UNMAPPED U-TURN BEHAVIOR PREDICTION USING MACHINE LEARNING

    公开(公告)号:US20220326714A1

    公开(公告)日:2022-10-13

    申请号:US17227177

    申请日:2021-04-09

    Applicant: Waymo LLC

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating unmapped U-turn predictions using a machine learning model. One of the methods includes obtaining features of an agent travelling on a roadway. One or more unmapped U-turn regions in a vicinity of the agent on the roadway are identified. For each of the unmapped U-turn regions and from at least the features of the agent, a respective likelihood score that represents a likelihood that the agent intends to make an unmapped U-turn at the unmapped U-turn region is generated. Based on the respective likelihood scores, one or more of the unmapped U-turn regions are selected. For each selected unmapped U-turn region, data specifying a candidate future trajectory in which the agent makes the unmapped U-turn at the selected unmapped U-turn region is provided as a possible future trajectory for the agent.

    Predicting Jaywaking Behaviors of Vulnerable Road Users

    公开(公告)号:US20210382489A1

    公开(公告)日:2021-12-09

    申请号:US16892579

    申请日:2020-06-04

    Applicant: Waymo LLC

    Abstract: Jaywalking behaviors of vulnerable road users (VRUs) such as cyclists or pedestrians can be predicted. Location data is obtained that identifies a location of a VRU within a vicinity of a vehicle. Environmental data is obtained that describes an environment of the VRU, where the environmental data identifies a set of environmental features in the environment of the VRU. The system can determine a nominal heading of the VRU, and generate a set of predictive inputs that indicate, for each of at least a subset of the set of environmental features, a physical relationship between the VRU and the environmental feature. The physical relationship can be determined with respect to the nominal heading of the VRU and the location of the VRU. The set of predictive inputs can be processed with a heading estimation model to generate a predicted heading offset (e.g., a target heading offset) for the VRU.

    PREDICTING NEAR-CURB DRIVING BEHAVIOR ON AUTONOMOUS VEHICLES

    公开(公告)号:US20220355824A1

    公开(公告)日:2022-11-10

    申请号:US17316625

    申请日:2021-05-10

    Applicant: Waymo LLC

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting near-curb driving behavior. One of the methods includes obtaining agent trajectory data for an agent in an environment, the agent trajectory data comprising a current location and current values for a predetermined set of motion parameters of the agent; processing a model input generated from the agent trajectory data using a trained machine learning model to generate a model output comprising a prediction of whether the agent will exhibit near-curb driving behavior within a predetermined timeframe, wherein an agent exhibits near-curb driving behavior when the agent operates within a particular distance of an edge of a road in the environment; and using the prediction to generate a planned path for a vehicle in the environment.

    Continuing Lane Driving Prediction

    公开(公告)号:US20220291690A1

    公开(公告)日:2022-09-15

    申请号:US17199948

    申请日:2021-03-12

    Applicant: Waymo LLC

    Abstract: The technology relates to controlling a vehicle in an autonomous driving mode in accordance with behavior predictions for other road users in the vehicle's vicinity. In particular, the vehicle's onboard computing system may predict whether another road user will perform a “continuing” lane driving operation, such as going straight in a turn-only lane. Sensor data from detected/observed objects in the vehicle's nearby environment may be evaluated in view of one or more possible behaviors for different types of objects. In addition, roadway features, in particular whether lane segments are connected in a roadgraph, are also evaluated to determine probabilities of whether other road users may make an improper continuing lane driving operation. This is used to generate more accurate behavior predictions, which the vehicle can use to take alternative (e.g., corrective) driving actions.

    BEHAVIOR PREDICTION FOR RAILWAY AGENTS FOR AUTONOMOUS DRIVING SYSTEM

    公开(公告)号:US20240126296A1

    公开(公告)日:2024-04-18

    申请号:US18494997

    申请日:2023-10-26

    Applicant: Waymo LLC

    CPC classification number: G05D1/617 G05D1/228

    Abstract: To operate an autonomous vehicle, a rail agent is detected in a vicinity of the autonomous vehicle using a detection system. One or more tracks are determined on which the detected rail agent is possibly traveling, and possible paths for the rail agent are predicted based on the determined one or more tracks. One or more motion paths are determined for one or more probable paths from the possible paths, and a likelihood for each of the one or more probable paths is determined based on each motion plan. A path for the autonomous vehicle is then determined based on a most probable path associated with a highest likelihood for the rail agent, and the autonomous vehicle is operated using the determined path.

    Mapping off-road entries for autonomous vehicles

    公开(公告)号:US11774259B2

    公开(公告)日:2023-10-03

    申请号:US17469253

    申请日:2021-09-08

    Applicant: WAYMO LLC

    Abstract: Aspects of the disclosure provide a method of identifying off-road entry lane waypoints. For instance, a polygon representative of a driveway or parking area may be identified from map information. A nearest lane may be identified based on the polygon. A plurality of lane waypoints may be identified. Each of the lane waypoints may correspond to a location within at least one lane. The polygon and the plurality of lane waypoints may be input into a model. A lane waypoint of the plurality of lane waypoints may be selected as an off-road entry lane waypoint. The off-road entry lane waypoint may be associated with the nearest lane. The association may be provided to an autonomous vehicle in order to allow the autonomous vehicle to use the association to control the autonomous vehicle in an autonomous driving mode.

    MAPPING OFF-ROAD ENTRIES FOR AUTONOMOUS VEHICLES

    公开(公告)号:US20230074387A1

    公开(公告)日:2023-03-09

    申请号:US17469253

    申请日:2021-09-08

    Applicant: WAYMO LLC

    Abstract: Aspects of the disclosure provide a method of identifying off-road entry lane waypoints. For instance, a polygon representative of a driveway or parking area may be identified from map information. A nearest lane may be identified based on the polygon. A plurality of lane waypoints may be identified. Each of the lane waypoints may correspond to a location within at least one lane. The polygon and the plurality of lane waypoints may be input into a model. A lane waypoint of the plurality of lane waypoints may be selected as an off-road entry lane waypoint. The off-road entry lane waypoint may be associated with the nearest lane. The association may be provided to an autonomous vehicle in order to allow the autonomous vehicle to use the association to control the autonomous vehicle in an autonomous driving mode.

    Predicting near-curb driving behavior on autonomous vehicles

    公开(公告)号:US12139172B2

    公开(公告)日:2024-11-12

    申请号:US17316625

    申请日:2021-05-10

    Applicant: Waymo LLC

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting near-curb driving behavior. One of the methods includes obtaining agent trajectory data for an agent in an environment, the agent trajectory data comprising a current location and current values for a predetermined set of motion parameters of the agent; processing a model input generated from the agent trajectory data using a trained machine learning model to generate a model output comprising a prediction of whether the agent will exhibit near-curb driving behavior within a predetermined timeframe, wherein an agent exhibits near-curb driving behavior when the agent operates within a particular distance of an edge of a road in the environment; and using the prediction to generate a planned path for a vehicle in the environment.

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