[CVPR'21] Multi-Modal Fusion Transformer for End-to-End Autonomous Driving

Overview

TransFuser

This repository contains the code for the CVPR 2021 paper Multi-Modal Fusion Transformer for End-to-End Autonomous Driving. If you find our code or paper useful, please cite

@inproceedings{Prakash2021CVPR,
  author = {Prakash, Aditya and Chitta, Kashyap and Geiger, Andreas},
  title = {Multi-Modal Fusion Transformer for End-to-End Autonomous Driving},
  booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021}
}

Setup

Install anaconda

wget https://repo.anaconda.com/archive/Anaconda3-2020.11-Linux-x86_64.sh
bash Anaconda3-2020.11-Linux-x86_64.sh
source ~/.profile

Clone the repo and build the environment

git clone https://github.com/autonomousvision/transfuser
cd transfuser
conda create -n transfuser python=3.7
pip3 install -r requirements.txt
conda activate transfuser

Download and setup CARLA 0.9.10.1

chmod +x setup_carla.sh
./setup_carla.sh

Data Generation

The training data is generated using leaderboard/team_code/auto_pilot.py in 8 CARLA towns and 14 weather conditions. The routes and scenarios files to be used for data generation are provided at leaderboard/data.

Running CARLA Server

With Display

./CarlaUE4.sh -world-port=<port> -opengl

Without Display

Without Docker:

SDL_VIDEODRIVER=offscreen SDL_HINT_CUDA_DEVICE=<gpu_id> ./CarlaUE4.sh -world-port=<port> -opengl

With Docker:

Instructions for setting up docker are available here. Pull the docker image of CARLA 0.9.10.1 docker pull carlasim/carla:0.9.10.1.

Docker 18:

docker run -it --rm -p 2000-2002:2000-2002 --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=<gpu_id> carlasim/carla:0.9.10.1 ./CarlaUE4.sh -world-port=2000 -opengl

Docker 19:

docker run -it --rm --net=host --gpus '"device=<gpu_id>"' carlasim/carla:0.9.10.1 ./CarlaUE4.sh -world-port=2000 -opengl

If the docker container doesn't start properly then add another environment variable -e SDL_AUDIODRIVER=dsp.

Run the Autopilot

Once the CARLA server is running, rollout the autopilot to start data generation.

./leaderboard/scripts/run_evaluation.sh

The expert agent used for data generation is defined in leaderboard/team_code/auto_pilot.py. Different variables which need to be set are specified in leaderboard/scripts/run_evaluation.sh. The expert agent is based on the autopilot from this codebase.

Routes and Scenarios

Each route is defined by a sequence of waypoints (and optionally a weather condition) that the agent needs to follow. Each scenario is defined by a trigger transform (location and orientation) and other actors present in that scenario (optional). The leaderboard repository provides a set of routes and scenarios files. To generate additional routes, spin up a CARLA server and follow the procedure below.

Generating routes with intersections

The position of traffic lights is used to localize intersections and (start_wp, end_wp) pairs are sampled in a grid centered at these points.

python3 tools/generate_intersection_routes.py --save_file <path_of_generated_routes_file> --town <town_to_be_used>

Sampling individual junctions from a route

Each route in the provided routes file is interpolated into a dense sequence of waypoints and individual junctions are sampled from these based on change in navigational commands.

python3 tools/sample_junctions.py --routes_file <xml_file_containing_routes> --save_file <path_of_generated_file>

Generating Scenarios

Additional scenarios are densely sampled in a grid centered at the locations from the reference scenarios file. More scenario files can be found here.

python3 tools/generate_scenarios.py --scenarios_file <scenarios_file_to_be_used_as_reference> --save_file <path_of_generated_json_file> --towns <town_to_be_used>

Training

The training code and pretrained models are provided below.

mkdir model_ckpt
wget https://s3.eu-central-1.amazonaws.com/avg-projects/transfuser/models.zip -P model_ckpt
unzip model_ckpt/models.zip -d model_ckpt/
rm model_ckpt/models.zip

Evaluation

Spin up a CARLA server (described above) and run the required agent. The adequate routes and scenarios files are provided in leaderboard/data and the required variables need to be set in leaderboard/scripts/run_evaluation.sh.

CUDA_VISIBLE_DEVICES=<gpu_id> ./leaderboard/scripts/run_evaluation.sh

Acknowledgements

This implementation is based on codebase from several repositories.

Comments
  • Poor evaluation result

    Poor evaluation result

    Hi! Thanks for your amazing work. I try to achieve the same performance as the paper shown, but now have some problems.

