Aspects of the disclosure provide for controlling an autonomous vehicle. For instance, a trajectory for the autonomous vehicle to traverse in order to follow a route to a destination may be generated. A first error value for a boundary of an object, a second error value for a location of the autonomous vehicle, a third error value for a predicted future location of the object may be received. An uncertainty value for the object may be determined by combining the first error value, the second error value, and the third error value. A lateral gap threshold for the object may be determined based on the uncertainty value. The autonomous vehicle may be controlled in an autonomous driving mode based on the lateral gap threshold for the object.

Embodiments of the disclosure provide for improved object pathing. The improved object pathing is provided based at least in part on continuous, real-time sensor data transmitted over a high-throughput communications network that enables updating of object in real-time as changes to an environment are detected from high-fidelity, continuous, real-time sensor data. Some embodiments are configured for receiving, in real-time via a high-throughput communications network, a continuous set of sensor data associated with one or more real-time sensors, determining current location data associated with at least one of a set of travelling objects within an environment, identifying current pathing data associated with each travelling object, generating optimized pathing data for the at least one travelling object based on the current pathing data and the current location data associated with each travelling object, and outputting the optimized pathing data to a computing device associated with the at least one travelling object.

Embodiments for operational envelope detection (OED) with situational assessment are disclosed. Embodiments herein relate to an operational envelope detector that is configured to receive, as inputs, information related to sensors of the system and information related to operational design domain (ODD) requirements. The OED then compares the information related to sensors of the system to the information related to the ODD requirements, and identifies whether the system is operating within its ODD or whether a remedial action is appropriate to adjust the ODD requirements based on the current sensor information. Other embodiments are described and/or claimed.

Various embodiments for systems and methods for cooperative driving of connected autonomous vehicles using responsibility-sensitive safety (RSS) rules are disclosed herein. The CAV system integrates proposed RSS rules with CAV's motion planning algorithm to enable cooperative driving of CAVs. The CAV system further integrates a deadlock detection and resolution system for resolving traffic deadlocks between CAVs. The CAV system reduces redundant calculation of dependency graphs.

A system and method for future forecasting using action priors that include receiving image data associated with a surrounding environment of an ego vehicle and dynamic data associated with dynamic operation of the ego vehicle. The system and method also include analyzing the image data to classify dynamic objects as agents and to detect and annotate actions that are completed by the agents that are located within the surrounding environment of the ego vehicle and analyzing the dynamic data to process an ego motion history that is associated with the ego vehicle that includes vehicle dynamic parameters during a predetermined period of time. The system and method further include predicting future trajectories of the agents located within the surrounding environment of the ego vehicle and a future ego motion of the ego vehicle within the surrounding environment of the ego vehicle based on the annotated actions.

An autonomous driving apparatus and method for an ego vehicle that autonomously travels includes a first sensor to detect a vehicle nearby the ego vehicle, a memory to store map information, and a processor to control autonomous driving of the ego vehicle based on the nearby vehicle detected by the first sensor and the map information stored in the memory.

An embodiment sensor information fusion method includes estimating, by a host vehicle, reliability of shared sensor fusion information received from a neighboring vehicle, the shared sensor fusion information being generated by the neighboring vehicle, and generating, based on the estimated reliability, fusion track information of an object located near the host vehicle or the neighboring vehicle using host vehicle sensor fusion information generated by the host vehicle and the shared sensor fusion information.

Provided are autonomous vehicles (AV), computer program products, and methods for maneuvering an AV in a roadway, including receiving forecast information associated with predicted trajectories of one or more actors in a roadway, determining a relevant trajectory of an actor based on correlating a forecast for predicted trajectories of the actor with the trajectory of the AV, regenerate a distance table for the relevant trajectory previously generated for processing constraints, generate a plurality of margins for the AV to evaluate, the margins based on a plurality of margin types for providing information about risks and effects on passenger comfort associated with a future proximity of the AV to the actor, classifying an interaction between the AV and the actor based on a plurality of margins, and generating continuous scores for each candidate trajectory that is also within the margin of the actor generated for the relevant trajectory.

Systems and methods for motion planning by a vehicle computing system of an autonomous vehicle are provided. The vehicle computing system can input sensor data to a machine-learned system including one or more machine-learned models. The computing system can obtain, as an output of the machine-learned model(s), motion prediction(s) associated with object(s) detected by the system. The system can convert a shape of the object(s) into a probability of occupancy by convolving an occupied area of the object(s) with a continuous uncertainty associated with the object(s). The system can determine a probability of future occupancy of a plurality of locations in the environment at future times based at least in part on the motion prediction(s) and the probability of occupancy of the object(s). The system can provide the motion prediction(s) and the probability of future occupancy of the plurality of locations to a motion planning system of the autonomous vehicle.

The technology relates to predicting that an object is going to enter into a trajectory of a vehicle. This may include receiving sensor data identifying a first location of the object in an environment of the vehicle at a first point in time and receiving sensor data identifying a second location of the object in the environment at a second point in time. In addition, a boundary of the trajectory is determined by defining at least a two-dimensional area through which the vehicle is expected to travel in the future. A first distance between the boundary and the first location and a second distance between the trajectory and the second location are determined. The first distance and the second distance are used to determine that the object is going to enter into the trajectory at a future point in time.