Abstract: A coordination support device includes a target traffic area recognizer configured to recognize traffic participants, a predictor configured to predict futures of a plurality of the traffic participants, and a coordination support information notifier configured to notify notification targets. The predictor includes a group classifier configured to divide the plurality of traffic participants into a plurality of moving groups G1 to G9 and acquire moving group information, a first predictor configured to predict whether or not a collision will occur among the moving groups G1 to G9 on the basis of the moving group information and specifies collision sites in the moving groups that are predicted to collide, and a second predictor configured to determine two or more specific traffic participants among the plurality of traffic participants on the basis of the collision sites and predict whether or not a collision will occur among the specific traffic participants.

Abstract: A predictor 62 of a traffic safety support system includes a movement state information acquirer 620 configured to acquire movement state information of the prediction target, a surrounding state information acquirer 621 configured to acquire surrounding state information, a first movement state predictor 624 configured to predict a first predicted movement state on the basis of the movement state information, a second movement state predictor 625 configured to predict a second predicted movement state on the basis of the driver state information, a collision risk calculator 626 configured to calculate a first collision risk value for the first predicted movement state and a second collision risk value for the second predicted movement state, and a support controller 65 configured to execute support control for the prediction target in a case where at least one of the first and second collision risk values is greater than a predetermined threshold value.

Abstract: A predictor 62 predicts a future of a prediction target in a traffic area. The predictor 62 includes a movement state information acquirer 620 configured to acquire movement state information, a surrounding state information acquirer 621 configured to acquire surrounding state information, a traffic environment information acquirer 622 configured to acquire traffic environment information, a driver state information acquirer 623 configured to acquire driver state information, a traffic scene specifier 624 configured to specify a traffic scene on the basis of the movement state information, the surrounding state information, and the traffic environment information, an action pattern selector 625 configured to select, as a predicted action pattern, at least one from among a plurality of predetermined action patterns on the basis of the traffic scene and the driver state information, and an action predictor 626 configured to predict future action on the basis of the predicted action pattern.

Abstract: A predictor 62 predicts a future of a prediction target in a monitoring area. The predictor 62 includes a movement state information acquirer 620 configured to acquire movement state information, a surrounding state information acquirer 621 configured to acquire surrounding state information regarding movement states of traffic participants around the prediction target, a confirmation state information acquirer 623 configured to acquire confirmation state information regarding a surrounding confirmation state of a driver of the prediction target, a moving speed predictor 624 configured to predict a future moving speed of the prediction target on a basis of the movement state information, the surrounding state information, and the confirmation state information, and a collision predictor 625 configured to predict whether or not a collision will occur in a future of the prediction target on the basis of the movement state information, the surrounding state information, and the future moving speed.

Abstract: The present invention relates to a system for evaluating a mission of a mobile entity. The system comprises a means for obtaining information on a planned trajectory, the mobile entity, mission requirements, environment and public body rules, a means for transforming the information into a symbolic, probabilistic representation to generate a probabilistic logic program, wherein facts are extracted from the information and systematically designated with names within the probabilistic logic program, and a means for generating an answer to the a probabilistic logic query based on the probabilistic logic program.

Abstract: A computer-implemented method is provided. The method calculates an importance score for each agent of the at least one other agent based on the acquired sensor data and the acquired position and motion data, generates a list comprising the at least one other agent based on the calculated importance scores of the at least one other agent, partitions the generated list in at least one partition based on the importance score of the at least one other agent, and generates a prediction result by predicting a behavior of the at least one other agent included in the at least one partition based on the sensor data and the position and motion data using a selected behavior prediction model. The selected behavior prediction model is selected from a plurality of behavior prediction models based on the importance score of the at least one agent included in the at least one partition.

Abstract: A method and system for assisting operation of an ego-vehicle in the traffic situation with shared traffic space is provided, in which a future path for the ego-vehicle and a future path for another traffic participant and a relevant path corridor for each determine future path is determined. Based thereon, an overlap of the path corridors to define a shared traffic space is determined and individual distance measures between the ego-vehicle and the shared traffic space and between the other traffic participant and the shared traffic space are calculated to determine an asymmetry. Based on the asymmetry an order is determined and (partially) automated in accordance with trajectory determined based on the order, or information on the intended passing order is provided to the ego-vehicle driver.

