 traveling ahead of an own vehicle 50, and determines the forward vehicle 51 traveling on an adjacent lane 64 to be a cutting-in vehicle into an own lane 63 and determines the forward vehicle 51 traveling on the own lane 63 to be a deviating vehicle from the own lane 63 on the basis of the calculated lateral position. The cutting-in/deviation determination unit 12 determines whether or not the own vehicle 50 is in a predetermined own vehicle turning state that is either one of a state before starting a turn or a state of turning, and determines permission of performing of cutting-in determination and deviation determination of the other vehicle on the basis of the determination result.

A racing coach device stores a first path of travel along a racetrack over a first time period and a second path of travel along the racetrack over a second time period. The racing coach device identifies, for each of a plurality of geolocations along the racetrack, one of the first path of travel or the second path of travel that is associated with a shorter duration of time over which the user traversed a segment of the path of travel associated with each of the plurality of geolocations. The device determines an optimal path of travel along the racetrack based on the identified first and second path of travel for each segment of the path of travel at each of the plurality of geolocations that results in a calculated lap time to traverse the racetrack that is less than the first time period and the second time period.