TUC-ProAut/HDC_driving_style_classification

VSA for driving behaviour classification

This repository is mainly based on the code of https://github.com/KhaledSaleh/driving_behaviour_classification

It has 3 different models:

• a LSTM model (original model from [1])
• a feed-forward model (ANN) for HDC encodings
• a spiking neural model (SNN) for HDC encodings

[1] K. Saleh, M. Hossny, and S. Nahavandi, “Driving behavior classification based on sensor data fusion using LSTM recurrent neural networks,” in International Conference on Intelligent Transportation Systems (ITSC), 2017.

Tested with the Python packages listed in requirements.txt.

Usage

• first, clone the Repo git clone https://github.com/TUC-ProAut/HDC_driving_style_classification.git

Train the networks (Python)

1. Run python3 main.py --help to check the available command line args.
2. Run ANN with HDC encodings:
   • python3 main.py --HDC_ANN True (use --dataset argument to select between full, motorway, secondary or full_crossval)
3. Run ANN with concatenated input sequences:
   • python3 main.py --Concat_ANN True
4. Run the original LSTM model from https://github.com/KhaledSaleh/driving_behaviour_classification
   • python3 main.py --LSTM True
5. Run SNN with HDC encodings:
   • python3 main.py --HDC_SNN True

The results are written to the log file logs/main_log.log

Data efficiency experiment (Python)

1. Run python3 main.py --data_efficiency True --HDC_ANN True for the appropriate network as in section above

(Optional) Hyper-parameter analysis for HDC encodings (Python)

1. Run python3 main.py --hyperparams_experiment True --HDC_ANN True

Run Baseline models (MATLAB)

1. Run eval_baseline_models.m