A vehicle traveling control apparatus includes a first recognition unit recognizing a surrounding environment based on first surrounding environment information, a transceiver, a second recognition unit recognizing the surrounding environment based on second surrounding environment information, and a traveling control unit stopping a first vehicle when there is a possibility of contact between a first vehicle and a mobile object. While the first vehicle is traveling at low speed, the transceiver sends the first surrounding environment information and receives the second surrounding environment information to/from a second vehicle, and the second recognition unit recognizes that a mobile object is present in a blind spot or that the second vehicle is in a transition state based on the second surrounding environment information. The traveling control unit executes deceleration control and lowers an operation starting threshold when there is the possibility of contact between the first vehicle and the mobile object or the second vehicle.

A host vehicle computer is programmed to receive intersection arrival data via vehicle-to-vehicle communications from one or more secondary vehicles. The computer assigns priority to each of the secondary vehicles based on the intersection arrival data and identifies one of the secondary vehicles as an immediately preceding vehicle. Upon receiving a ready signal clearance from the immediately preceding vehicle, the computer actuates a powertrain of the host vehicle, causing the host vehicle to proceed into, and through, the intersection.

Embodiments of the present disclosure provide a method and a device for assisting in controlling automatic driving of a vehicle, a medium, and a system. The method for assisting in controlling automatic driving of a vehicle may include: acquiring sensing information related to environment collected by a sensor, the sensor being disposed in the environment and independent of the vehicle; determining an environment sensing result related to the environment by processing the acquired sensing information, the environment sensing result indicating relevant information about a plurality of objects including the vehicle in the environment; and providing the environment sensing result to a vehicle-side control device associated with the vehicle for assisting in controlling a driving behavior of the vehicle.

In techniques disclosed herein, machine learning models can be utilized in the control of autonomous vehicle(s), where the machine learning models are trained using automatically generated training instances. In some such implementations, a label corresponding to an object in a labeled instance of training data can be mapped to the corresponding instance of unlabeled training data. For example, an instance of sensor data can be captured using one or more sensors of a first sensor suite of a first vehicle can be labeled. The label(s) can be mapped to an instance of data captured using one or more sensors of a second sensor suite of a second vehicle.

Autonomous vehicles must accommodate various road configurations such as straight roads, curved roads, controlled intersections, uncontrolled intersections, and many others. Autonomous driving systems must make decisions about the speed and distance of traffic and about obstacles including obstacles that obstruct the view of the autonomous vehicle's sensors. For example, at intersections, the autonomous driving system must identify vehicles in the path of the autonomous vehicle or potentially in the path based on a planned path, estimate the distance to those vehicles, and estimate the speeds of those vehicles. Then, based on those and the road configuration and environmental conditions, the autonomous driving system must decide whether it is safe to proceed along the planned path or not, and when it is safe to proceed.

Behavior information input unit (54) receives stop-behavior information about vehicle (100) from automatic-driving control device (30). Image-and-sound output unit (51) outputs inquiry information for inquiring of an occupant whether a possibility of collision between an obstacle and vehicle (100) is to be excluded from a determination object in automatic-driving control device (30) to notification device (2), when a distance from one point on a predictive movement route of the obstacle to the obstacle is greater than or equal to a first threshold, and a speed of the obstacle is less than or equal to a second threshold. Operation signal input unit (50) receives a response signal for excluding the collision possibility from the determination object. Command output unit (55) outputs a command to exclude the collision possibility from the determination object to automatic-driving control device (30).

Systems and methods for predicting object motion and controlling autonomous vehicles are provided. In one example embodiment, a computer implemented method includes obtaining state data indicative of at least a current or a past state of an object that is within a surrounding environment of an autonomous vehicle. The method includes obtaining data associated with a geographic area in which the object is located. The method includes generating a combined data set associated with the object based at least in part on a fusion of the state data and the data associated with the geographic area in which the object is located. The method includes obtaining data indicative of a machine-learned model. The method includes inputting the combined data set into the machine-learned model. The method includes receiving an output from the machine-learned model. The output can be indicative of a plurality of predicted trajectories of the object.

In one embodiment, a method, apparatus, and system for planning the trajectory of an autonomous driving vehicle (ADV) in view of an object within a buffer area in front of the ADV is disclosed. A buffer area in front of an ADV is identified. A first object of one or more objects that have entered the buffer area is identified. A first distance cost and a first relative speed cost associated with the first object are determined. A first object cost associated with the first object is determined based on a combination of the first distance cost and the first relative speed cost. A trajectory for the ADV is planned based at least in part on a cost function comprising the first object cost, where the cost function is minimized in the planning. Control signals are generated to drive the ADV based on the planned trajectory.

A method and apparatus are provided for determining one or more behavior models used by an autonomous vehicle to predict the behavior of detected objects. The autonomous vehicle may collect and record object behavior using one or more sensors. The autonomous vehicle may then communicate the recorded object behavior to a server operative to determine the behavior models. The server may determine the behavior models according to a given object classification, actions of interest performed by the object, and the object's perceived surroundings.

Systems and methods for determining object prioritization and predicting future object locations for an autonomous vehicle are provided. A method can include obtaining, by a computing system comprising one or more processors, state data descriptive of at least a current or past state of a plurality of objects that are perceived by an autonomous vehicle. The method can further include determining, by the computing system, a priority classification for each object in the plurality of objects based at least in part on the respective state data for each object. The method can further include determining, by the computing system, an order at which the computing system determines a predicted future state for each object based at least in part on the priority classification for each object and determining, by the computing system, the predicted future state for each object based at least in part on the determined order.