Systems and methods are provided for navigating a host vehicle. At least one processing device may be programmed to receive an image representative of an environment of the host vehicle; determine a planned navigational action for the host vehicle; analyze the image to identify a target vehicle in the environment of the host vehicle; determine a next-state lateral distance between the host vehicle and the target vehicle that would result if the planned navigational action was taken; determine a lateral braking distance for the host vehicle and the target vehicle based on a maximum yaw rate capability, a maximum change in turn radius capability, and a current lateral speed of the host vehicle and the target vehicle; and implement the planned navigational action if the determined next-state distance is greater than a sum of the lateral braking distances for the host vehicle and the target vehicle.

Centralized shared scenario-specific operational control management includes receiving, at a centralized shared scenario-specific operational control management device, shared scenario-specific operational control management input data, from an autonomous vehicle, validating the shared scenario-specific operational control management input data, identifying a current distinct vehicle operational scenario based on the shared scenario-specific operational control management input data, generating shared scenario-specific operational control management output data based on the current distinct vehicle operational scenario, and transmitting the shared scenario-specific operational control management output data.

A method and an apparatus for determining a vehicle speed computing a probability distribution of action intentions based on observation information of a surrounding object. Then, a probability redistribution of the different action intentions is computed based on travel times for the vehicle to travel from a current position to risk areas corresponding to the different action intentions, motion status variations of the surrounding object with the different action intentions are predicted based on the travel times for the vehicle to travel to the risk areas corresponding to the different action intentions. Finally, the travelling speed of the vehicle is determined based on the probability redistribution of the different action intentions, the motion status variations of the surrounding object with the different action intentions, and motion status variations of the vehicle under different travelling speed control actions.

Techniques for determining to modify a trajectory based on an object are discussed herein. A vehicle can determine a drivable area of an environment, capture sensor data representing an object in the environment, and perform a spot check to determine whether or not to modify a trajectory. Such a spot check may include processing to incorporate an actual or predicted extent of the object into the drivable area to modify the drivable area. A distance between a reference trajectory and the object can be determined at discrete points along the reference trajectory, and based on a cost, distance, or intersection associated with the trajectory and the modified area, the vehicle can modify its trajectory. One trajectory modification includes following, which may include varying a longitudinal control of the vehicle, for example, to maintain a relative distance and velocity between the vehicle and the object.

The present invention extends to methods, systems, and computer program products for using geofences to restrict vehicle operation. Aspects of the invention include creating dynamic geofences and limiting vehicle movements (e.g., speed, acceleration, steering, etc.) within and in the vicinity of the dynamic geofences to protect pedestrians from physical harm. In general, radio devices track people in an area by counting the number of devices and calculating the number of people based on average number of devices per person. Using count and location data, a geofence is created when population or population density within an area exceeds a threshold. The geofence can be sent to vehicles to restrict vehicle operation, for example, slowing down or stopping the vehicle, within and around the geofence.

Methods and devices for controlling operation of a Self-Driving Car (SDC) are disclosed. The method includes, at a first moment in time during an approach of the SDC to a crosswalk: identifying, an object-inclusion zone in proximity to the crosswalk, determining presence of an object in the object-inclusion zone, determining an interval of time for the object based on movement data of the object, using the interval of time and movement data of the SDC for determining operation-control data for controlling operation of the SDC, and assigning decision data to the object-inclusion zone indicative of a decision.

In one embodiment, a system receives a captured image perceiving an environment of an ADV from an image capturing device of the ADV, where the captured image identifies an obstacle in motion near the ADV. The system generates a feasible area surrounding the moving obstacle based on a projection of the moving obstacle. If the ADV is within the feasible area, the system determines an upper bound velocity limit for the ADV. The system generates a trajectory having a trajectory velocity less than the upper bound velocity limit to control the ADV autonomously according to the trajectory such that if the ADV is within the feasible area the ADV is to decelerate.

According to various embodiments, systems and methods are provided for use by an ADV to nudge incoming objects in a self-reverse lane. In an embodiment, an ADV determines that a self-reverse lane has a predetermined width before entering the self-reverse lane. After entering the self-reverse lane, the ADV can follow a first reference line at the center of the self-reverse lane. In response to detecting an incoming object, the ADV creates an alternative lane in the self-reverse lane by temporarily modifying a high definition map. The ADV subsequently follows a second reference line in the alternative lane to nudge the incoming object. In response to detecting that the incoming object has passed and the self-reverse lane is clear, the ADV can drive back to the center of the self-reverse lane, to continue to follow the first reference line in the self-reverse lane.

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.

An advanced pedestrian warning or alert system is a system for automotive vehicles is described herein in various embodiments. This system detects if a vulnerable road user, for example a pedestrian, is at an unsafe distance from the moving vehicle. If a pedestrian is too close to the vehicle when the vehicle is in motion, then the system will issue auditory and/or visual warnings to notify the pedestrian back to safety.