A method for controlling mechatronic systems is described herein. In accordance with one embodiment the method includes planning a nominal path for a mechatronic system using an automatic path planner, receiving information concerning one or more objects detected in the surrounding environment of the mechatronic system and calculating one or more occupancy sets corresponding to the one or more detected objects, and detecting whether the nominal path violates at least one of the one or more Occupancy Sets. In one embodiment, the occupancy sets may represent theoretic system states of the mechatronic system which are potentially occupied by the stationary and dynamic objects at a specific time. Furthermore, a corresponding control system is described.

A method for detecting a standstill of a vehicle includes detecting the standstill of the vehicle using at least one sensor, and, when the standstill is detected using the at least one sensor, carrying out a test routine for checking the standstill of the vehicle. The test routine uses at least one signal from at least one rate-of-rotation sensor as an input value. The method further includes rejecting the detected standstill of the vehicle when the at least one signal from the at least one rate-of-rotation sensor indicates that there is no standstill of the vehicle.

A system for an attention-based perception includes a camera device configured to provide an image of an operating environment of a vehicle. The system further includes a computerized device monitoring the image, analyzing sensor data to identify a feature in the image as corresponding to an object in the operating environment and assign a score for the feature based upon an identification, a location, or a behavior of the object. The computerized device is further operable to define candidate regions of interest upon the image, correlate the score for the feature to the candidate regions of interest to accrue a total region score, select some of the candidate regions for analysis based upon the total region scores, and analyze the portion of the candidate regions to generate a path of travel output. The system further includes a device controlling the vehicle based upon the output.

There is provided a method for preventing car collision, the method comprising: obtaining information about a vehicle stopped in front of a preceding vehicle acquired by the preceding vehicle; calculating a deceleration amount for preventing a collision with the stopped vehicle based on the information about the stopped vehicle; predicting a preset event related to the preceding vehicle based on a change in the position of the preceding vehicle; and controlling an autonomous driving vehicle based on the deceleration amount according to a prediction result.

A model generation method includes specifying image coordinates that fall within a two-dimensional image obtained by capturing at least a mobile object and that correspond to at least one point among vertexes of a rectangular shape formed when an outer shape of the mobile object viewed from above is projected on a road, as a key point of the mobile object, and creating the two-dimensional image, to which information on the key point is added, as training data, and generating a machine learning model that outputs the key point from a two-dimensional image obtained by capturing at least a mobile object, by performing machine learning using the training data.

An information processing apparatus includes: a point group data acquisition unit configured to acquire, based on information from a sensor configured to detect an object existing in surroundings of a vehicle, point group data related to a plurality of points representing the object; a movement amount estimation unit configured to estimate a movement amount of the vehicle; a storage unit configured to store, as a point group map recorded in association with position information including a latitude and a longitude, relative positions of the plurality of points relative to a first reference position that is a place on a travel path of the vehicle; and a position estimation unit configured to estimate a position of the vehicle based on the point group map, the point group data, and the movement amount.

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 vehicle drive assist apparatus for avoiding collision of a vehicle with a recognized object recognizes a surrounding environment around the vehicle; acquires feature information of a three-dimensional object in the surrounding environment; sets a traveling path of the vehicle based on the surrounding environment; recognizes an aerial object based on the feature information; identify a type of the aerial object based on the feature information; determines whether the aerial object has a possibility of hindering traveling of the vehicle; performs steering control based on a control signal; continues normal traveling control when the aerial object does not have the hindrance possibility; estimates a falling point of the aerial object when the aerial object has the hindrance possibility; when the falling point is on the traveling path of the vehicle, sets a new traveling path to steer around the falling point and executes traveling control along the new traveling path.

A surrounding environment recognition device recognizes a surrounding environment around a vehicle. A traveling control unit centrally controls a whole of the entire vehicle. An obstacle presumer presumes presence of an obstacle ahead of the vehicle based on a behavior distribution of preceding vehicles recognized by the surrounding environment recognition device, and estimates a position and an area where the obstacle is present when the presence of the obstacle is presumed. A traveling path calculator calculates candidates for traveling path areas along which the vehicle is expected to travel while avoiding collision with the presumed obstacle. A traveling path selector selects a traveling path area from among the calculated traveling path areas and sets the selected traveling path area. The traveling control unit controls the vehicle to travel along the set traveling path area.

Aspects of the disclosure relate to methods for controlling a vehicle having an autonomous driving mode. For instance, sensor data may be received from one or more sensors of the perception system of the vehicle, the sensor data identifying characteristics of an object perceived by the perception system. When it is determined that the object is no longer being perceived by the one or more sensors of the perception system, predicted characteristics for the object may be generated based on one or more of the identified characteristics. The predicted characteristics of the object may be used to control the vehicle in the autonomous driving mode such that the vehicle is able to respond to the object when it is determined that the object is no longer being perceived by the one or more sensors of the perception system.