https://github.com/aakarsh/traffic-behavior-simulation

https://github.com/aakarsh/traffic-behavior-simulation

Repository

https://github.com/aakarsh/traffic-behavior-simulation/blob/main/

# Traffic Behavior Simulation (tbsim)
TBSIM is a simulation environment designed for data-driven closed-loop simulation of autonomous vehicles. It supports training and evaluation of popular traffic models such as behavior cloning, CVAE, and our new [BITS](https://arxiv.org/abs/2208.12403) model specifically designed for AV simulation. The users can flexibly specify the simulation environment and plug in their own model (learned or analytic) for evaluation.

Thanks to [trajdata](https://github.com/NVlabs/trajdata), TBSIM can access data and scenarios from a wide range of public datasets, including [Lyft Level 5](https://woven.toyota/en/prediction-dataset), [nuScenes](https://www.nuscenes.org/nuscenes), and [nuPlan](https://nuplan.org/).

TBSIM is well equiped with abundant util functions, and supports batched simulation in parallel, logging, and replay. We also provide a suite of simulation metrics that measures the safety, liveness, and diversity of the simulation.



## Installation

Install `tbsim`
```angular2html
conda create -n tbsim python=3.8
conda activate tbsim
git clone git@github.com:NVlabs/traffic-behavior-simulation.git tbsim
cd tbsim
pip install -e .
```

Install `trajdata`
```
cd ..
git clone ssh://git@github.com:NVlabs/trajdata.git trajdata
cd trajdata
# replace requirements.txt with trajdata_requirements.txt included in tbsim
pip install -e .
```

Install `Pplan`
```
cd ..
git clone ssh://git@github.com:NVlabs/spline-planner.git Pplan
cd Pplan
pip install -e .
```

Usually the user needs to install torch separately that fits the hardware setup (OS, GPU, CUDA version, etc., check https://pytorch.org/get-started/locally/ for instructions)
## Quick start
### 1. Obtain dataset(s)
We currently support the Lyft Level 5 [dataset](https://woven.toyota/en/prediction-dataset) and the nuScenes [dataset](https://www.nuscenes.org/nuscenes).

#### Lyft Level 5:
* Download the Lyft Prediction dataset (only the metadata and the map) and organize the dataset directory as follows:
    ```
    lyft_prediction/
       aerial_map/
       semantic_map/
       meta.json
    scenes
          sample.zarr
          train_full.zarr
          train.zarr
    |   |   validate.zarr
    ```

#### nuScenes
* Download the nuScenes dataset (with the v1.3 map extension pack) and organize the dataset directory as follows:
    ```
    nuscenes/
       maps/
        expansion/
       v1.0-mini/
       v1.0-trainval/
    ```
### 2. Train a behavior cloning model
Lyft dataset (set `--debug` flag to suppress wandb logging):
```
python scripts/train.py --dataset_path  --config_name l5_bc --debug
```

nuScenes dataset (set `--debug` flag to suppress wandb logging):
```
python scripts/train.py --dataset_path  --config_name nusc_bc --debug
```

See the list of registered algorithms in `configs/registry.py`

### 3. Train BITS model

Lyft dataset:

First train a spatial planner:
```
python scripts/train.py --dataset_path  --config_name l5_spatial_planner --debug
```
Then train a multiagent predictor:
```
python scripts/train.py --dataset_path  --config_name l5_agent_predictor --debug
```

nuScenes dataset:
First train a spatial planner:
```
python scripts/train.py --dataset_path  --config_name nusc_spatial_planner --debug
```
Then train a multiagent predictor:
```
python scripts/train.py --dataset_path  --config_name nusc_agent_predictor --debug
```

See the list of registered algorithms in `configs/registry.py`
### 4. Evaluate a trained model (closed-loop simulation)
```
python scripts/evaluate.py \
  --results_root_dir results/ \
  --num_scenes_per_batch 2 \
  --dataset_path  \
  --env  \
  --policy_ckpt_dir  \
  --policy_ckpt_key  \
  --eval_class BC \
  --render
```

### 5. Closed-loop simulation with BITS
With the spatial planner and multiagent predictor trained, one can run BITS simulation with

```
python scripts/evaluate.py \
  --results_root_dir results/ \
  --dataset_path  \
  --env  \
  --ckpt_yaml  \
  --eval_class HierAgentAware \
  --render
```
The ckpt_yaml file specifies the checkpoints for the spatial planner and predictor, an example can be found at `evaluation/BITS_example.yaml` with pretrained checkpoints.

Pretrained checkpoints can be downloaded at https://www.dropbox.com/sh/vdmy9eq9nlvx0nf/AADpCpvCF2ypLIuvVe1Cizd0a?dl=0.

You can check the launch.json file if using VS code.

### 6. Closed-loop evaluation of policy with BITS

TBSIM allows the ego to have a separate policy than the rest of the agents. An example command is

```
python scripts/evaluate.py \
  --results_root_dir results/ \
  --dataset_path  \
  --env  \
  --ckpt_yaml  \
  --eval_class  \
  --agent_eval_class=HierAgentAware\
  --render
```

Here your policy should be declared in `tbsim/evaluation/policy_composer.py`.

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