A system for user interaction with an automated device includes a control system configured to operate the device during an operating mode corresponding to a first state in which the control system automatically controls the device operation, and the operating mode prescribes that a user monitor the device operation during automated control. The control system is configured to allocate a time period for the device to transition to a temporary state in which automated control is maintained and the user is permitted to stop monitoring and perform a task unrelated to device operation. The system includes a user interaction system including a visual display configured to present trajectory information, an indication as to whether an area is conducive to putting the device in the temporary state, and time period allocation information, the user interaction system including an interface engageable by the user to manage scheduling of allocated time period(s).

A method, system and medium for assisted driving of a vehicle are disclosed. The method comprises predicting a target intersection based on map information, a positioning location of the vehicle, and a motion status of the vehicle, where the target intersection is an intersection that the vehicle is to first pass in the future; determining a distance between the vehicle and the target intersection based on the map information and the positioning location of the vehicle; collect a turn light status of the vehicle; and when the distance between the vehicle and the target intersection is less than a first threshold, generating prompt information based on the turn light status of the vehicle and a first turning rule of a first lane in which the vehicle is located at the target intersection, where the prompt information prompts that the turn light status does not conform to the first turning rule.

Various aspects of a system and method for driving assistance along a path are disclosed herein. In accordance with an embodiment, a unique identifier is received from a communication device at an electronic control unit (ECU) of a first vehicle. The unique identifier is received when the first vehicle has reached a first location along a first portion of the path. A communication channel is established between the first vehicle and the communication device based on the received unique identifier. Data associated with a second portion of the path is received by the ECU from the communication device based on the established communication channel. Alert information associated with the second portion of the path is generated by the ECU based on the received data.

Systems and methods are provided to implement cooperative traffic congestion detection, and enhance the accuracy of detection of traffic congestion for enhanced routing and maneuvering vehicles along a travel route. A vehicle is configured to receive vehicle data from an ad-hoc network of a plurality of vehicles that are communicatively connected (and proximately located). A subset of the plurality of vehicles can be sensor-rich vehicles that are equipped with ranging sensors (e.g., cameras, LIDAR, radar, ultrasonic sensors), which enables real-time detection of the multiple traffic parameters, such as the presence of other vehicles, vehicle speed, vehicle movement, traffic, and the like, within the vicinity along the route. The vehicle employs cooperative traffic congestion detection, and fuses data from the plurality of vehicles, including sensor-rich vehicles and legacy vehicles, and applies a learning-based algorithm, such as a machine-learning (ML) algorithm, to generate a real-time and more accurate estimate of traffic congestion.

The present invention is directed to a Spatiotemporal scene-graph embedding methodology that models scene-graphs and resolves safety-focused tasks for autonomous vehicles. The present invention features a computing system comprising instructions for accepting the one or more images, extracting one or more objects from each image, computing an inverse-perspective mapping transformation of the image to generate a bird's-eye view (BEV) representation of each image, calculating relations between each object for each image, and generating a scene-graph for each image based on the aforementioned calculations. The system may further comprise instructions for calculating a confidence value for whether or not a collision will occur through the generation of a spatio-temporal graph embedding based on a spatial graph embedding and a temporal model.

A driving assist device includes, a traveling environment information acquisition unit, an intersection determination unit, a vehicle position information acquisition unit, a predicted travel path setting unit, a blinker signal acquisition unit, a traffic division information acquisition unit, a determination unit, and a notifier. The determination unit determines whether directions indicated by three factors of traffic division information acquired by the traffic division information acquisition unit; a blinker signal acquired by the blinker signal acquisition unit, and a predicted travel path predicted by the predicted travel path setting unit match each other. When the determination unit determines that directions indicated by two factors of the three factors match each other and a mismatch occurs between the directions indicated by the two factors and a direction indicated by a remaining factor of the three factors, the notifier issues a notification about the mismatch of the direction indicated by the remaining factor.

Systems and methods for situational behavior of an autonomous vehicle are disclosed. In one aspect, an autonomous vehicle includes at least one perception sensor configured to generate perception data indicative of at least one other vehicle on a roadway, a non-transitory computer readable medium, and a processor. The processor is configured to determine that the other vehicle is violating one or more rules of the roadway based on the perception data, tag the other vehicle as a non-compliant driver, and modify control of the autonomous vehicle in response to tagging the other vehicle as a non-compliant driver.

A method and system for road condition monitoring including receiving roadway data from connected vehicles in response to a third party request. The connected vehicles include sensors for capturing the roadway data. The method and system include determining a roadway condition based on the roadway data and calculating a data price based on the roadway condition and the roadway data. Further, the method and system include controlling access to the roadway data according to the data price.

Systems, apparatuses and methods may provide for technology that generates, via a first neural network such as a grid network, a first vector representing a prediction of future behavior of an autonomous vehicle based on a current vehicle position and a vehicle velocity. The technology may also generate, via a second neural network such as an obstacle network, a second vector representing a prediction of future behavior of an external obstacle based on a current obstacle position and an obstacle velocity, and determine, via a third neural network such as a place network, a future trajectory for the vehicle based on the first vector and the second vector, the future trajectory representing a sequence of planned future behaviors for the vehicle. The technology may also issue actuation commands to navigate the autonomous vehicle based on the future trajectory for the vehicle.

A method for determining a speed profile to be followed by a vehicle, including acquiring event data including a distance from an event and a target speed at this event for the vehicle, and determining a speed profile to be followed as a function of time, between an initial speed and the target speed in three successive distinct phases, respectively a first phase in which the jerk is set constant at a predetermined maximum jerk value to reach an optimal target acceleration value, a second phase in which the optimal target acceleration value is kept constant, and a third phase in which the jerk is again set constant to reach a zero acceleration value at the end of the third phase. The optimal target acceleration value is such that the distance required to carry out the three phases of the profile is equal to the distance from the event.