Abstract: A vehicle trajectory may be determined by sampling sequences of vehicle controls based on a set of control distributions. The control distributions may then be updated based on a set of trajectory-related costs associated with the sampled trajectories. The sampled trajectories may be used to determine a trajectory for the vehicle. The determined trajectory may be used to control the vehicle and/or the vehicle's driving behavior.

Abstract: Techniques are discussed for generating and optimizing trajectories for autonomous vehicles using route-relative numerical integration relative to a reference path. A planner component of an autonomous vehicle, for example, can receive or generate a reference path corresponding to a route through an environment. The current vehicle state, sideslip, and vehicle dynamics can be represented in a system of equations, such that the planner component can substantially simultaneously solve for subsequent trajectory states in a single convergence operation. In various examples, the subsequent trajectory states can be used to evaluate and determine candidate trajectories to control the autonomous vehicle to traverse the environment.

Abstract: Techniques for accurately predicting and avoiding collisions with objects detected in an environment of a vehicle are discussed herein. A vehicle computing device can implement a model to generate action references usable in a tree search to control the vehicle. For example, the model may represent one or more trajectory generators that modify a baseline trajectory in different ways to output a set of actions trajectories that are usable to define a tree structure. The set of action trajectories can be used by the vehicle computing device for predicting vehicle actions by the vehicle computing device to control the vehicle.

Abstract: Systems and techniques for determining a sideslip vector for a vehicle that may have a direction that is different from that of a heading vector for the vehicle. The sideslip vector in a current vehicle state and sideslip vectors in predicted vehicles states may be used to determine paths for a vehicle through an environment and trajectories for controlling the vehicle through the environment. The sideslip vector may be based on a vehicle position that is the center point of the wheelbase of the vehicle and may include lateral velocity, facilitating the control of four-wheel steered vehicle while maintaining the ability to control two-wheel steered vehicles.

Abstract: Techniques for accurately predicting and avoiding collisions with objects detected in an environment of a vehicle are discussed herein. A vehicle computing device can implement a model to generate action references usable in a tree search to control the vehicle. For example, the model may represent one or more trajectory generators that modify a baseline trajectory in different ways to output a set of actions trajectories that are usable to define a tree structure. The set of action trajectories can be used by the vehicle computing device for predicting vehicle actions by the vehicle computing device to control the vehicle.

Abstract: Various aspects of the disclosure generally relate to a system for securing a bicycle on a truck bed. In one aspect, a modular and convertible truck bed system may serve to carry or mount a bicycle and serve as a base for truck bed cover, overlanding rack, and other truck bed accessories.

Abstract: The techniques discussed herein may comprise an autonomous vehicle guidance system that generates a group of candidate trajectories based at least in part on sensor data. This group of candidate trajectories is clustered into two or more clusters and one or more representative trajectories may be determined for each cluster. The representative trajectory may be one of the trajectories in the cluster or a separately determined trajectory that represents the trajectories in a cluster, such as a mean or median trajectory. The representative trajectory may be used to predict how a dynamic object would react to the representative trajectory. This prediction may be used to determine potentially different costs associated with the different trajectories of the cluster with which the representative trajectory is associated. These costs may include costs to induce following behavior by the autonomous vehicle and/or cost(s) associated with conditionally available roadway portions.

Abstract: Techniques are discussed for generating and optimizing trajectories for autonomous vehicles using route-relative numerical integration relative to a reference path. A planner component of an autonomous vehicle, for example, can receive or generate a reference path corresponding to a route through an environment. The current vehicle state, sideslip, and vehicle dynamics can be represented in a system of equations, such that the planner component can substantially simultaneously solve for subsequent trajectory states in a single convergence operation. In various examples, the subsequent trajectory states can be used to evaluate and determine candidate trajectories to control the autonomous vehicle to traverse the environment.

Abstract: Systems and techniques for determining a sideslip vector for a vehicle that may have a direction that is different from that of a heading vector for the vehicle. The sideslip vector in a current vehicle state and sideslip vectors in predicted vehicles states may be used to determine paths for a vehicle through an environment and trajectories for controlling the vehicle through the environment. The sideslip vector may be based on a vehicle position that is the center point of the wheelbase of the vehicle and may include lateral velocity, facilitating the control of four-wheel steered vehicle while maintaining the ability to control two-wheel steered vehicles.

Abstract: Techniques are described herein for generating trajectories for autonomous vehicles using velocity-based steering limits. A planning component of an autonomous vehicle can receive steering limits determined based on safety requirements and/or kinematic models of the vehicle. Discontinuous and discrete steering limit values may be converted into a continuous steering limit function for use during on-vehicle trajectory generation and/or optimization operations. When the vehicle is traversing a driving environment, the planning component may use steering limit functions to determine a set of situation-specific steering limits associated with the particular vehicle state and/or driving conditions. The planning component may execute loss functions, including steering angle and/or steering rate costs, to determine a vehicle trajectory based on the steering limits applicable to the current vehicle state.

Abstract: Techniques are described herein for generating trajectories for autonomous vehicles using velocity-based steering limits. A planning component of an autonomous vehicle can receive steering limits determined based on safety requirements and/or kinematic models of the vehicle. Discontinuous and discrete steering limit values may be converted into a continuous steering limit function for use during on-vehicle trajectory generation and/or optimization operations. When the vehicle is traversing a driving environment, the planning component may use steering limit functions to determine a set of situation-specific steering limits associated with the particular vehicle state and/or driving conditions. The planning component may execute loss functions, including steering angle and/or steering rate costs, to determine a vehicle trajectory based on the steering limits applicable to the current vehicle state.