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This is the code for my BU EC500 (Spring 2023) course project report **Autonomous Driving using Spiking Neural Networks on Dynamic Vision Sensor Data: A Case Study of Traffic Light Change Detection **.

You can find the project report here.

If you find the code useful, please star this repo. Thank you!

Run the following commands one by one to create a conda environment and install related libraries

conda create -n snn python=3.8

conda activate snn

pip install spikingjelly

conda install pandas

Run the following commands to create a copy of this repository in your local system.

git clone https://github.com/xueleichen/snn-dvs-carla.git

cd snn-dvs-carla

The code has been tested with:

• Ubuntu 20.04.4 LTS
• CUDA Tookit 11.1
• CUDNN 8.4.0
• Nvidia RTX 3060

Note: There is a environment.yml file in this repository, which records the library versions that I used. You can also quickly set up using the following one-line command:

conda env create -f environment.yml

DVS data are stored in the ./data folder. RGB data are stored in the ./rgb_data folder.

./*.txt files store training testing data path and labels.

I designed three configurations for experiments in this project: SNN with DVS data, CNN with DVS data, and CNN with RGB data.

When you run the following three commands, you will get three result curves in my report.

python main_SNN.py

python main_CNN.py

python main_CNN_Image.py