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 determining a driving trajectory for an autonomous vehicle to follow are described herein. A vehicle may receive a destination associated with a region of an environment to which the vehicle is to navigate. Based on the destination, the vehicle can generate a local probabilistic graph that includes states, edges connecting the states, actions for the vehicle to perform along the graph, and/or probabilities associated with the actions. The vehicle may determine cost-to-go values for each state within the local probabilistic graph. While navigating to the destination, the vehicle can generate candidate trajectories. When determining the cost of following a candidate trajectory, the vehicle can determine the cost by projecting an ending state of the candidate trajectory onto the local probabilistic graph to determine an optimal action which considers a larger understanding of the goal of the vehicle. The vehicle can be controlled based on the candidate trajectory.

Abstract: Techniques for generating lane ending costs for determining costs for vehicle positions in an environment are described herein. A planning component within a vehicle may determine, a lane ending of a lane of a route, the lane ending indicative of whether a vehicle is able to continue along the lane to a desired endpoint of the route. The planning component may determine, based at least in part on the lane ending, a lane ending cost and determine a cost for a candidate trajectory based at least in part on the lane ending cost. The planning component may then determine a control trajectory for an autonomous vehicle, based at least in part on the cost for the candidate trajectory.

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 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 representing sensor data and predicted behavior of various objects in an environment are described herein. For example, an autonomous vehicle can represent prediction probabilities as an uncertainty model that may be used to detect potential collisions, define a safe operational zone or drivable area, and to make operational decisions in a computationally efficient manner. The uncertainty model may represent a probability that regions within the environment are occupied using a heat map type approach in which various intensities of the heat map represent a likelihood of a corresponding physical region being occupied at a given point in time.

Abstract: Techniques for enabling a vehicle to navigate around a blocking object, such as double-parked vehicle (“DPV”), are discussed herein. In some examples, a vehicle may receive sensor data representative of an environment. The vehicle may analyze such sensor data to determine that a DPV is located proximate the vehicle. When determining whether to follow the trajectory, the vehicle may determine one or more cost values corresponding to the trajectory. Further, the vehicle may receive weighted heatmap(s) that cover the region proximate the DPV. The vehicle may modify some or all cost values of the trajectory based on the weighted values of the heatmap(s). In some examples, the vehicle may follow the trajectory based at least in part on the cost values.

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 representing sensor data and predicted behavior of various objects in an environment are described herein. For example, an autonomous vehicle can represent prediction probabilities as an uncertainty model that may be used to detect potential collisions, define a safe operational zone or drivable area, and to make operational decisions in a computationally efficient manner. The uncertainty model may represent a probability that regions within the environment are occupied using a heat map type approach in which various intensities of the heat map represent a likelihood of a corresponding physical region being occupied at a given point in time.

Abstract: Embodiments of the disclosure are drawn to apparatuses and methods for bad row mode. The memory may prevent proper access operations (e.g., read operations) from being performed on a selected bad row of the memory as part of a bad row mode. For example, the memory may store a bad row address and when an access address matches the bad row address, may suppress one or more signals, change data read from the address, or combinations thereof. The bad row mode may be used to provide a positive control for post package repair (PPR) operations on the memory. A controller may enter the memory into bad row mode and then test the memory to determine if the selected bad row can be located and repaired via PPR.

Abstract: Techniques for compensating for errors in position of a vehicle are discussed herein. In some cases, a discrepancy may exist between a measured state of the vehicle and a desired state as determined by a system of the vehicle. Techniques and methods for a planning architecture of an autonomous vehicle that is able to provide maintain a smooth trajectory as the vehicle follows a planned path or route. In some cases, a planning architecture of the autonomous vehicle may compensate for differences between an estimated state and a planned path without the use of a separate system. In this example process, the planning architecture may include a mission planning system, a decision system, and a tracking system that together output a trajectory for a drive system.

Abstract: Embodiments of the disclosure are drawn to apparatuses and methods for bad row mode. The memory may prevent proper access operations (e.g., read operations) from being performed on a selected bad row of the memory as part of a bad row mode. For example, the memory may store a bad row address and when an access address matches the bad row address, may suppress one or more signals, change data read from the address, or combinations thereof. The bad row mode may be used to provide a positive control for post package repair (PPR) operations on the memory. A controller may enter the memory into bad row mode and then test the memory to determine if the selected bad row can be located and repaired via PPR.

Abstract: Techniques for determining vehicle trajectories to operate a vehicle according to a planned path are described herein. In an example, a vehicle computing system may determine a location of the vehicle at a first time. Based on the location, the vehicle computing system may determine an estimated location of the vehicle at a second time, the estimated location of the vehicle including a lateral coordinate and a longitudinal coordinate. The vehicle computing system may determine the longitudinal coordinate based on a vehicle trajectory associated with the first time (e.g., previously determined trajectory) and the lateral coordinate based on the planned path. The vehicle computing system may determine a second vehicle trajectory based in part on the estimated location and the first trajectory, and may control the vehicle according to the second vehicle trajectory.

Abstract: Techniques for representing sensor data and predicted behavior of various objects in an environment are described herein. For example, an autonomous vehicle can represent prediction probabilities as an uncertainty model that may be used to detect potential collisions, define a safe operational zone or drivable area, and to make operational decisions in a computationally efficient manner. The uncertainty model may represent a probability that regions within the environment are occupied using a heat map type approach in which various intensities of the heat map represent a likelihood of a corresponding physical region being occupied at a given point in time.

Abstract: Techniques for representing sensor data and predicted behavior of various objects in an environment are described herein. For example, an autonomous vehicle can represent prediction probabilities as an uncertainty model that may be used to detect potential collisions, define a safe operational zone or drivable area, and to make operational decisions in a computationally efficient manner. The uncertainty model may represent a probability that regions within the environment are occupied using a heat map type approach in which various intensities of the heat map represent a likelihood of a corresponding physical region being occupied at a given point in time.

Abstract: Techniques for compensating for errors in position of a vehicle are discussed herein. In some cases, a discrepancy may exist between a measured state of the vehicle and a desired state as determined by a system of the vehicle. Techniques and methods for a planning architecture of an autonomous vehicle that is able to provide maintain a smooth trajectory as the vehicle follows a planned path or route. In some cases, a planning architecture of the autonomous vehicle may compensate for differences between an estimated state and a planned path without the use of a separate system. In this example process, the planning architecture may include a mission planning system, a decision system, and a tracking system that together output a trajectory for a drive system.

Abstract: A device capable of being mounted on a ladder having hollow rungs comprises a platform or tray having a first arm mounted thereon adjacent one end thereof and a second arm pivotably mounted thereon adjacent the other end thereof and extending below the platform or tray, said second arm being adjustable in length and said arms being adapted to enter the hollow rungs of a ladder. In use the first and second arms are inserted in separate rungs of the ladder, the second arm being inserted in the rung below the first arm.