    1. In leaderboard/data/scenarios/town05_all_scenarios.json, I see that the last one has "scenario_type": "Scenario10". But in the evaluation log, I get these:
    "meta": {
                    "exceptions": [
                        [
                            "RouteScenario_16",
                            0,
                            "Failed - Agent got blocked"
                        ],
                        [
                            "RouteScenario_17",
                            1,
                            "Failed - Agent got blocked"
                        ],
                        [
                            "RouteScenario_18",
                            2,
                            "Failed - Agent got blocked"
                        ],
                        [
                            "RouteScenario_20",
                            4,
                            "Failed - Agent got blocked"
                        ],
                        [
                            "RouteScenario_21",
                            5,
                            "Failed - Agent got blocked"
                        ],
                        [
                            "RouteScenario_22",
                            6,
                            "Failed - Agent got blocked"
                        ],
                        [
                            "RouteScenario_23",
                            7,
                            "Failed - Agent got blocked"
                        ],
                        [
                            "RouteScenario_24",
                            8,
                            "Failed - Agent got blocked"
                        ],
                        [
                            "RouteScenario_25",
                            9,
                            "Failed - Agent got blocked"
                        ]
                    ]
                },
    

    I am wondering that what's the relationship between Scenario and RouteScenario?

    1. I evaluated the pre-trained model and a model trained by myself without any modification on your code. The results are:

    Result of model_ckpt/geometric_fusion (pretrained)

    Result of model_ckpt/transfuser (pretrained)

    Result of transfuser (self-trained)

    I think there is something wrong, because they are significantly poorer than the results in your paper. Could you find any possible mistakes?

    Evaluation script used for pre-trained transfuser model:

    #!/bin/bash
    
    export CARLA_ROOT=carla
    export CARLA_SERVER=${CARLA_ROOT}/CarlaUE4.sh
    export PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI
    export PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI/carla
    export PYTHONPATH=$PYTHONPATH:$CARLA_ROOT/PythonAPI/carla/dist/carla-0.9.10-py3.7-linux-x86_64.egg
    export PYTHONPATH=$PYTHONPATH:leaderboard
    export PYTHONPATH=$PYTHONPATH:leaderboard/team_code
    export PYTHONPATH=$PYTHONPATH:scenario_runner
    
    export LEADERBOARD_ROOT=leaderboard
    export CHALLENGE_TRACK_CODENAME=SENSORS
    export PORT=2000 # same as the carla server port
    export TM_PORT=8000 # port for traffic manager, required when spawning multiple servers/clients
    export DEBUG_CHALLENGE=0
    export REPETITIONS=1 # multiple evaluation runs
    export ROUTES=leaderboard/data/evaluation_routes/routes_town05_long.xml
    export TEAM_AGENT=leaderboard/team_code/transfuser_agent.py
    export TEAM_CONFIG=model_ckpt/transfuser
    export CHECKPOINT_ENDPOINT=results/transfuser_result_1203_V2.json
    export SCENARIOS=leaderboard/data/scenarios/town05_all_scenarios.json
    export SAVE_PATH=data/expert_TF1203_V2 # path for saving episodes while evaluating
    export RESUME=True
    
    
    python3 ${LEADERBOARD_ROOT}/leaderboard/leaderboard_evaluator.py \
    --scenarios=${SCENARIOS}  \
    --routes=${ROUTES} \
    --repetitions=${REPETITIONS} \
    --track=${CHALLENGE_TRACK_CODENAME} \
    --checkpoint=${CHECKPOINT_ENDPOINT} \
    --agent=${TEAM_AGENT} \
    --agent-config=${TEAM_CONFIG} \
    --debug=${DEBUG_CHALLENGE} \
    --record=${RECORD_PATH} \
    --resume=${RESUME} \
    --port=${PORT} \
    --trafficManagerPort=${TM_PORT}
    
    opened by Co1lin 21
  • Questions about evaluation

    Questions about evaluation

    Hi, I want to ask you for help. When I run the run_evaluation.sh to evaluate the method, I just stuck in the step 'Loading the world' for a long time. When it finally displayed the frames, the screen is very slow and the script will interrupt when the ego-agent into the bend. The following is the fault code. image

