Abstract: Techniques described herein involve a determination of a set of vectors associated with a vector field that is indicative of a traffic direction in the environment. Data associated with an environment in a vicinity of a vehicle is received. Based on the data, a set of vectors associated with a vector field that is indicative of a traffic direction in the environment is determined. The vectors in the set of vectors are indicative of the traffic direction at respective positions in the environment. Instructions are generated to control the vehicle based at least in part on the set of vectors.

Abstract: Techniques for generating a driving surface cost landscape for determining costs for vehicle positions in an environment are described herein. A planning component within a vehicle may determine a non-preferred surface associated with a type of non-preferred area of an environment along a route of a vehicle, determine a preferred surface associated with a preferred area of the environment, determine an adjusted non-preferred surface by removing an overlapping area of the non-preferred surface that overlaps the preferred surface and determine a cost associated with a vehicle position based at least on the preferred surface and the adjusted non-preferred surface. The planning component may then determine a control trajectory for the autonomous vehicle based at least in part on the cost associated with the vehicle position.

Abstract: Techniques for generating a driving surface cost landscape for determining costs for vehicle positions in an environment are described herein. A planning component within a vehicle may determine a non-preferred surface associated with a type of non-preferred area of an environment along a route of a vehicle, determine a preferred surface associated with a preferred area of the environment, determine an adjusted non-preferred surface by removing an overlapping area of the non-preferred surface that overlaps the preferred surface and determine a cost associated with a vehicle position based at least on the preferred surface and the adjusted non-preferred surface. The planning component may then determine a control trajectory for the autonomous vehicle based at least in part on the cost associated with the vehicle position.

Abstract: Techniques associated with detecting non-deterministic behavior with a component and/or subcomponents of an autonomous vehicle are discussed herein. In some cases, a simulation system may be configured to simulate operations of the autonomous vehicle and to detect changes in behavior between instances and with respect to log data or expected results. The simulation system may flag or otherwise identify components and/or subcomponents responsive to detecting potentially non-deterministic behavior.

Abstract: Techniques associated with detecting non-deterministic behavior with a component and/or subcomponents of an autonomous vehicle are discussed herein. In some cases, a simulation system may be configured to simulate operations of the autonomous vehicle and to detect changes in behavior between instances and with respect to log data or expected results. The simulation system may flag or otherwise identify components and/or subcomponents responsive to detecting potentially non-deterministic behavior.