A system, method, and processor-readable medium for assessing the reliability of vehicle systems used in an autonomous vehicle. The assessment may be performed at least in part on the basis of data collected by one or more of the vehicle's sensors. The result of the assessment may be used as the basis for decisions about vehicle operation carried out by an autonomous driving module.

A system and method for adaptive cruise control for defensive driving are disclosed. A particular embodiment includes: receiving input object data from a subsystem of an autonomous vehicle, the input object data including distance data and velocity data relative to a lead vehicle; generating a weighted distance differential corresponding to a weighted difference between an actual distance between the autonomous vehicle and the lead vehicle and a desired distance between the autonomous vehicle and the lead vehicle; generating a weighted velocity differential corresponding to a weighted difference between a velocity of the autonomous vehicle and a velocity of the lead vehicle; combining the weighted distance differential and the weighted velocity differential with the velocity of the lead vehicle to produce a velocity command for the autonomous vehicle; and controlling the autonomous vehicle to conform to the velocity command.

The present disclosure provides a controller for controlling an ego vehicle in an uncertain environment. The controller is caused to acquire knowledge of the environment from measurements associated with sensors the ego. The measurements are based on a state of the ego vehicle and sensing instructions associated with controlling an operation of the sensors. The controller is further caused to estimate a state of the environment, including uncertainty of a state of the at least one moving object or obstacle in the environment. Further a sequence of control inputs is determined by solving a multivariable and a multistage stochastic constrained optimization of a model of the motion of the ego vehicle. The controller is then caused to control the ego vehicle and the sensors based on the sequence of control inputs and the sequence of sensing instructions.

An apparatus for controlling a vehicle includes a vehicle additional yaw moment calculator that calculates, based on a yaw rate of a vehicle, a vehicle additional yaw moment to be applied to the vehicle independently of a steering system, a slipping condition determiner that makes a determination as to a slipping condition of the vehicle, and an adjustment gain calculator that calculates an adjustment gain to adjust the vehicle additional yaw moment so as to reduce the vehicle additional yaw moment additional yaw moment when the vehicle is determined to be in the slipping condition, and increases the adjustment gain in accordance with a degree of a slip of the vehicle when the vehicle is determined to recover from the slipping condition.

Described embodiments include a self-propelled vehicle, method, and system. The self-propelled vehicle includes an autonomous driving system configured to dynamically determine maneuvers operating the vehicle along a route in an automated mode without continuous input from a human driver. The vehicle includes an input device configured to receive a real-time request for a specific dynamic maneuver by the vehicle operating along the route from the human driver. The vehicle includes a decision circuit configured to select a real-time dynamic maneuver by arbitrating between (i) the received real-time request for the specific dynamic maneuver from the human driver and (ii) a real-time determination relative to the specific dynamic maneuver received from the autonomous driving system. The vehicle includes an implementation circuit configured to output the selected real-time dynamic maneuver to an operations system of the vehicle.

A method controls an energy equivalence factor of a motor vehicle including a heat engine and at least one electric motor powered by a storage battery. The method includes estimating a value of the energy equivalence factor proportional to a predetermined maximum value when the difference is lower than the threshold value or proportional to a predetermined minimum value when the difference is higher than the threshold value.

A vehicle motion control device according to the present invention obtains a translation force for causing the position of a vehicle to trace a target travel path, on the basis of a lateral displacement amount which is an amount of displacement of the vehicle in a lateral direction with respect to a target movement point, obtain a rotational force for correcting an orientation of the vehicle with respect to the target travel path, on the basis of an orientation displacement amount which is an amount of displacement of the vehicle in a yaw direction with respect to the target movement point, weight the translation force and the rotational force on the basis of specifications relating to traveling of the vehicle, and output a control command for achieving a target lateral force obtained by adding up the weighted translation force and the weighted rotational force.

A method is described and includes subsequent to an autonomous vehicle becoming immobilized, initiating a local assistance request; subsequent to the initiating, receiving local assistance input from a passenger of the autonomous vehicle; and using the local assistance input to determine an action to be taken by the autonomous vehicle to mobilize the autonomous vehicle.

A speed of a target vehicle in a target lane of operation is determined relative to a host vehicle in a host lane of operation. A virtual boundary is determined around the target vehicle based on the speed of the target vehicle. A position in the target lane and outside the virtual boundary is selected based on a) a first cost function for a deviation of a speed of the host vehicle from a requested speed, and b) a second cost function for a frequency of lane changes. Upon determining to move the host vehicle from the host lane to the target lane, the host vehicle is operated to the position in the target lane.

A method of generating an output trajectory of an ego vehicle includes recording trajectory data of the ego vehicle and pedestrian agents from a scene of a training environment of the ego vehicle. The method includes identifying at least one pedestrian agent from the pedestrian agents within the scene of the training environment of the ego vehicle causing a prediction-discrepancy by the ego vehicle greater than the pedestrian agents within the scene. The method includes updating parameters of a motion prediction model of the ego vehicle based on a magnitude of the prediction-discrepancy caused by the at least one pedestrian agent on the ego vehicle to form a trained, control-aware prediction objective model. The method includes selecting a vehicle control action of the ego vehicle in response to a predicted motion from the trained, control-aware prediction objective model regarding detected pedestrian agents within a traffic environment of the ego vehicle.