An autonomous vehicle collects sensor data of an environment surrounding the autonomous vehicle including traffic entities such as pedestrians, bicyclists, or other vehicles. The sensor data is provided to a machine learning based model along with an expected turn direction of the autonomous vehicle to determine a hidden context attribute of a traffic entity given the expected turn direction of the autonomous vehicle. The hidden context attribute of the traffic entity represents factors that affect the behavior of the traffic entity, and the hidden context attribute is used to predict future behavior of the traffic entity. Instructions to control the autonomous vehicle are generated based on the hidden context attribute.

A vehicle control device includes a peripheral recognition unit configured to recognize a peripheral status of a vehicle including a position of a traffic participant present in a periphery of the vehicle on the basis of an output of an in-vehicle device, an estimation unit configured to estimate a peripheral attention ability of the traffic participant on the basis of an output of the in-vehicle device, and a risk area setting unit configured to set a risk area of the traffic participant on the basis of a result of the estimation performed by the estimation unit.

The invention relates to an adaptive cruise control system for a vehicle configured for determining, for each preceding vehicle driving ahead of the vehicle, a candidate target acceleration for modifying the acceleration of the vehicle depending on whether the vehicle is in an inevitable collision state and on how comfortably a respective safety distance can be established. Thereby, the acceleration of the vehicle is always adapted with an optimal balance between safety and comfort. The invention further relates to a vehicle incorporating such an adaptive cruise control system and to a corresponding method of determining a target acceleration of a vehicle.

An impairment analysis (“IA”) computer system for alerting a first driver of a first vehicle to a driving hazard posed by a second vehicle operated by a second driver is provided. The IA computer system is associated with the first vehicle, and includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: (i) receive second vehicle data including second driver data and second vehicle condition data, where the second vehicle data is collected by a plurality of sensors included on the first vehicle; (ii) analyze the second vehicle data by applying a baseline model to the second vehicle data; (iii) determine that the second vehicle poses a driving hazard to the first vehicle based upon the analysis; and/or (iv) generate an alert signal based upon the determination that the second vehicle poses a driving hazard to the first vehicle.

A vehicle control method executed by a processor capable of making a subject vehicle change a lane includes: acquiring surrounding information of the subject vehicle by a sensor provided in the subject vehicle; determining whether a distractive factor for a driver of another vehicle is present when the subject vehicle enters the front of the other vehicle traveling on the second lane for changing lanes from a first lane to a second lane adjacent to the first lane; setting a lane change time required for the subject vehicle to change lanes longer than when determining none of the distractive factor to be present when determining distractive the factor to be present; and controlling a traveling position of the subject vehicle on the first lane within the lane change time.

A control device that controls a vehicle includes: an input unit to which an image including another vehicle or a driver of another vehicle is input; and a control unit that outputs a signal for controlling the vehicle generated based on danger in the another vehicle determined based on the image.

A vehicle control method executed by a processor capable of making a subject vehicle change a lane includes: acquiring surrounding information of the subject vehicle by a sensor provided in the subject vehicle; determining whether a distractive factor for a driver of another vehicle is present when the subject vehicle enters the front of the other vehicle traveling on the second lane for changing lanes from a first lane to a second lane adjacent to the first lane; setting a lane change time required for the subject vehicle to change lanes longer than when determining none of the distractive factor to be present when determining distractive the factor to be present; and controlling a traveling position of the subject vehicle on the first lane within the lane change time.

A vehicle control device includes a recognition unit which recognizes surrounding situations of a vehicle, and a driving control unit which automatically controls at least steering of the vehicle based on the surrounding situations recognized by the recognition unit, and the driving control unit increases a distance between the vehicle and a traffic participant in the case where the recognition unit recognizes the traffic participant as an overtaking target and a predetermined structure in a traveling direction of the vehicle, and the vehicle travels on a side opposite to the predetermined structure in a road width direction in a case that viewed from the traffic participant to overtake the traffic participant, as compared to a case where the traffic participant as the overtaking target is recognized by the recognition unit in the traveling direction of the vehicle and the predetermined structure is not recognized.

An impairment analysis (“IA”) computer system for detecting a driver or vehicle impairment is provided. The IA computer system is associated with a host vehicle, and includes a plurality of sensors, and at least one processor in communication with the plurality of sensors and at least one memory device. The at least one processor is programmed to: (i) interrogate a target vehicle via the plurality of sensors by scanning at least one of the target vehicle and a target driver; (ii) receive sensor data including at least one of target driver data and target vehicle condition data; (iii) analyze the sensor data to determine whether at least one of lane drift and vehicle speed deviation for the target vehicle exceeds a respective threshold; (iv) detect an impairment of the target driver or target vehicle based upon the analysis; and/or (v) direct collision avoidance action based upon the detection.

A system uses a machine learning based model to determine attributes describing states of mind and behavior of traffic entities in video frames captured by an autonomous vehicle. The system classifies video frames according to traffic scenarios depicted, where each scenario is associated with a filter based on vehicle attributes, traffic attributes, and road attributes. The system identifies a set of video frames associated with ground truth scenarios for validating the accuracy of the machine learning based model and predicts attributes of traffic entities in the video frames. The system analyzes video frames captured after the set of video frames to determine actual attributes of the traffic entities. Based on a comparison of the predicted attributes and actual attributes, the system determines a likelihood of the machine learning based model making accurate predictions and uses the likelihood to generate a navigation action table for controlling the autonomous vehicle.