This repository contains scripts to download experiment data, to reproduce figures, and instructions on how to reproduce experiments for D3: A Dynamic Deadline-Driven Approach for Building Autonomous Vehicles, which is to appear at EuroSys 2022.

Downloading log data of experiments and generating graphs

./download_data.sh
# Verify that the experiments directory contains log data of our experiments.
# Install dependencies required to run the plotting scripts.
pip3 install -r requirements.txt
cd plotting_scripts
./generate_plots.sh
# The graphs will be generated in plotting_scripts/graphs.

Executing experiments

Executing system experiments (Figure 8)

We benchmark ERDOS's performance against ROS, a robotics middleware which has been used to develop AVs, and Flink, a streaming system that provides similar time management features to ERDOS (e.g., timestamping and watermarks).

We measure performance for several common communication patterns for both inter-process and intra-process communication, including:

Sending messages of various sizes (Figure 8a)
Broadcasting messages to varying numbers of receivers (Figure 8b)
Scaling up an emulated AV pipeline (Figure 8c)

Setup instructions

To run the experiments, ensure that the following programs are installed:

Rust
Pandas python library
Maven
Docker
docker-compose

To install these applications, run

bash systems-experiments/scripts/install_dependencies.sh

Executing systems experiments

ERDOS

To run all ERDOS experiments, run

cd systems-experiments/erdos-experiments
bash scripts/run_all.sh

The results are stored as CSVs under systems-experiments/erdos-experiments/results/$(hostname)/.

For more information, see the ERDOS experiments README.

Flink

To run all Flink experiments, run

cd systems-experiments/flink-experiments
bash scripts/run_all.sh

The results are stored as CSVs under systems-experiments/flink-experiments/results/$(hostname)/.

For more information, see the Flink experiments README.

ROS

To run all ROS experiments, run

cd systems-experiments/ros-experiments
bash scripts/run_all.sh

The results are stored as CSVs under systems-experiments/ros-experiments/results/$(hostname)/.

For more information, see the ROS experiments README.

Plotting message size vs. latency (Figure 8a)

Generate the graph by executing the following commands from the current folder:

cd ${ERDOS_EXPERIMENTS_HOME}/plotting_scripts
HOST=$(hostname)   # Set to another value if generating plots on a different machine
python3 plot_msg_size_latency.py \
    "../eurosys_systems_experiments/$HOST/erdos/msg-size-latency-intra-process.csv" \
    "../eurosys_systems_experiments/$HOST/flink/msg-size-latency-intra-process.csv" \
    "../eurosys_systems_experiments/$HOST/erdos/msg-size-latency-inter-process.csv" \
    "../eurosys_systems_experiments/$HOST/flink/msg-size-latency-inter-process.csv" \
    "../eurosys_systems_experiments/$HOST/ros/msg-size-latency-inter-process.csv"

Plotting broadcast latency (Figure 8b)

Generate the graph by executing the following commands from the current folder:

cd ${ERDOS_EXPERIMENTS_HOME}/plotting_scripts
HOST=$(hostname)   # Set to another value if generating plots on a different machine
python3 plot_broadcast_latency.py \
    "../eurosys_systems_experiments/$HOST/erdos/broadcast-latency-intra-process.csv" \
    "../eurosys_systems_experiments/$HOST/flink/broadcast-latency-intra-process.csv" \
    "../eurosys_systems_experiments/$HOST/erdos/broadcast-latency-inter-process.csv" \
    "../eurosys_systems_experiments/$HOST/flink/broadcast-latency-inter-process.csv" \
    "../eurosys_systems_experiments/$HOST/ros/broadcast-latency-inter-process.csv"

Plotting synthetic pipeline latency

Generate the graph by executing the following commands from the current folder:

cd ${ERDOS_EXPERIMENTS_HOME}/plotting_scripts
HOST=$(hostname)   # Set to another value if generating plots on a different machine
python3 plot_synthetic_pipeline.py \
    "../eurosys_systems_experiments/$HOST/erdos/synthetic-pipeline-intra-process.csv" \
    "../eurosys_systems_experiments/$HOST/erdos/synthetic-pipeline-intra-process.csv"

