README.md

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DLII-JSSP

This repo is the implementation of the following paper:

"Efficient Sampling-based Trajectory Planning with Dual-Layer Probabilistic Intention Prediction for Autonomous Driving in Complex Intersections"

The open-source code will be published here.

To-Do List

• Results [Completed]
• Datasets [Completed]
• Code [In Progress]

0. Introduction

1. Set up your virtual environment

• Create a virtual environment using conda (python3.9)

  conda create -n dlii python=3.9

• Activate the virtual environment

  conda activate dlii

• Install python dependency packages via pip. Open the project directory as follows ../setup/requirements.txt, install dependency package.

  pip install -r setup/requirements.txt
  # Speed up (use pip source in China)
  pip install -r setup/requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

2. Preparation

2.1 Download the code and data

• Clone this repo from the GitHub.

 git clone [email protected]:byChenZhifa/DLII-JSSP.git

• Download the all scenarios data [here] and save it to project directory ./data_scenarios .

  • 8 scenes demo: scenes_demo(8 for planning ablation study).zip
  • all the intersection scenes: scenes_all(800).tar.xz

 cd DLII-JSSP
 mkdir data_scenarios

3. run.

2.2 Suggested structure for the project code

TODO

3. Our test results

Note: This framework initiates with a high-layer inference for the target road and subsequently refines this prediction for the target lane.

Fig. 2. Schematic diagram of dual-layer intention inference for road layer and lane layer

Fig. 4. Schematics on the conversion from Cartesian frame to Frenet frame.

Fig. 6. Multiple Lane Centerline Models based on Lane Frenet States inside One Road

3.1 More results for trajectory predictor

Test scenario :7_28_1_89, vehicle #4 turn left test using two method. (CVCYR, Our predictor with IMM)

CVCYR

Our predictor with IMM

Test scenario :8_2_1_563, vehicle #8 going straight and changing lanes.(CVCYR, Our predictor with IMM)

CVCYR

Our predictor with IMM

Test scenario :8_3_1_54, vehicle # 3 turn right.(CVCYR, Our predictor with IMM)

CVCYR

Our predictor with IMM

3.2 More results for trajectory planner

Note: Some results of trajectory planning using predefined linear strategies .

Test scenario : JSSP-8_3_4_565

Test scenario : JSSP-8_8_1_135

Test scenario : JSSP-8_32_left_straight_in_opposite_36

Test scenario : JSSP-8_9_2_332

Test scenario : JSSP-10_60_straight_in_adjacent_left_61

Contact us

If you have any issues with the code, please contact to this email: [email protected]

Citation

If you find our work useful for your research, please consider citing the paper.

TODO

reference

• https://github.com/SS47816/fiss_plus_planner