An in-vehicle system includes: a detection unit that detects visual line information of a passerby on the basis of an image obtained by capturing an image in front of a vehicle; a decision unit that decides a motion of the vehicle on the basis of the visual line information of the passerby which is detected by the detection unit; and operation units which perform processing corresponding to the motion of the vehicle which is decided by the decision unit. As a result, the in-vehicle system can attain an effect capable of improving communication between the vehicle and the passerby on the basis of the visual line information of the passerby.

A vehicle may include a communicator configured to receive driver state information from a surrounding vehicle, a detector configured to obtain driving information related to surrounding vehicle, a driving assistance module configured to control at least one of a driving speed or a driving direction and a controller configured to determine whether a driver of the surrounding vehicle is in drowsiness state based on whether the received driver state information satisfies a predetermined condition and if the driver of the surrounding vehicle is determined as drowsiness state, control the driving assistance module to avoid the surrounding vehicle.

The present disclosure relates to a collision control method and apparatus, an electronic device, and a storage medium. The method includes: detecting a target object in an image photographed by a current object; determining a danger level of the target object; and executing collision control corresponding to the danger level.

An impairment analysis (IA) computer system for detecting an impairment is provided. The IA computer system is associated with a host vehicle, and includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: (i) interrogate or otherwise scan a target vehicle by using a plurality of sensors included on a host vehicle to scan the target vehicle and a target driver; (ii) receive sensor data including target driver data and target vehicle condition data; (iii) analyze the sensor data by applying a baseline model to the sensor data; (iv) detect an impairment of the target driver or target vehicle based upon the analysis; and/or (v) output an alert signal to a host vehicle controller, or direct collision preventing actions (such as automatically engage vehicle safety systems), based upon the determination that the target driver or target vehicle is impaired.

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.

An autonomous vehicle uses machine learning based models to predict hidden context attributes associated with traffic entities. The system uses the hidden context to predict behavior of people near a vehicle in a way that more closely resembles how human drivers would judge the behavior. The system determines an activation threshold value for a braking system of the autonomous vehicle based on the hidden context. The system modifies a world model based on the hidden context predicted by the machine learning based model. The autonomous vehicle is safely navigated, such that the vehicle stays at least a threshold distance away from traffic entities.

An autonomous vehicle uses machine learning based models to predict hidden context attributes associated with traffic entities. The system uses the hidden context to predict behavior of people near a vehicle in a way that more closely resembles how human drivers would judge the behavior. The system determines an activation threshold value for a braking system of the autonomous vehicle based on the hidden context. The system modifies a world model based on the hidden context predicted by the machine learning based model. The autonomous vehicle is safely navigated, such that the vehicle stays at least a threshold distance away from traffic entities.

A control device includes a controller. The controller detects, as an event, that a target present outside a vehicle has been overlooked by a driver of the vehicle during. The controller controls the outputting of a target image, which is an image including the target overlooked in the detected event, toward the driver.

A method for defining a target deceleration for an ego transportation vehicle including determining at least one motional variable of a transportation vehicle ahead; determining a braking time and a braking distance based on the motional variable which the transportation vehicle ahead respectively needs to come to a standstill; determining for the ego transportation vehicle a braking time that is needed to come to a standstill at the latest at the same position as the transportation vehicle ahead when the transportation vehicle ahead has travelled the braking distance; defining a target deceleration for the ego transportation vehicle based on a relationship of the braking times of the transportation vehicle ahead and the ego transportation vehicle. An apparatus for defining a target deceleration for an ego transportation vehicle and a transportation vehicle having such an apparatus.

Vehicle manipulation is performed using occupant image analysis. A camera within a vehicle is used to collect cognitive state data including facial data, on an occupant of a vehicle. A cognitive state profile is learned, on a first computing device, for the occupant based on the cognitive state data. The cognitive state profile includes information on absolute time. The cognitive state profile includes information on trip duration time. Voice data is collected and the cognitive state data is augmented with the voice data. Further cognitive state data is captured, on a second computing device, on the occupant while the occupant is in a second vehicle. The further cognitive state data is compared, on a third computing device, with the cognitive state profile that was learned for the occupant. The second vehicle is manipulated based on the comparing of the further cognitive state data.