A behavior prediction system predicts human behaviors based on environment-aware information such as camera movement data and geospatial data. The system receives sensor data of a vehicle reflecting a state of the vehicle at a given time and a given location. The system determines a field of concern in images of a video stream and determines one or more portions of images of the video stream that correspond to the field of concern. The system may apply different levels of processing powers to objects in the images based on whether an object is in the field of concern. The system then generates features of objects and identify VRUs from the objects of the video stream. For the identified VRUs, the system inputs a representation of the VRUs and the features into a machine learning model, and outputs from the machine learning model a behavioral risk assessment of the VRUs.

In one aspect, an operating method of an intelligence vehicle driving control system is provided that comprises: a collecting step of collecting big data including a wheel torque and a speed for every vehicle type and traffic information; a torque calculating step of learning the big data using a predetermined machine learning model and inputs a specific desired speed profile to the machine learning model to calculate a motor torque of a driving vehicle; and an optimal speed profile deriving step of calculating an energy consumption required to generate the calculated motor torque using a predetermined dynamic programming method and a reverse vehicle dynamic model and deriving an optimal speed profile in which the energy consumption is minimized.

A device may receive emergency data, traffic data, network performance data, crime data, and gunshot data associated with a geographical area and may identify a location within the geographical area based on the emergency data, the traffic data, the network performance data, the crime data, and the gunshot data. The device may determine, based on the emergency data, the traffic data, the network performance data, the crime data, and the gunshot data for the location, a risk level for the location and may identify an autonomous vehicle based on the risk level, the traffic data, and the network performance data for the location. The device may determine a route for the autonomous vehicle to the location based on the traffic data and the network performance data for the location, and may perform actions based on the autonomous vehicle and the route.

The offline map generation process may collect multiple point cloud data of the same area. A perception algorithm may operate on the point cloud data to detect static objects, which may be fixed road features that do not change among the point cloud data, allowing the perception algorithm to more accurately detect the static objects. During online operation of the ADV through the area, the ADV may trim regions-of-interest (ROI) of the area to exclude the predefined static objects. The perception algorithm may execute the sensor data of the ROI in real-time to detect objects in the ROI. The may be added back to the output of the perception algorithm to complete the perception output.

Provided are methods for vehicle scenario mining for machine learning methods, which can include determining a set of attributes associated with an untested scenario for which a machine learning model of an autonomous vehicle is to make planned movements. The method includes searching a scenario database for the untested scenario based on the set of attributes. The scenario database includes a plurality of datasets representative of data received from an autonomous vehicle sensor system in which the plurality of datasets is marked with at least one attribute of the set of attributes. The method further includes obtaining the untested scenario from the scenario database for inputting into the machine learning model for training the machine learning model. The machine learning model is configured to make the planned movements for the autonomous vehicle. Systems and computer program products are also provided.

A vehicle may transmit, to a network entity, a lane use request message associated with a lane for the vehicle. The network entity may identify lane information associated with a lane for a vehicle. The network entity may identify vehicle information associated with the vehicle. The network entity may transmit, to the vehicle, and the vehicle may receive, from the network entity, a lane use grant message based on at least one of the identified lane information or the identified vehicle information. The lane use grant message may be indicative of a permission for the vehicle to use the lane. The vehicle may not be permitted to use the lane without the permission. The lane may correspond to a flexible direction lane, an emergency lane, a road shoulder, an HOV lane, or a passing lane.

A mobile object control device according to an embodiment includes a recognizer configured to recognize a surroundings situation of a mobile object, a contact likelihood determiner configured to change a condition for determining that there is a likelihood that the mobile object and the object will come into contact with each other in the future on the basis of a state of an object around the mobile object recognized by the recognizer, and determine the likelihood that the mobile object and the object will come into contact with each other in the future on the basis of the changed condition.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for planning a trajectory of a vehicle. One of the methods includes obtaining input data for planning a driving trajectory for a vehicle, the input data comprising an intended route for the vehicle and data characterizing an environment in a vicinity of the vehicle; processing the input data using an input encoder neural network to generate feature data that includes a respective feature representation for each of a plurality of locations in the environment; applying spatial attention to the feature representations to generate a respective attention weight for each of the plurality of locations; generating a respective attended feature representation for each of the plurality of locations; generating a bottlenecked representation of the attended feature representations; and generating a planned future trajectory from at least the bottlenecked representation.

Systems and methods for perceiving a field around a mobile device include a sensor system has a distance sensor arranged on a mobile device. The sensor system captures distance measurements of a field of view. The distance measurements are captured at a unique set of angular scan positions per revolution of the distance sensor over a sequence of scan rotations. A perception system generates a three-dimensional point cloud representation of the field of view based on the distance measurements for each scan rotation in the sequence of scan rotations. The perception system generates a composite three-dimensional depth map of the field of view by compiling each of the three-dimensional point cloud representations for the sequence of scan rotations. Each of the three-dimensional point cloud representations has a resolution that is lower than a resolution of the composite three-dimensional depth map of the field of view.

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.