NGSIM US-101

ngsim us-101
This study investigates the recognition and prediction of vehicle lane-changing behavior based on data-driven methodologies. The project selects vehicle kinematic states, sequences, and multi objects spatial-temporal interactions as the core input parameters. For example, by extracting and smoothing trajectory sequences from open-source datasets and feeding them into different algorithmic models, the study observes and compares variations in intention recognition accuracy and prediction performance.

NGSIM US-101
本研究基于数据驱动方法，探究车辆变道行为的识别与预测问题。本研究选取车辆运动学状态、轨迹序列以及多目标时空交互作为核心输入参数。例如，本研究从开源数据集中提取并平滑处理轨迹序列，将其输入至不同算法模型中，观测并对比意图识别准确率与预测性能的差异。

搜集汇总

背景与挑战

背景概述

NGSIM US-101是一个用于研究车辆换道行为识别与预测的数据集，基于车辆运动状态、序列和时空交互参数。它包含多个轨迹CSV文件，支持数据驱动的方法进行模型比较。