Abstract: Techniques are described herein for evaluating and tuning parameters for controlling the behavior of an autonomous vehicle in or near intersections controlled by traffic signals. A planning component of an autonomous vehicle may use a set of traffic signal parameters to compute trajectory costs for the vehicle associated with traversing an intersection controlled by a traffic signal in a non-permissible state (e.g., a red light). A parameter tuning system may execute driving simulations using the planning component and a candidate set of parameters to control the behavior of a simulation vehicle within a training data set of traffic signal intersections. The parameter tuning system may determine performance metrics based on the behaviors of the simulated vehicle and may execute a search algorithm to determine modified (e.g., optimal) parameter sets for controlling vehicles during real-world driving scenarios having intersections controlled by traffic signals.

Abstract: Techniques are described herein for safely transitioning a vehicle from a first trajectory to a second trajectory. In response to a safety event, for example, a vehicle may switch from being controlled in accordance with a nominal planner output (a planned trajectory) to a safety, or stopping, trajectory. In some examples, in response to the vehicle overcoming the safety event, a safe and smooth return to a nominal planning system output trajectory (an updated planned trajectory) may be determined and used to cause the vehicle to continue along to a previously determined destination.

Abstract: Techniques are discussed for generating and optimizing trajectories for controlling autonomous vehicles in performing on-route and off-route actions within a driving environment. A planning component of an autonomous vehicle can receive or generate time-discretized (or temporal) trajectories for the autonomous vehicle to traverse an environment. Trajectories can be optimized, for example, based on the lateral and longitudinal dynamics of the vehicle, using loss functions and/or costs. In some examples, the temporal optimization of a trajectory may include resampling a previous trajectory based on the differences in the time sequences of the temporal trajectories, to ensure temporal consistency of trajectories across planning cycles. Constraints also may be applied during temporal optimization in some examples, to control or restrict driving maneuvers that are not supported by the autonomous vehicle.

Abstract: Systems and techniques for determining a trajectory for use in controlling a vehicle are described. A trajectory determination system may generate a variety of trajectories for potential use in controlling a vehicle, including a maximum braking trajectory that enables the maximum application of the vehicle's brakes. A vehicle computing system may determine a distance between vehicle and an obstacle and stopping distances for the various trajectories and implement the maximum braking trajectory after determining that the distance to stop for that trajectory is the same as, but not substantially greater than, the distance between the vehicle and the obstacle.

Abstract: A tree search may comprise a cost function that may be used to determine, from multiple controller profiles, a controller profile to implement a transition between vehicle actions. A controller profile may limit the controls output by a controller sufficient to track from a first vehicle action to a second vehicle action. The controller controls may result in a trajectory achieved by the vehicle and the controller profile may limit the controls of the controller such that the trajectory is limited by a maximum jerk and/or maximum acceleration.

Abstract: Techniques are discussed for generating and optimizing trajectories for controlling autonomous vehicles in performing on-route and off-route actions within a driving environment. A planning component of an autonomous vehicle can receive or generate time-discretized (or temporal) trajectories for the autonomous vehicle to traverse an environment. Trajectories can be optimized, for example, based on the lateral and longitudinal dynamics of the vehicle, using loss functions and/or costs. In some examples, the temporal optimization of a trajectory may include resampling a previous trajectory based on the differences in the time sequences of the temporal trajectories, to ensure temporal consistency of trajectories across planning cycles. Constraints also may be applied during temporal optimization in some examples, to control or restrict driving maneuvers that are not supported by the autonomous vehicle.

Abstract: Techniques are described herein for determining simulated vehicle positional errors and correlating the positional errors with vehicle features. Such techniques may include receiving log data comprising trajectories and position data for a real vehicle, and executing a log-based simulated vehicle based on the log data. A simulated vehicle may be controlled to follow a simulation trajectory in a simulated environment based on the trajectory of the real vehicle. A simulation system may determine a difference between the positions of the simulated vehicle and corresponding positions of the real vehicle in the real environment. The techniques may further include determining a vehicle state features correlated to the lateral and/or longitudinal position errors of the simulated vehicle, and determining, based on the vehicle state features, position error distributions and/or models that can be used to control subsequent driving simulations.

