Evaluation framework for predicted trajectories in autonomous driving vehicle traffic prediction

A technology of automatic driving and trajectory, applied in traffic control systems of road vehicles, motor vehicles, traffic control systems, etc., can solve difficult problems such as actual similarity

Active Publication Date: 2019-06-25
BAIDU COM TIMES TECH (BEIJING) CO LTD +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

While it is easy to evaluate predicted "behavior" at a concrete classification level, it is difficu...

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  • Evaluation framework for predicted trajectories in autonomous driving vehicle traffic prediction
  • Evaluation framework for predicted trajectories in autonomous driving vehicle traffic prediction
  • Evaluation framework for predicted trajectories in autonomous driving vehicle traffic prediction

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Embodiment Construction

[0018] Various embodiments and aspects of the disclosure will be described with reference to details discussed below, and the accompanying drawings will illustrate the various embodiments. The following description and drawings are illustrative of the present disclosure and should not be construed as limiting the present disclosure. Numerous specific details are described to provide a thorough understanding of various embodiments of the present disclosure. However, in certain instances, well-known or conventional details are not described in order to provide a concise discussion of embodiments of the present disclosure.

[0019] In the specification, reference to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present disclosure. The appearances of the phrase "in one embodiment" in various places in the specification are not necessari...

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Abstract

Disclosed are a computer-implemented method for evaluating predictions of trajectories by autonomous driving vehicles, a non-transitory machine-readable medium, and a data processing system. When a predicted trajectory is received, a set of one or more features are extracted from at least some of the trajectory points of the predicted trajectory. The predicted trajectory is predicted using a prediction method or algorithm based on perception data perceiving an object within a driving environment surrounding an autonomous driving vehicle (ADV). The extracted features are fed into a predetermined DNN model to generate a similarity score. The similarity score represents a difference or similarity between the predicted trajectory and a prior actual trajectory that was used to train the DNN model. The similarity score can be utilized to evaluate the prediction method that predicted the predicted trajectory. It is very easy to numerically and objectively measure the actual similarity betweenpredicted trajectories and actual trajectories.

Description

technical field [0001] Embodiments of the present disclosure generally relate to operating autonomous vehicles. More specifically, embodiments of the present disclosure relate to evaluating traffic predictions for autonomous vehicles. Background technique [0002] A vehicle operating in an autonomous driving mode (eg, driverless) relieves the occupants, especially the driver, of some driving-related duties. When operating in autonomous mode, the vehicle can navigate to various locations using on-board sensors, allowing the vehicle to drive with minimal human interaction or in some cases without any passengers. [0003] Traffic prediction is a very important problem to solve when building software for autonomous vehicles. While the perception module collects sensor data and generates structured detections of objects such as vehicles, cyclists, and pedestrians, the prediction module needs to actually predict the behavior of these objects. The predicted output includes predi...

Claims

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Application Information

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IPC IPC(8): G01C21/32
CPCG05D1/0221G05D2201/0213G06N3/08G06V20/58G06V10/82G06V10/7788G05D1/0212B60W60/0011G06T2207/30241G06V20/588G08G1/163G08G1/166G05D1/021G06N3/04G06F18/2185
Inventor 李力耘缪景皓夏中谱
Owner BAIDU COM TIMES TECH (BEIJING) CO LTD
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