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Modular autonomous driving platform running on the CARLA simulator and real-world vehicles.
| Date | Stars |
|---|---|
| 2026-07-24 | 535 |
| 2026-07-25 | 535 |
| 2026-07-28 | 535 |
| 2026-07-30 | 535 |
| 2026-08-06 | 535 |
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[](https://github.com/erdos-project/pylot/actions) [](https://pylot.readthedocs.io/en/latest/?badge=latest) Pylot is an autonomous vehicle platform for developing and testing autonomous vehicle components (e.g., perception, prediction, planning) on the CARLA simulator and real-world cars. * [**Setup instructions**](#setup-instructions) * [**Documentation**](https://pylot.readthedocs.io/en/latest/) * [**Pylot components**](#pylot-components) * [**Data collection**](#data-collection) * [**Build Docker image**](#build-your-own-docker-image) * [**CARLA autonomous driving challenge**](#carla-autonomous-driving-challenge) * [**Getting involved**](#getting-involved) # Setup instructions ## Deploy using Docker 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 run ```./scripts/install-nvidia-docker.sh``` We provide a Docker image with both Pylot and CARLA already setup. ```console docker pull erdosproject/pylot nvidia-docker run -itd --name pylot -p 20022:22 erdosproject/pylot /bin/bash ``` Following, start the simulator in the container: ```console nvidia-docker exec -i -t pylot /home/erdos/workspace/pylot/scripts/run_simulator.sh ``` Finally, start Pylot in the container: ```console nvidia-docker exec -i -t pylot /bin/bash cd ~/workspace/pylot/ python3 pylot.py --flagfile=configs/detection.conf ``` ## Visualizing components In case you desire to visualize outputs of different components (e.g., bounding boxes), you have to forward X from the container. First, add your public ssh key to the `~/.ssh/authorized_keys` in the container: ```console 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: ```console 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:  ## Manual installation instructions Alternatively, you can install Pylot on your base system by executing the following steps: ```console ./install.sh pip install -e ./ ``` Next, start the simulator: ```console export CARLA_HOME=$PYLOT_HOME/dependencies/CARLA_0.9.10.1/ ./scripts/run_simulator.sh ``` In a different terminal, setup the paths: ```console export CARLA_HOME=$PYLOT_HOME/dependencies/CARLA_0.9.10.1/ cd $PYLOT_HOME/scripts/ source ./set_pythonpath.sh ``` Finally, run Pylot: ```console cd $PYLOT_HOME/ python3 pylot.py --flagfile=configs/detection.conf ``` # Pylot components Pylot comprises of several components: obstacle detection, traffic light detection, lane detection, obstacle tracking, localization, segmentation, fusion, prediction, planners, and control. Each component is implemented using one or more ERDOS operators and can be executed in isolation or with the entire Pylot application. Please read the [**Documentation**](https://pylot.readthedocs.io/en/latest/) for a more in depth description.  Run the following command to see a demo of all the components, and the Pylot driving policy: ```console python3 pylot.py --flagfile=configs/demo.conf ``` The demo will execute: obstacle detection, traffic light detection, segmentation, prediction, planning, and the driving policy. *** You can also run components in isolation: ### Obstacle detection Pylot supports three object detection models: `frcnn_resnet101`, `ssd-mobilenet-fp
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:52f345225f02345d, topic:autonomous-driving, topic:self-driving-car, desc:autonomous driving