Embodiments of a system/method is disclosed to operate an autonomous driving vehicle (ADV). In one embodiment, a system perceives a driving environment surrounding the ADV using a plurality of sensors mounted on the ADV including one or more obstacles. The system receives traffic signal information from one or more traffic indicators identified within a predetermined radius of the ADV. For each of the one or more obstacles, the system determines if the obstacle is situated on a lane with traffic flow coordinated by the one or more traffic indicators. The system predicts a behavior of the obstacle based on the traffic signal information for the lane. The system plans a trajectory based on the predicted behaviors for the one or more obstacles to control the ADV based on the planned trajectory.

The technology relates to route planning and performing driving operations in autonomous vehicles, such as cargo trucks, articulating buses, as well as other vehicles. A detailed kinematic model of the vehicle in evaluated in conjunction with roadgraph and other information to determine whether a route or driving operation is feasible for the vehicle. This can include evaluating a hierarchical set of driving rules and whether current driving conditions impact any of the rules. Driving trajectories and cost can be evaluated when pre-planning a route for the vehicle to follow. This can include determining an ideal trajectory for the vehicle to take a particular driving action. Pre-planned routes may be shared with a fleet of vehicles, and can be modified based on information obtained by different vehicles of the fleet.

Systems and methods are provided for navigating a host vehicle. At least one processing device may be programmed to receive an image of an environment of the host vehicle; detect, based on analysis of the image, a pedestrian crosswalk in the image; detect a presence of a traffic light and determine whether the traffic light is relevant to the host vehicle and the pedestrian crosswalk; determine a state of the traffic light; determine, when a pedestrian appears in the image, a proximity of the pedestrian relative to the pedestrian crosswalk; determine a planned navigational action for navigating the host vehicle relative to the pedestrian crosswalk based on a driving policy, the state of the traffic light and the proximity of the pedestrian relative to the pedestrian crosswalk; and cause one or more actuator systems of the host vehicle to implement the planned navigational action.

A computing device configured to: obtain images representative of an environment of a host vehicle, the host vehicle traveling on a roadway; detect, from the images, a mark located on the roadway; identify, from the images, points corresponding to the mark on the roadway; identify the mark as a type of roadway marking, corresponding to the identified points, the type of roadway marking selected from multiple types of roadway markings; determine a position of the mark on the roadway relative to the host vehicle, using the identified points corresponding to the mark; and determine a trajectory to navigate the host vehicle on the roadway, based on the position of the mark within the roadway and the type of roadway marking.

Systems and methods are provided for vehicle navigation. In one implementation, a system may comprise an interface to obtain sensing data of an environment of the host vehicle. A processing device may be configured to determine a planned navigational action for the host vehicle; identify a target vehicle in the environment of the host vehicle; predict a resulting distance between the host and target vehicles if the planned action were taken; determine a host vehicle stopping distance based on a braking rate, maximum acceleration capability, and current speed of the host vehicle; determine a target vehicle stopping distance based on a braking rate and current speed of the target vehicle; and continue with the planned navigational action while the predicted distance is greater than a minimum safe longitudinal distance calculated based on the host vehicle stopping distance and the target vehicle stopping distance.

A system can analyze a live sensor view of a self-driving vehicle (SDV) in accordance with a safety threshold to detect objects of interest along a route and classify each detected object of interest. When the safety threshold is not met, the system can transmit a teleassistance inquiry using LIDAR data to a backend computing system. When a certainty threshold is not met for an object of interest, the system can transmit a different teleassistance inquiry using image data to the backend computing system.

A vehicle control device includes a peripheral recognition unit configured to recognize a peripheral status of a vehicle including a position of a traffic participant present in a periphery of the vehicle on the basis of an output of an in-vehicle device, an estimation unit configured to estimate a peripheral attention ability of the traffic participant on the basis of an output of the in-vehicle device, and a risk area setting unit configured to set a risk area of the traffic participant on the basis of a result of the estimation performed by the estimation unit.

A vehicle control device includes a recognizer configured to recognize a surrounding environment of a vehicle, a setter configured to set a first risk area in a surrounding area of the vehicle on the basis of a recognition result of the recognizer, and a controller configured to control at least one of a speed and steering of the vehicle. The setter sets the first risk area so that the first risk area includes an area between the moving object and a first end of a crosswalk where the moving object is scheduled to arrive in the crosswalk when the moving object is entering the crosswalk which is provided in front of the vehicle and where the vehicle is scheduled to pass on the basis of the recognition result of the recognizer. The controller prevents the vehicle from entering the first risk area when a first predetermined condition is satisfied.

A method of setting a driving route of an autonomous vehicle (AV) providing an advertisement on a road obtains information related to an advertisee's reaction to the advertisement, sets an order of priority for lanes in which the AV is drivable depending on a reference and based on road context information and a current lane of the AV, and determines a driving lane and driving route of the AV with a driving lane set based on the order of priority. The method determines a degree of reaction of the advertisee to the advertisement and sets the driving route and driving lane based on the advertisee's degree of reaction for efficient advertisement. The method can be associated with artificial intelligence modules, drones (unmanned aerial vehicles (UAVs)), robots, augmented reality (AR) devices, virtual reality (VR) devices, devices related to 5G service, etc.

A system for mapping road segment free spaces for use in autonomous vehicle navigation. The system includes at least one processor programmed to: receive from a first vehicle one or more location identifiers associated with a lateral region of free space adjacent to a road segment; update an autonomous vehicle road navigation model for the road segment to include a mapped representation of the lateral region of free space based on the received one or more location identifiers; and distribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles.