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LC_NGSIM

lane change trajectories extracted from NGSIM fig1

This dataset comes with the paper below

@inproceedings{dong2017lane,
  title={Lane-change social behavior generator for autonomous driving car by non-parametric regression in Reproducing Kernel Hilbert Space},
  author={Dong, Chiyu and Zhang, Yihuan and Dolan, John M},
  booktitle={Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on},
  pages={4489--4494},
  year={2017},
  organization={IEEE}
}

Paper is accepted by IROS 2017

A related Ramp Merging Control paper is published in IV 2017,

here is the accepted version, and an updated version: Ramp merge data and full link to IEEE Xplore will be updated soon. To see the final version:

@inproceedings{dong2017intention,
  title={Intention estimation for ramp merging control in autonomous driving},
  author={Dong, Chiyu and Dolan, John M and Litkouhi, Bakhtiar},
  booktitle={Intelligent Vehicles Symposium (IV), 2017 IEEE},
  pages={1584--1589},
  year={2017},
  organization={IEEE}
}

The original NGSIM program:

@article{
author={Alexiadis,Vassili and Colyar,James and Halkias,John and Hranac,Rob and McHale,Gene},
year={2004},
month={08},
title={The Next Generation Simulation Program},
journal={Institute of Transportation Engineers.ITE Journal},
volume={74},
number={8},
pages={22-26},
}

Source Code.

For source code, click here

Data description

nlc_data_5zones.mat

  • nlc_data: cell, contains 870 sequences of none-lane-change scenarios.
    • veh_s: subjcet vehicle
      • x: Lateral position (m)
      • y: Longitudinal position (m)
      • len: Vehicle length (m)
      • wid: Vehicle width (m)
      • v: Vehicle speed (m/s)
      • a: Vehicle acceleration (m/s^2)
    • veh_f: front vehicle in current lane
    • veh_r: rear vehicle in current lane
    • veh_ft: front vehicle in target lane
    • veh_rt: rear vehicle in target lane
    • veh_st: overlap vehicle in target lane

lc_data_20s_withpoints.mat

  • lc_data: Same structure as nlc_data
  • points: array, n row, 2 column, [start, end]