    `========= Preparing RouteScenario_16 (repetition 0) =========

    Setting up the agent routes_town05_long_03_17_20_35_11 Loading the world Skipping scenario 'Scenario4' due to setup error: list index out of range Running the route leaderboard/team_code/aim_agent.py:169: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:180.) rgb = torch.from_numpy(scale_and_crop_image(Image.fromarray(tick_data['rgb']), scale=self.config.scale, crop=self.config.input_resolution)).unsqueeze(0) /home/watson/anaconda3/envs/carla10/lib/python3.7/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.) return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode) /home/watson/methods/transfuser_1/leaderboard/scripts/run_evaluation.sh: line 38: 22229 Segmentation fault (core dumped) python3 ${LEADERBOARD_ROOT}/leaderboard/leaderboard_evaluator.py --scenarios=${SCENARIOS} --routes=${ROUTES} --repetitions=${REPETITIONS} --track=${CHALLENGE_TRACK_CODENAME} --checkpoint=${CHECKPOINT_ENDPOINT} --agent=${TEAM_AGENT} --agent-config=${TEAM_CONFIG} --debug=${DEBUG_CHALLENGE} --record=${RECORD_PATH} --resume=${RESUME} --port=${PORT} --trafficManagerPort=${TM_PORT} `

    And this is the run_evaluation.sh I changed.

    #!/bin/bash

    export CARLA_ROOT=carla/CARLA_0.9.10.1 export CARLA_SERVER=${CARLA_ROOT}/CarlaUE4.sh export PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI export PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI/carla export PYTHONPATH=$PYTHONPATH:$CARLA_ROOT/PythonAPI/carla/dist/carla-0.9.10-py3.7-linux-x86_64.egg export PYTHONPATH=$PYTHONPATH:leaderboard export PYTHONPATH=$PYTHONPATH:leaderboard/team_code export PYTHONPATH=$PYTHONPATH:scenario_runner

    export LEADERBOARD_ROOT=/home/watson/methods/transfuser_1/leaderboard export CHALLENGE_TRACK_CODENAME=SENSORS export PORT=2000 # same as the carla server port export TM_PORT=8000 # port for traffic manager, required when spawning multiple servers/clients export DEBUG_CHALLENGE=0 export REPETITIONS=1 # multiple evaluation runs export ROUTES=leaderboard/data/evaluation_routes/routes_town05_long.xml export TEAM_AGENT=leaderboard/team_code/aim_agent.py # agent export TEAM_CONFIG=/home/watson/methods/transfuser_1/aim/log/aim # model checkpoint, not required for expert export CHECKPOINT_ENDPOINT=results/aim_result.json # results file export SCENARIOS=leaderboard/data/scenarios/town05_all_scenarios.json export SAVE_PATH=data/expert # path for saving episodes while evaluating export RESUME=True

    python3 ${LEADERBOARD_ROOT}/leaderboard/leaderboard_evaluator.py
    --scenarios=${SCENARIOS}
    --routes=${ROUTES}
    --repetitions=${REPETITIONS}
    --track=${CHALLENGE_TRACK_CODENAME}
    --checkpoint=${CHECKPOINT_ENDPOINT}
    --agent=${TEAM_AGENT}
    --agent-config=${TEAM_CONFIG}
    --debug=${DEBUG_CHALLENGE}
    --record=${RECORD_PATH}
    --resume=${RESUME}
    --port=${PORT}
    --trafficManagerPort=${TM_PORT}

    opened by Watson52 19
  • can not evaluate auto pilot or transfuser

    can not evaluate auto pilot or transfuser

    i'm following the steps described here , yet im getting this when i try to evaluate , would you please help me understand ?

    /leaderboard/leaderboard/leaderboard_evaluator_local.py:89: DeprecationWarning: distutils Ver sion classes are deprecated. Use packaging.version instead.
    if LooseVersion(dist.version) < LooseVersion('0.9.10'):

    Registering the global statistics

    and then it exit execution

    opened by shenzo-ai 18
  • ./local_evaluation.sh

    ./local_evaluation.sh

    when i use ./local_evaluation.sh,it stops automatically. `89: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead. if LooseVersion(dist.version) < LooseVersion('0.9.10'):

    Registering the global statistics` and then it stops.

    opened by yanzhaohui1124 17
  • Some questions about hyperparameters

    Some questions about hyperparameters

    Hi! First of all I love the work you've with the TransFuser project, it's really quite interesting and I've had fun diving into autonomous driving research with CARLA thanks to you and your team, so thank you for that! Currently I am trying to replicate your results and also tune the hyperparameters of the transfuser model so I have a few questions with respect to that.

    1. I see that in all of the train.py scripts for all of the models, the --epochs parameter is set to a default value of 101 except for CILRS which is set to 201, however in the supplementary material you mention that all of the models were trained for 101 epochs. Was the CILRS model really trained at 201 epochs or why is it the only model set to that default value?

