TTSG: Text-to-Traffic-Scene Generation

A text-to-traffic-scene generation pipeline with the large language model

Bo-Kai Ruan, Hao-Tang Tsui, Yung-Hui Li, Hong-Han Shuai

Contact: Bo-Kai Ruan

arXiv paper | Project Website

A. Installation 🚀

First, download Carla from the official website. This codebase has been tested with version 0.9.15.

# Update the version number if necessary
conda create --name ttsg python=3.10
conda activate ttsg
conda install pytorch==2.2.2 torchvision==0.17.2 pytorch-cuda=12.1 -c pytorch -c nvidia -y
pip install carla==0.9.15  # Choose your specific Carla version
pip install -r requirements.txt

Next, set up your GPT model key. Obtain the API key from OpenAI and save it to a .env file:

OPENAI_API_KEY=your_openai_key

B. Usage 📦

B-1. Text-to-Scene

This functionality allows users to generate a traffic scene from scratch based on a text prompt. There's no need to manually create an ego vehicle—the scene, including the ego car, will be generated automatically 😊.

# Ensure the Carla server is running before executing this command. If --plan-only is given, no server is needed
python text_to_scene.py \
    --input_text "user's input prompt" \
    --save-dir "path/to/save/recorded/images" \
    --use-cache \
    --return-ego \ # Include this flag if you want the ego car in the output
    --plan-only \ # Use this flag if you only want to plan the scene withour rendering

The use-cache flag uses a pre-built graph if available in the cache directory; otherwise, it generates a new graph and stores it in the cache. To force a new graph generation, either delete the cache directory or omit the use-cache flag.

B-2. Diversity Test

To conduct the diversity test:

python misc/evaluate.py --path <path-to-scenarios>

Our reported results from the paper are available in the diversity_test directory.

To create a custom diversity test, you can use the text-to-scene command mentioned above to generate traffic scenarios.

B-3. Training and Evaluation on SafeBench

For training and evaluation, we follow the setup of ChatScene on the benchmark of SafeBench.

# Training, the scenario_id is range from 1 to 8
python scripts/run_train.py --agent_cfg=adv_text_to_scene.yaml --scenario_cfg=train_agent_text_to_scene.yaml --mode train_agent --scenario_id <scenario_id>

# Evaluation, choose the best epoch for evaluate, using the scenic configuration for fair comparison
python scripts/run_eval.py --agent_cfg=adv_text_to_scene.yaml --scenario_cfg=eval_scenic.yaml --mode eval --scenario_id <scenario_id> --test_epoch <epoch>

We provide the following pretrained models:

Scenario ID	Scenario Name	Path
1	Straight Obstacle	`safebench/agent/model_ckpt/adv_train/sac/text_to_scene/scenario_1/model.sac.-001.torch`
3	Lane Changing	`safebench/agent/model_ckpt/adv_train/sac/text_to_scene/scenario_3/model.sac.-001.torch`
6	Unprocted Left-turn	`safebench/agent/model_ckpt/adv_train/sac/text_to_scene/scenario_6/model.sac.-001.torch`

To use our pretrained models, set the test_epoch flag to be -1.

C. Environment 🌍

Detailed information about the environment setup can be found here.

D. Known Issue 🚨

We have identified an issue with the current Carla version, where a segmentation fault occurs when setting the pedestrian’s relative position to at_the_sidewalk. This happens when calling the previous() function from the Carla API, causing the program to crash. We recommend retrying the process if you encounter this issue.

Citation

@article{ruan2024ttsg,
  title={Traffic Scene Generation from Natural Language Description for Autonomous Vehicles with Large Language Model},
  author={Ruan, Bo-Kai and Tsui, Hao-Tang and Li, Yung-Hui and Shuai, Hong-Han},
  journal={arXiv preprint arXiv:2409.09575},
  year={2024}
}

basiclab/TTSG