A context-aware safety device includes a wireless transceiver, a memory storing an application, and one or more processors. When executing the application, the one or more processors are configured to determine a context of a safety device, configure an alert based on the determined context, and broadcast the configured alert using the wireless transceiver.

Systems and methods for connected vehicle and mobile device communications are provided herein. An example method includes determining a distracted condition for at least one of a driver or a pedestrian by evaluating actions occurring within in a vehicle of the driver or on a mobile device of the pedestrian; determining a distraction level for either the driver or the pedestrian based on the actions occurring within in the vehicle or on the mobile device; and providing an alert message to the mobile device or a human machine interface of the vehicle based on the distraction level, the alert message warning of a distracted condition of the pedestrian or the driver.

The present disclosure relates to a method and system for communication between a vulnerable road user and an autonomous vehicle using augmented reality to highlight information to the vulnerable road user regarding potential interactions between the autonomous vehicle and the vulnerable road user.

The present disclosure relates to a method and system for communication between a vulnerable road user and an autonomous vehicle using augmented reality to highlight information to the vulnerable road user regarding potential interactions between the autonomous vehicle and the vulnerable road user.

A vehicle peripheral monitoring apparatus includes an autonomous driving level that recognizes an autonomous driving level of another vehicle present around a subject vehicle, a driver condition acquisition part that acquires a driver condition of said another vehicle, a storage that stores autonomous driving levels and acceptable driver conditions in association with each other, a determination part that compares the acceptable driver condition, which is stored in the storage, at the autonomous driving level recognized by the autonomous driving level recognition part with the driver condition of said another vehicle acquired by the driver condition acquisition part, in order to determine whether or not the driver condition of said another vehicle is acceptable, and a notification part notifies a warning to a driver of the subject vehicle when the determination part determines that the driver condition of said another vehicle is not acceptable.

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.

Systems and methods described herein relate to estimating risk associated with a vehicular maneuver. One embodiment acquires a geometric representation of an intersection including a lane in which a vehicle is traveling and at least one other lane; discretizes the at least one other lane into a plurality of segments; determines a trajectory along which the vehicle will travel; estimates a probability density function for whether a road agent external to the vehicle is present in the respective segments; estimates a traffic-conflict probability of a traffic conflict in the respective segments conditioned on whether an external road agent is present; estimates a risk associated with the vehicle following the trajectory by integrating a product of the probability density function and the traffic-conflict probability over the at least one other lane and the plurality of segments; and controls operation of the vehicle based, at least in part, on the estimated risk.

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.

A method for carrying out a prediction of a driving behavior of a second vehicle by a control unit of a first vehicle. Data of vehicle surroundings of the second vehicle, and/or data of a vehicle driver and/or of a load of the second vehicle being received by the control unit, at least one feature being ascertained based on the data and a likely driving behavior of the second vehicle being calculated by the control unit based on the ascertained feature. A control unit is also described.

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.