    2. In the README for each model you give an example of how to train that model, for example, CUDA_VISIBLE_DEVICES=<gpu_id> python3 train.py --id transfuser --batch_size 56 and CUDA_VISIBLE_DEVICES=<gpu_id> python3 train.py --id late_fusion --batch_size 128 I see that for different models you provide different values for --batch_size in those examples, however the default values are all set to 24 if I'm not mistaken. What batch size did you use for producing the results found in your main paper? Did you use the same values for all of the models or, like in the examples provided, use different values for different models?

    3. During the training process, the model validates itself every 5 epochs, is this so that it can autostop if there's no improvement after a certain number of epochs or what is the reason for this validation? If this is the case, then there wouldn't be much of a need to tune the epochs parameters, correct?

    4. From what I can tell, the main tunable parameters when calling the train.py script are --epochs, --lr (learning rate), and --batch_size. Do you have any suggestions/recommendations as to how I can go about trying to tune these? Also, are there any other parameters that I didn't mention that could also be fun to experiment with to try to optimize the transfuser model?

    opened by gitped 15
  • Ask for help about eval own trained model.

    Ask for help about eval own trained model.

    Why do I use the dataset (210G) you provided to train the model by myself, during test phase on the Carla , the ego car can't go straight on the road but just make a turn and run out of the road? In the training phase, we try to train 'only_wp'=1 and set --setting with both 'all' and '02_05_withheld'. I use one GPU 3090 to train the model and set epoch for 41, batch_size=32, other settings are default. I am very confused with this phenomenon and appreciate if you could provide some idea for me.

    opened by 13629281511 13
  • About data generation

    About data generation

    Hello! I have some problems about the data generation. My operation process is:

    1. Running CARLA Server image
    2. ./leaderboard/scripts/run_evaluation.sh image This seems that data generation is not successful. I would like to ask if the above operation is correct, and what should I do if there is a problem. And I understand that some images should be generated in the end, where will they be stored? I would be very grateful if you could reply.
    opened by XiangTodayEatsWhat 13
  • I found that the lidar input of the network model seems to be wrong

    I found that the lidar input of the network model seems to be wrong

    I ran this set of codes and found that the lidar point cloud would flicker during the process of displaying the point cloud input. After careful exploration, it seems that the slow rotation frequency of the lidar sensor causes the input of the point cloud to alternate between one frame before and one frame after. I think this is wrong. In order to verify it, I tried without laser point cloud input. The effect of the network model does not seem to be affected, which is very unreasonable.I doubled the rotation frequency and this phenomenon will be corrected. Can you explain it to me? Did I make a mistake somewhere?

    opened by HongYegg 12
  • data for training

    data for training

    Hi When I trying to generate the data with different scenerios, I have some questions about the dataset.

    1. I found the 14_weathers_minimal_data is divide by routes in differenct Town and one route includes kinds of scenarios. Could I divide the dataset by scenario? I mean that there is just one kind of scenario occur in one route. Does it will affect the result of training?
    2. There are 3 types of routes: long/short/tiny. I would like to know how the training result will happen without 'long' routes. Or even just have 'tiny' routes, will it make a different to the result?

    Looking forward to your help.

    opened by Watson52 11
  • Hello, I want to know what ‘3 training seeds’ mean, can you talk about it in detail?

    Hello, I want to know what ‘3 training seeds’ mean, can you talk about it in detail?

    We report the mean and standard deviation over 9 runs of each method (3 training seeds, each trained model evaluated 3 times) on Route Completion (RC) and Driving Score (DS) in Town05 Short and Town05 Long settings with scenario and without scenarios. LF: Late Fusion, GF: Geometric Fusion, TF: TransFuser

    Hello, I want to know what ‘3 training seeds’ mean, can you talk about it in detail?

    opened by HongYegg 11
  • Where is the Auxiliary task

    Where is the Auxiliary task

    Thanks for your great work. I have some problems about the auxiliary task. I don't find the auxiliary loss in your train.py of the transfuser. Could you point how to use the auxiliary task?

    opened by raozhongyu 10
  • training weather

    training weather

    Hello

    Thank you for your great repository. I was wondering which weathers did you use to train your model in the paper? Is it only clearNoon weather similar to validation dataset?

    Thank You

    opened by mmahdavian 0
  • Some problems with the evaluation on leaderboard

    Some problems with the evaluation on leaderboard

    I was trying to submit the transfuser model to the CARLA Leaderboard, and the submission says it was pending as it uses computes and never progresses. I tested the image on a local docker container and found that run_evaluation.sh times out as the client cannot connect to the server. Normally, I run the server on a different terminal. When I try to do this on the container, make_docker.sh does not copy the ./CarlaUE4.sh to the image, so we don't have a usual way to run the CARLA server on the container. Can someone lend more details on how to connect/run the server on the docker container, or is if someone faced a similar issue?

    opened by Sou0602 1
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