A radar target simulation system for simulating at least one radar target is disclosed. The radar target simulation system includes a processing circuit and an antenna array that is connected with the processing circuit. The antenna array is configured to receive a radar signal from a device under test, thereby generating an input signal. The processing circuit is configured to receive the input signal generated by the antenna array. The processing circuit is configured to simulate the at least one radar target based on the input signal. The processing circuit further is configured to simulate at least one additional event, wherein the at least one additional event is associated with at least one of the at least one radar target and an environment of the at least one radar target. The processing circuit is configured to generate an output signal for the antenna array based on the simulation of the at least one radar target and based on the simulation of the at least one additional event. Moreover, a radar target simulation method is described.

An autonomous or semi-autonomous vehicle can detect an imminent collision according to sensor data and responsively select an action or a sequence of actions to avoid the collision if avoidable, and to reduce or minimize the harm of the collision if unavoidable. An artificial intelligence model may be used to process the sensor data, detect the imminent collision, and select the collision avoidance action or actions, or the harm-reduction or harm-minimization action or actions. For example, when the collision is considered unavoidable, the vehicle may apply the brakes to reduce the vehicle's speed and therefore, the severity of the impact. When the collision is considered avoidable, the vehicle may automatically apply steering to avoid the potential collision, such as when the vehicle is departing a lane and may collide with a vehicle traveling in the same or opposite direction.

An adaptive vehicle control system that includes processors, memory modules communicatively coupled to the processors, and machine readable instructions stored in the one or more memory modules that cause the adaptive vehicle control system to determine an autonomous operation profile of a target vehicle positioned in a vehicle operating environment, wherein the vehicle operating environment includes a roadway having one or more lanes, determine an autonomous operation profile of one of more neighboring vehicles positioned within the vehicle operating environment, compare the autonomous operation profile of at least one of the one or more neighboring vehicles with the autonomous operation profile of the target vehicle, and alter a condition of the target vehicle such that the autonomous operation profile of the target vehicle matches an autonomous operation profile of an individual neighboring vehicle of the one or more neighboring vehicles positioned in the same lane as the target vehicle.

An adaptive vehicle control system that includes processors, memory modules communicatively coupled to the processors, and machine readable instructions stored in the one or more memory modules that cause the adaptive vehicle control system to determine an autonomous operation profile of a target vehicle positioned in a vehicle operating environment, wherein the vehicle operating environment includes a roadway having one or more lanes, determine an autonomous operation profile of one of more neighboring vehicles positioned within the vehicle operating environment, compare the autonomous operation profile of at least one of the one or more neighboring vehicles with the autonomous operation profile of the target vehicle, and alter a condition of the target vehicle such that the autonomous operation profile of the target vehicle matches an autonomous operation profile of an individual neighboring vehicle of the one or more neighboring vehicles positioned in the same lane as the target vehicle.

A method is disclosed to operate a motor vehicle using a driver assistance system. The driver assistance system includes, for at least one driver assistance system function, at least one criterion relating to a vehicle environment. The at least one criterion is defined for an adaptation of the driver assistance system function, a number of features of the environment of the vehicle are detected, a probability of the occurrence of at least one criterion is estimated on the basis of a combination of the detected features, and the driver assistance system function is adapted on the basis of the estimated probability.

A system and method to avoid collisions on highways, and to minimize the fatalities, injury, and damage when a collision is unavoidable. The system includes sensor means to detect other vehicles, and computing means to evaluate when a collision is imminent and to determine whether the collision is avoidable. If the collision is avoidable by a sequence of controlled accelerations and decelerations and steering, the system implements that sequence of actions automatically. If the collision is unavoidable, a different sequence is implemented to minimize the overall harm of the unavoidable collision. The system further includes indirect mitigation steps such as flashing the brake lights automatically. An optional post-collision strategy is implemented to prevent secondary collisions, particularly if the driver is incapacitated. Adjustment means enable the driver to set the type and timing of automatic interventions.

An adaptive vehicle control system that includes processors, memory modules communicatively coupled to the processors, and machine readable instructions stored in the one or more memory modules that cause the adaptive vehicle control system to determine an autonomous operation profile of a target vehicle positioned in a vehicle operating environment, wherein the vehicle operating environment includes a roadway having one or more lanes, determine an autonomous operation profile of one of more neighboring vehicles positioned within the vehicle operating environment, compare the autonomous operation profile of at least one of the one or more neighboring vehicles with the autonomous operation profile of the target vehicle, and alter a condition of the target vehicle such that the autonomous operation profile of the target vehicle matches an autonomous operation profile of an individual neighboring vehicle of the one or more neighboring vehicles positioned in the same lane as the target vehicle.

Vehicles can be operated according to an artificial intelligence model contained in an on-board processor. The AI model can analyze sensor data, such as visible or infrared images of traffic, and determine when a collision is possible, whether it has become imminent, and whether the collision is avoidable or unavoidable using sequences of accelerations, braking, and steering. The AI model can also select the most appropriate sequence of actions from a large plurality of calculated sequences to avoid the collision if avoidable, and to minimize the harm if unavoidable. The AI model can also cause a processor to actuate linkages connected to the throttle (or electric power control), brakes (or regenerative braking), and steering to implement the selected sequence of actions. Thus the collision can be avoided or mitigated by an ADAS system or a fully autonomous vehicle.

An autonomous or semi-autonomous vehicle can detect an imminent collision according to sensor data and responsively select an action or a sequence of actions to avoid the collision if avoidable, and to reduce or minimize the harm of the collision if unavoidable. An artificial intelligence model may be used to process the sensor data, detect the imminent collision, and select the collision avoidance action or actions, or the harm-reduction or harm-minimization action or actions. For example, when the collision is considered unavoidable, the vehicle may apply the brakes to reduce the vehicle's speed and therefore, the severity of the impact. When the collision is considered avoidable, the vehicle may automatically apply steering to avoid the potential collision, such as when the vehicle is departing a lane and may collide with a vehicle traveling in the same or opposite direction.

A multi layer learning based control system and method for an autonomous vehicle or mobile robot. A mission planning layer, behavior planning layer and motion planning layer each having one or more neural neworks are used to develop an optimal route for the autonomous vehicle or mobile robot, provide a series of functional tasks associated with at least one or more of the neural networks to follow the planned optimal route and develop commands to implement the functional tasks.