Executing autonomous driving experiments

Configuring ERDOS, Pylot, and the CARLA simulator

In order to evaluate the efficacy of ERDOS, we deveploed Pylot, an autonomous vehicle that runs both in simulation and on real-vehicles. The easiest way to get Pylot running is to use our Docker image. Please ensure you have nvidia-docker on your machine before you start installing Pylot. In case you do not have nvidia-docker please download install-nvidia-docker.sh and execute it.

We provide a Docker image with ERDOS, Pylot and CARLA simulator already setup.

docker pull erdosproject/pylot:v0.3.2
nvidia-docker run -itd --name pylot -p 20022:22 erdosproject/pylot /bin/bash

To test if the image runs on your hardware, please start the simulator in the container:

nvidia-docker exec -i -t pylot /home/erdos/workspace/pylot/scripts/run_simulator.sh

In another terminal, start Pylot using the container:

nvidia-docker exec -i -t pylot /bin/bash
cd ~/workspace/pylot/
# Execute a version of Pylot that uses a Faster-RCNN object detector.
python3 pylot.py --flagfile=configs/detection.conf

Following, you can inspect ~/workspace/pylot/pylot.log to see if the simulator and Pylot are progressing. Moreover, you can visualize the outputs of different components (e.g., bounding boxes for detected objects) by forwarding X from the container. In order to do so, you have to first add your public ssh key to the ~/.ssh/authorized_keys in the container:

nvidia-docker cp ~/.ssh/id_rsa.pub pylot:/home/erdos/.ssh/authorized_keys
nvidia-docker exec -i -t pylot sudo chown erdos /home/erdos/.ssh/authorized_keys
nvidia-docker exec -i -t pylot sudo service ssh start

Finally, ssh into the container with X forwarding:

ssh -p 20022 -X erdos@localhost
cd /home/erdos/workspace/pylot/
python3 pylot.py --flagfile=configs/detection.conf --visualize_detected_obstacles

If everything worked ok, you should be able to see a visualization like the one below:

Executing experiments using complex driving scenarios

Hardware requirements: all experiments were performed on a machine having 2 x Xeon Gold 6226 CPUs, 128GB of RAM, and 2 x 24GB Titan-RTX GPUs, running Linux Kernel 5.3.0.

Important note: executing the experiments on different hardware would could significantly affect the results as any differences in end-to-end response would cause the simulation experiments to diverse from the simulations we executed (e.g., an increased response time might cause an early collision, but avoid later collissions due to divergence between simulations).

We highlight that due to limitations in the simulator's performance, executing 1 second of simulation takes approximately 10-30 wallclock time seconds. Thus, in order to address the issue that executing our experiments takes approximately a month, we provide the logs of all our experiments together with scripts to analyze them and re-generate all our figures:

./download_data.sh
# Verify that the experiments directory contains log data of our experiments.
# Install dependencies required to run the plotting scripts.
pip3 install -r requirements.txt
cd plotting_scripts
./generate_plots.sh
# The graphs will be generated in plotting_scripts/graphs.

If you want to replicate our experiments, please ensure that you have updated Pylot and are using the deadlines branch in the ERDOS repository. You can do this by running the following commands:

# Connect to the Pylot container.
nvidia-docker exec -i -t pylot /bin/bash
cd ~/workspace/pylot ; git pull ; git checkout de30da016cd406369b782054c3de6acec45a02d7
cd ~/workspace/pylot/dependencies/frenet_optimal_trajectory_planner ; git pull ; rm -r build ; ./build.sh
cd ~/worskpace ; rm -rf leaderboard ; git clone https://github.com/erdos-project/leaderboard.git
cd ~/workspace/erdos
pip3 uninstall erdos
git pull
git checkout -b deadlines origin/deadlines
python3 python/setup.py install --user

