무엇보다도, 에이전트(예를 들면, 차량, 자전거, 보행자 등)가 이전 움직임, 도로망, 주변 대상체 및/또는 다른 관련 환경 인자에 기초하여 환경에서 어떻게 이동할 것인지를 예측하기 위한 기술이 설명된다. 하나의 궤적 예측 기술은 에이전트의 움직임에 대한 확률 맵을 생성하는 것을 포함한다. 다른 궤적 예측 기술은 에이전트의 움직임에 대한 궤적 격자를 생성하는 것을 포함한다. 추가적으로, 상이한 궤적 예측 기술은 각각의 모델이 현재 입력에 기초하여 궤적을 생성하도록 분류기(예를 들면, 신경 네트워크)가 다수의 (학습된) 모드의 확률을 분류하도록 트레이닝되는 멀티 모달 회귀를 포함한다.

    Among other things, techniques are described for predicting how an agent (e.g., a vehicle, bicycle, pedestrian, etc.) will move in an environment based on prior movement, the road network, the surrounding objects and/or other relevant environmental factors. One trajectory prediction technique involves generating a probability map for an agent's movement. Another trajectory prediction technique involves generating a trajectory lattice, for an agent's movement. In addition, a different trajectory prediction technique involves multi-modal regression where a classifier (e.g., a neural network) is trained to classify the probability of a number of (learned) modes such that each model produces a trajectory based on the current input.


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    Title :

    TRAJECTORY PREDICTION FROM PRECOMPUTED OR DYNAMICALLY GENERATED BANK OF TRAJECTORIES


    Additional title:

    미리 계산되거나 동적으로 생성된 궤적 뱅크로부터의 궤적 예측


    Publication date :

    2024-12-13


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Korean


    Classification :

    IPC:    B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G01S RADIO DIRECTION-FINDING , Funkpeilung / G06T Bilddatenverarbeitung oder Bilddatenerzeugung allgemein , IMAGE DATA PROCESSING OR GENERATION, IN GENERAL



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