Abstract: The present invention relates to a method for assisting a driver of an ego-vehicle in a traffic situation, wherein an ego-trajectory of the ego-vehicle and a trajectory of a traffic participant are calculated, an ego-trajectory alternative is generated by applying a lateral shift to the calculated ego-trajectory, an uncertainty area at least with respect to a lateral direction is determined for each trajectory, a spatio-temporal closeness of the uncertainty area of the ego-trajectory and the uncertainty area of the trajectory of the traffic participant is estimated for each of the plurality of ego-trajectories and an ego-trajectory is selected from the plurality of ego-trajectories based on the evaluated spatio-temporal closeness.

Abstract: The present invention provides an advanced driver assistance system for assisting a driver of a vehicle. The system comprises a sensor unit, a processing unit and a display unit. The sensor unit is configured to sense an environment of the vehicle. The processing unit is configured to determine, based on a sensing output, at least one feature of the environment. The processing unit is configured to determine, for a current time, a risk zone of the feature, by estimating, based on at least one parameter of the vehicle at the current time, at each virtual position of two or more virtual positions of the vehicle a respective risk with regard to the feature; and by forming the risk zone based on the two or more risks. The display unit is configured to display the environment of the vehicle with the feature and the risk zone of the feature.

Abstract: A method and system for assisting operation of an ego-vehicle in the traffic situation with shared traffic space is provided, in which a future path for the ego-vehicle and a future path for another traffic participant and a relevant path corridor for each determine future path is determined. Based thereon, an overlap of the path corridors to define a shared traffic space is determined and individual distance measures between the ego-vehicle and the shared traffic space and between the other traffic participant and the shared traffic space are calculated to determine an asymmetry. Based on the asymmetry an order is determined and (partially) automated in accordance with trajectory determined based on the order, or information on the intended passing order is provided to the ego-vehicle driver.

Abstract: The present invention relates to a method for use in a driver assistance system of an ego-vehicle. The method supports driving of the ego-vehicle and comprises the steps of retrieving a priority relationship between the ego-vehicle and at least one traffic participant involved in a traffic situation; selecting a prediction model for the at least one traffic participant depending on the priority relationship; predicting at last one hypothetical future trajectory for the ego-vehicle and, based on the selected prediction model, at last one hypothetical future trajectory for the at least one traffic participant; and calculating a behavior relevant score for ego-vehicle based on the calculated hypothetical future trajectories.

Abstract: The present invention relates to a method for use in a driver assistance system of an ego-vehicle. The method supports driving of the ego-vehicle and comprises the steps of retrieving a priority relationship between the ego-vehicle and at least one traffic participant involved in a traffic situation; selecting a prediction model for the at least one traffic participant depending on the priority relationship; predicting at last one hypothetical future trajectory for the ego-vehicle and, based on the selected prediction model, at last one hypothetical future trajectory for the at least one traffic participant; and calculating a behavior relevant score for ego-vehicle based on the calculated hypothetical future trajectories.

Abstract: An automotive driver assistance for a vehicle with an improved capability to handle obstructed sensor coverage includes steps of acquiring sensor data on an environment of the vehicle from at least one sensor, of generating an environment representation based on the acquired sensor data and of predicting at least one behavior of the vehicle. The method determines at least one area in the environment of the vehicle wherein for the at least one area either a confidence for the sensor data is below a threshold or no sensor data is available and generates at least one virtual traffic entity in the at least one determined area, wherein the virtual traffic entity is adapted to interact with the at least one predicted behavior of the vehicle. A risk measure for each combination of the at least one virtual traffic entity and the predicted behavior of the vehicle is estimated, the calculated risk measure is evaluated and a controlling action for the vehicle is executed based on the evaluated risk measure.

Abstract: An automotive driver assistance for a vehicle with an improved capability to handle obstructed sensor coverage includes steps of acquiring sensor data on an environment of the vehicle from at least one sensor, of generating an environment representation based on the acquired sensor data and of predicting at least one behavior of the vehicle. The method determines at least one area in the environment of the vehicle wherein for the at least one area either a confidence for the sensor data is below a threshold or no sensor data is available and generates at least one virtual traffic entity in the at least one determined area, wherein the virtual traffic entity is adapted to interact with the at least one predicted behavior of the vehicle. A risk measure for each combination of the at least one virtual traffic entity and the predicted behavior of the vehicle is estimated, the calculated risk measure is evaluated and a controlling action for the vehicle is executed based on the evaluated risk measure.