Measuring the adaptability of the detection and planning components (Figure 9)

In order to measure how well the detection and planning components adapt to frequent deadline changes you have to execute the following commands:

# Connect to the Pylot container.
nvidia-docker exec -i -t pylot /bin/bash
cd ~/workspace/pylot/
# Checkout the branch, which contains the code to conduct frequent mode changes.
git checkout -b mode_changes origin/mode_changes
cd ~/workspace/
# Run Pylot without a policy that randomly adjusts the deadlines of the detection and planning components.
./leaderboard/scripts/run_mode_change_experiment.sh

The experiment will generates logs in ~/workspace/logs_mode_change_town_1_route_2_timely_True_run_1, which can be plotted by running the following commands outside the container:

mkdir -p ${ERDOS_EXPERIMENTS_HOME}/reproduced_experiments/mode_changes
docker cp pylot:/home/erdos/workspace/logs_mode_change_town_1_route_2_timely_True_run_1 ${ERDOS_EXPERIMENTS_HOME}/reproduced_experiments/mode_changes
cd ${ERDOS_EXPERIMENTS_HOME}/plotting_scripts
python3 plot_mode_changes.py --base_dir=../reproduced_experiments/mode_changes --file_format=pdf --paper_mode

Measuring the overhead introduced by a no-op policy

This experiment compares the response time of Pylot configured without a deadline policy to the response time of Pylot with a no-op policy. The goal of the experiment is to measure the overhead introduced by the mechanism for implementing policies. In order to conduct the experiment, you have to run:

# Connect to the Pylot container.
nvidia-docker exec -i -t pylot /bin/bash
cd ~/workspace/pylot
# Checkout the branch, which contains the Pylot code without a policy.
git checkout -b no-ttd origin/no-ttd
cd ~/workspace/
# Run Pylot without a policy for adjusting deadlines.
./leaderboard/scripts/run_nopolicy_experiments.sh
cd ~/workspace/pylot
# Checkout the branch, which contains the Pylot code with a no-op policy.
git checkout -b deadlines origin/deadlines
cd ~/workspace/
# Run Pylot with a no-op policy.
./leaderboard/scripts/run_policy_experiments.sh dedicated
mkdir -p ~/workspace/reproduced_experiments/challenge/comparison_w_and_wo_policy
mv ~/workspace/logs_*policy* ~/workspace/reproduced_experiments/challenge/comparison_w_and_wo_policy/

Next, generate the graphs and response time statistics by running the following commands:

# In another terminal, copy the logs from the container.
mkdir -p ${ERDOS_EXPERIMENTS_HOME}/reproduced_experiments/challenge/comparison_w_and_wo_policy/
docker cp pylot:/home/erdos/workspace/reproduced_experiments/challenge/comparison_w_and_wo_policy ${ERDOS_EXPERIMENTS_HOME}/reproduced_experiments/challenge/
cd ${ERDOS_EXPERIMENTS_HOME}/plotting_scripts
# Execute script that will analyze the log and print out the distribution of the end-to-end response time.
python3 plot_challenge_policy.py --base_dir=../reproduced_experiments/challenge/comparison_w_and_wo_policy/ --file_format=pdf --small_paper_mode --plot_runtimes --towns=1 --start_route=1 --end_route=9 --num_reps=5 --verbose

Measuring the effect of deadline exception handlers on response time (Figure 10)

In this experiment, the self-driving car drives in a 70km route with static deadlines per-component. The goal of the experiment is to show that the deadline exception handlers execute quickly, and thus ensure that the components always send output shortly after their deadline. Please run the following commands in order to execute the experiment:

