The invention provides a vehicle speed control system having a side-slope detection system (10, 19, 185C). The system comprises a processor (10, 19) arranged to receive, from one or more sensors (185C) arranged to capture data in respect of terrain ahead of the vehicle, terrain information indicative of the topography of an area extending ahead of the vehicle (100). The processor (10, 19), in dependence upon a predicted path of the vehicle (100) over said terrain extending ahead of the vehicle (100), generates, for the predicted path of the vehicle (100), information indicative of the angle of side-slope of the predicted path, being the slope of the predicted path transverse to a direction of travel of the vehicle (100). The vehicle speed control system is configured to control vehicle speed in dependence at least in part on the information indicative of the angle of side-slope of the predicted path generated by the side-slope detection system.

A method is provided for danger prediction. The method includes generating fully-annotated simulated training data for a machine learning model responsive to receiving a set of computer-selected simulator-adjusting parameters. The method further includes training the machine learning model using reinforcement learning on the fully-annotated simulated training data. The method also includes measuring an accuracy of the trained machine learning model relative to learning a discriminative function for a given task. The discriminative function predicts a given label for a given image from the fully-annotated simulated training data. The method additionally includes adjusting the computer-selected simulator-adjusting parameters and repeating said training and measuring steps responsive to the accuracy being below a threshold accuracy. The method further includes predicting a dangerous condition relative to a motor vehicle and providing a warning to an entity regarding the dangerous condition by applying the trained machine learning model to actual unlabeled data for the vehicle.

Artificial intelligence vehicle systems include vehicle guidance systems and adaptive, evolutionary driving training protocols for state machines. A state machine makes decisions based on information supplied by the sensors attached to the vehicle, the current state of the vehicle, the capabilities of the vehicle, and optionally the applicable traffic laws (e.g., if a roadway vehicle) or facility rules (e.g., if a facility vehicle, such as warehouse, construction site, campus, or the like). An autonomous driver of a state machine decides between possible actions given the current environment where those possible actions to existing conditions are represented by action rules, which may be referred to as genes. The adaptive systems enable improved vehicle guidance and can improve over time as new circumstances are encountered and processed.

A control system for providing a yaw moment control action is provided. The control system comprises a command interpreter and a control segment. The command interpreter is configured to generate desired current vehicle states, when a vehicle is driven manually, wherein the current vehicle states comprise a target yaw rate state and a target lateral velocity state. The command interpreter is further configured to generate a desired states vector, when the vehicle is driven autonomously, using vehicle path planning instructions, wherein the desired states vector comprises current and future ideal yaw rate and lateral velocity states. The control segment is configured to generate a yaw moment control action using the desired current vehicle states when the vehicle is driven manually and generate a yaw moment control action using the desired states vector when the vehicle is driven autonomously.

Systems and methods for situational modification of autonomous vehicle operation are disclosed. According to aspects, a computing device may detect the occurrence of an emergency event and may determine a current operation of an autonomous vehicle that may be associated with the emergency event. The computing device may determine a modification to operation of the autonomous vehicle, where the modification may represent a violation of a roadway regulation that may enable effective handling of the emergency event. The computing device may generate a set of instructions for the autonomous vehicle to execute to cause the autonomous vehicle to undertake the operation modification.

A driving assist device includes: a driving operation element; circuitry configured to acquire traveling state relevant information indicating a traveling state, control the own vehicle such that the own vehicle travels in a state where a target traveling condition is met, and determine whether the traveling state changed by the operation of the driving operation element is a specific state; and a request generation device configured to generate a condition change request when the predetermined operation or input is performed while the own vehicle is in the driving assist control, wherein the circuitry is configured to change the target traveling condition based on the traveling state relevant information when the condition change request is generated in a case where it is determined that the changed traveling state is the specific state.

A system and method to distinguish between a lane change and a turn of a vehicle as an intended movement of the vehicle based on a driver-initiated motion toward an adjacent lane that is adjacent to a lane occupied by the vehicle include obtaining input from one or more sources. The one or more sources include a sensor system or a communication system. The method also includes processing the input from the one or more sources to obtain indirect information that indicates a direction of travel in the lane adjacent or to obtain direct information specific to a location of the vehicle, determining whether the lane change or the turn is more likely to be the intended movement based on the indirect information or the direct information, and modifying an action of a vehicle system based on the determining whether the lane change or the turn is more likely.

A method is disclosed for operating a motor vehicle having a plurality of driver assistance systems, wherein the method includes setting, via an operator interface of the motor vehicle, an assistance degree parameter, which indicates the extent to which support for a driver by the driver assistance systems is desired. The method also includes setting, via the operator interface of the motor vehicle, at least one additional parameter, which is related to the driving operation of the motor vehicle. The method additionally includes specifying, by a processing device of the motor vehicle, at least one operating parameter for each driver assistance system, in accordance with which operating parameter the driver assistance system is operated, as a function of the assistance degree parameter and the additional parameter(s). A motor vehicle is also disclosed that performs the method.

In various examples, systems and methods are disclosed that accurately identify driver and passenger in-cabin activities that may indicate a biomechanical distraction that prevents a driver from being fully engaged in driving a vehicle. In particular, image data representative of an image of an occupant of a vehicle may be applied to one or more deep neural networks (DNNs). Using the DNNs, data indicative of key point locations corresponding to the occupant may be computed, a shape and/or a volume corresponding to the occupant may be reconstructed, a position and size of the occupant may be estimated, hand gesture activities may be classified, and/or body postures or poses may be classified. These determinations may be used to determine operations or settings for the vehicle to increase not only the safety of the occupants, but also of surrounding motorists, bicyclists, and pedestrians.

A controller receives sensor data during a ride and provides it to a server system. A passenger further provides feedback concerning the ride in the form of some or all of an overall rating, flagging of ride anomalies, and flagging of road anomalies. The sensor data and feedback are input to a training algorithm, such as a deep reinforcement learning algorithm, which updates an artificial intelligence (AI) model. The updated model is then propagated to controllers of one or more autonomous vehicle which then perform autonomous navigation and collision avoidance using the updated AI model.