Abstract: A prediction component of an autonomous vehicle may detect and use a combination of object attributes and semantic features to determine potential destinations and corresponding potential trajectories for the dynamic objects in an environment. In some examples, the prediction component may analyze sensor data using trained models to determine a number of object attributes for a dynamic object, as well as semantic features associated with the intent or purpose of the dynamic object. Using a combination of the object attributes and the semantic features, for the dynamic object and any number of potential destination(s), the prediction component may predict a likely potential destination and determine a predicted trajectory for the dynamic object. Based on predicted trajectory of the dynamic object, the autonomous vehicle may select and execute driving maneuvers to control the autonomous vehicle safely and efficiently within the environment.

Abstract: A passenger may be rather vulnerable to safety risks during pickup and/or drop-off of a passenger by a vehicle. To mitigate or eliminate such risk, the vehicle may determine an endpoint for a vehicle route to pickup or drop-off a passenger at a location. The vehicle may determine an estimated path between the endpoint and the location and may determine a safety confidence score by a machine-learned model for the estimated path and/or may predict a trajectory of a detected object to ascertain whether the estimated path is safe. The vehicle may execute any of a number of different mitigation actions to reduce or eliminate a safety risk if one is detected.

Abstract: An over actuated system capable of controlling wheel parameters, such as speed (e.g., by torque and braking), steering angles, caster angles, camber angles, and toe angles, of wheels in an associated vehicle. The system may determine the associated vehicle is in a rollover state and adjust wheel parameters to prevent vehicle rollover. Additionally, the system may determine a driving state and dynamically adjust wheel parameters to optimize driving, including, for example, cornering and parking. Such a system may also dynamically detect wheel misalignment and provide alignment and/or corrective driving solutions. Further, by utilizing degenerate solutions for driving, the system may also estimate tire-surface parameterization data for various road surfaces and make such estimates available for other vehicles via a network.

Abstract: Techniques for determining parking spaces and/or parking trajectories for a vehicle based on multistage filtering are discussed herein. In some examples, a vehicle may navigate to a destination within an environment. The vehicle may receive a set of parking locations and perform filtering operations to determine a subset of the parking locations. For example, the vehicle may use a list of parking characteristics (e.g., conditions) to filter the set of parking spaces. Based on determining the subset of parking spaces, the vehicle may generate a set of candidate trajectories to the subset of parking spaces. The vehicle may determine a subset of the candidate trajectories based on filtering the set of candidate trajectories according to various constraints. Further, the vehicle may determine a cost for the subset of candidate trajectories. Based on the costs, the vehicle may determine a candidate trajectory for the vehicle to follow.

Abstract: This disclosure describes techniques for autonomous vehicles to determine driving paths and associated trajectories through unstructured or off-route driving environments. When an object is detected within a driving environment, a vehicle may determine a cost-based side association for the object. Various costs may be used in different examples, including costs based on a cost plot and/or motion primitives that may vary for terminal and non-terminal desired destinations (or ending vehicle states). Using tree searches to determine estimated candidate costs, the autonomous vehicle may compare right-side and left-side driving path costs around the object to determine a side association for the object. Based on the side association, the autonomous vehicle may determine an updated planning corridor and/or a trajectory to control the vehicle from a current state to a desired ending vehicle state.

Abstract: A passenger may be rather vulnerable to safety risks during pickup and/or drop-off of a passenger by a vehicle. To mitigate or eliminate such risk, the vehicle may determine an endpoint for a vehicle route to pickup or drop-off a passenger at a location. The vehicle may determine an estimated path between the endpoint and the location and may determine a safety confidence score by a machine-learned model for the estimated path and/or may predict a trajectory of a detected object to ascertain whether the estimated path is safe. The vehicle may execute any of a number of different mitigation actions to reduce or eliminate a safety risk if one is detected.