# Connect to the Pylot container.
nvidia-docker exec -i -t pylot /bin/bash
cd ~/workspace/pylot/
git checkout -b deadlines origin/deadlines
cd ~/workspace/
# Run Pylot with static deadlines.
./leaderboard/scripts/run_deadline_experiments.sh dedicated
mkdir -p ~/workspace/reproduced_experiments/challenge/deadlines_comparison_with_accurate_lidar
mv ~/workspace/logs_deadlines_* ~/workspace/reproduced_experiments/challenge/deadlines_comparison_with_accurate_lidar

Following, execute Pylot using the data-driven execution model, which does not enforce deadlines:

# Connect to the Pylot container.
nvidia-docker exec -i -t pylot /bin/bash
cd ~/workspace/pylot/
git checkout -b no-deadlines origin/no-deadlines
cd ~/workspace/
# Run Pylot in data-driven execution model (i.e., without deadlines).
./leaderboard/scripts/run_timely_experiments.sh
mv ~/workspace/logs_town_* ~/workspace/reproduced_experiments/challenge/deadlines_comparison_with_accurate_lidar

Next, generate the graph by executing the following commands outside of the container:

# Copy the logs from the container.
mkdir -p ${ERDOS_EXPERIMENTS_HOME}/reproduced_experiments/challenge/deadlines_comparison_with_accurate_lidar
docker cp pylot:/home/erdos/workspace/reproduced_experiments/challenge/deadlines_comparison_with_accurate_lidar ${ERDOS_EXPERIMENTS_HOME}/reproduced_experiments/challenge/
cd ${ERDOS_EXPERIMENTS_HOME}/plotting_scripts
python3 plot_challenge_deadline_paper.py --base_dir=../experiments/challenge/deadlines_comparison_with_accurate_lidar/ --file_format=png --paper_mode --towns=1 --start_route=1 --end_route=9 --num_reps=7 --file_format=pdf --small_paper_mode

Measuring collisions while using different execution models (Figure 11)

This experiment compares the periodic and data-driven execution models with the executions enforcing static deadlines and dynamic deadlines. The logs for the data-driven and static deadlines execution models were already generated in the experiment measuring the effect of deadline exception handlers (see here in case you need to run the experiment).

Please run the following commands in order to generate the logs of Pylot running atop of the periodic execution model:

# Connect to the Pylot container.
nvidia-docker exec -i -t pylot /bin/bash
cd ~/workspace/pylot/
git checkout -b frequency-execution origin/frequency-execution
cd ~/workspace/
# Run Pylot using a frequency-driven execution model.
./leaderboard/scripts/run_frequency_experiments.sh
mv ~/workspace/logs_frequency_* ~/workspace/reproduced_experiments/challenge/deadlines_comparison_with_accurate_lidar

Following, please run the following commands in order to obtain the logs generated by Pylot executing using a simple policy that dynamically adapts deadlines:

# Connect to the Pylot container.
nvidia-docker exec -i -t pylot /bin/bash
cd ~/workspace/pylot/
git checkout -b deadlines-ttd origin/deadlines-ttd
cd ~/workspace/
# Run Pylot with a simple deadline allocation policy.
./leaderboard/scripts/run_policy_experiments.sh dedicated
mv ~/workspace/logs_policy_* ~/workspace/reproduced_experiments/challenge/deadlines_comparison_with_accurate_lidar

Next, generate collision statistics and graphs by running the following commands:

# Copy the logs from the container.
mkdir -p ${ERDOS_EXPERIMENTS_HOME}/reproduced_experiments/challenge/deadlines_comparison_with_accurate_lidar
docker cp pylot:/home/erdos/workspace/reproduced_experiments/challenge/deadlines_comparison_with_accurate_lidar ${ERDOS_EXPERIMENTS_HOME}/reproduced_experiments/challenge/
cd ${ERDOS_EXPERIMENTS_HOME}/plotting_scripts
# Generate collision statistics.
python3 generate_collision_stats.py --base_dir=../reproduced_experiments/challenge/deadlines_comparison_with_accurate_lidar/ --filter_carla_cola=True --towns=1 --start_route=1 --end_route=9 --num_reps=7
python3 plot_challenge_collisions_barchart.py