Abstract: Techniques for determining parking spaces and/or parking trajectories for a vehicle based on multistage filtering are discussed herein. In some examples, a vehicle may navigate to a destination within an environment. The vehicle may receive a set of parking locations and perform filtering operations to determine a subset of the parking locations. For example, the vehicle may use a list of parking characteristics (e.g., conditions) to filter the set of parking spaces. Based on determining the subset of parking spaces, the vehicle may generate a set of candidate trajectories to the subset of parking spaces. The vehicle may determine a subset of the candidate trajectories based on filtering the set of candidate trajectories according to various constraints. Further, the vehicle may determine a cost for the subset of candidate trajectories. Based on the costs, the vehicle may determine a candidate trajectory for the vehicle to follow.

Abstract: An over actuated system capable of controlling wheel parameters, such as speed (e.g., by torque and braking), steering angles, caster angles, camber angles, and toe angles, of wheels in an associated vehicle. The system may determine the associated vehicle is in a rollover state and adjust wheel parameters to prevent vehicle rollover. Additionally, the system may determine a driving state and dynamically adjust wheel parameters to optimize driving, including, for example, cornering and parking. Such a system may also dynamically detect wheel misalignment and provide alignment and/or corrective driving solutions. Further, by utilizing degenerate solutions for driving, the system may also estimate tire-surface parameterization data for various road surfaces and make such estimates available for other vehicles via a network.

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 safely transitioning a vehicle from a first trajectory to a second trajectory. In response to a safety event, for example, a vehicle may switch from being controlled in accordance with a nominal planner output (a planned trajectory) to a safety, or stopping, trajectory. In some examples, in response to the vehicle overcoming the safety event, a safe and smooth return to a nominal planning system output trajectory (an updated planned trajectory) may be determined and used to cause the vehicle to continue along to a previously determined destination.

Abstract: This disclosure describes techniques for autonomous vehicles to determine driving paths and associated trajectories through unstructured or off-route driving environments. When an object is detected within a driving environment, a vehicle may determine a cost-based side association for the object. Various costs may be used in different examples, including costs based on a cost plot and/or motion primitives that may vary for terminal and non-terminal desired destinations (or ending vehicle states). Using tree searches to determine estimated candidate costs, the autonomous vehicle may compare right-side and left-side driving path costs around the object to determine a side association for the object. Based on the side association, the autonomous vehicle may determine an updated planning corridor and/or a trajectory to control the vehicle from a current state to a desired ending vehicle state.

Abstract: Four-wheel steering of a vehicle, e.g., in which leading wheels and trailing wheels are steered independently of each other, can provide improved maneuverability and stability. A first vehicle model may be used to determine trajectories for execution by a vehicle equipped with four-wheel steering. A second vehicle model may be used to control the vehicle relative to the determined trajectories. For instance, the second vehicle model can determine leading wheels steering angles for steering leading wheels of the vehicle and trailing wheels steering angles for steering trailing wheels of the vehicle, independently of the leading wheels.

Abstract: Techniques for optimal trajectory generation for a vehicle are described herein. In some cases, the techniques described herein include determining a first primary trajectory by a first component (e.g., a planner component) of the vehicle, determining a triggering event associated with the first primary trajectory by a second component (e.g., a trajectory validation component) of the vehicle, controlling the vehicle based on an alternative trajectory (e.g., a trajectory configured to cause the vehicle to stop and/or slow down) in response to the triggering event, determining a second primary trajectory by the first component and based at least in part on a state of the vehicle along the alternative trajectory, and one or more of: (i) controlling the vehicle based on the alternative trajectory if the triggering event is still present, or (ii) controlling the vehicle based on the second primary trajectory if the triggering event is no longer present.