Generating the response time histograph from logged data (Figure 12)

Please run the following commands in order to generate the histogram comparing the end-to-end response time of Pylot's executions with static and dynamic deadlines:

cd ${ERDOS_EXPERIMENTS_HOME}/plotting_scripts
python3 plot_challenge_e2e_response_time.py --base_dir=../reproduced_experiments/challenge/deadlines_comparison/ --end_route=9 --num_reps=7 --file_format=pdf --small_paper_mode --plot_histogram

Measuring collision speed across across different scenarios (Figure 13)

Please run the following commands in order to reproduce the experiment:

# Connect to the Pylot container.
nvidia-docker exec -i -t pylot /bin/bash
cd ~/workspace/pylot/
git checkout 1518c0a14ff1dc66a3c2cfd38862ca468084ec5c
cd ~/workspace/
git clone https://github.com/erdos-project/erdos-experiments.git
cd ~/workspace/erdos-experiments/experiments/scenario_runner
# Run the Traffic Jam and Person Behind Truck scenarios using different Pylot configurations with static deadlines.
./run_static_deadlines_traffic_jam.sh
./run_static_deadlines_person_behind_car.sh
cd ~/workspace/pylot/
git checkout -b deadlines-ttd origin/deadlines-ttd
cd ~/workspace/erdos-experiments/experiments/scenario_runner
# Run the Traffic Jam and Person Behind Truck scenarios using different Pylot configurations with dynamic deadlines.
./run_dynamic_deadlines_traffic_jam.sh
./run_dynamic_deadlines_person_behind_car.sh

In another terminal, copy the log data from the container in to the experiments directory:

mkdir -p ${ERDOS_EXPERIMENTS_HOME}/reproduced_experiments/scenario_runner/scenarios_collision/
docker cp pylot:/home/erdos/workspace/pylot/traffic_jam* ${ERDOS_EXPERIMENTS_HOME}/reproduced_experiments/scenario_runner/scenarios_collision/
docker cp pylot:/home/erdos/workspace/pylot/person_behind_car* ${ERDOS_EXPERIMENTS_HOME}/reproduced_experiments/scenario_runner/scenarios_collision/

Generating timelines of the response time during a drive in a scenario (Figure 14)

Please execute the following script in order to reproduce the timelines comparing the end-to-end response time of the executions with static and dynamic deadlines:

cd ${ERDOS_EXPERIMENTS_HOME}/plotting_scripts
python3 plot_scenario_response_time_timeline.py --static_log_base=../reproduced_experiments/scenario_runner/person_behind_car_response_time/person_behind_car_detection_200_planning_309_target_speed_12_Hz_5 --dynamic_log_base=../reproduced_experiments/scenario_runner/person_behind_car_response_time/person_behind_car_dynamic_deadlines_target_speed_12_Hz_5

wonkyoc / erdos-experiments Goto Github PK

erdos-experiments's Introduction

Downloading log data of experiments and generating graphs

Executing experiments

Executing system experiments (Figure 8)

Setup instructions

Executing systems experiments

ERDOS

Flink

ROS

Plotting message size vs. latency (Figure 8a)

Plotting broadcast latency (Figure 8b)

Plotting synthetic pipeline latency

Executing autonomous driving experiments

Configuring ERDOS, Pylot, and the CARLA simulator

Executing experiments using complex driving scenarios

Measuring the adaptability of the detection and planning components (Figure 9)

Measuring the overhead introduced by a no-op policy

Measuring the effect of deadline exception handlers on response time (Figure 10)

Measuring collisions while using different execution models (Figure 11)

Generating the response time histograph from logged data (Figure 12)

Measuring collision speed across across different scenarios (Figure 13)

Generating timelines of the response time during a drive in a scenario (Figure 14)

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