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BSc Thesis: Deep Learning - Lane Change Recognition & Prediction

Using computer vision based deep learning approaches on the PREVENTION dataset (https://prevention-dataset.uah.es/) for the recognition and prediction of lane changing behaviour of surrounding vehicles in highways.

Report

For a detailed understanding of the research and methodologies employed in this project, you can refer to the report.

Downloading the dataset

To download the PREVENTION dataset, run the following command in the same directory as the file data_downloader.sh:

bash data_downloader.sh RECORD DRIVE

where RECORD is the number of the RECORD in the dataset and DRIVE is the number of the DRIVE of the record, both input as integers.

Lane Change / No Lane Change clip extraction

The dataset is obtained from preprocessing the 5 records that are in the PREVENTION dataset. From these records, small clips have been extracted that were used to train and test the implemented models. All the preprocessing related scripts can be found in the data_preprocessing folder.

Work flow for clip extraction with the scripts:

  1. Use LC_exractor.py to extract the lane change clips or NLC_extractor.py to extract the no lane change clips from the records.
  2. After manually checking and removing outliers from the dataset, use move_processed.py to move the files to another folder and label them as processed. the script assumes that all the same lane change clips are manually labeled as passed and put into a passed subfolder or all files are moved by putting variable move_all_files = True.
  3. After you have extracted and processed all (or some of) the needed clips, use clip_store.py to generate clip store csv files. The clip_store.csv file can be used to track the amount of clips extracted, and is used to split the data to test-train.

NOTE: both LC_extractor.py and NLC_extractor.py contain some variables that can be used to tune the extraction specifications.

Test-train split with data augmentation

The test-train split with data augmentation can be done with data_splitter.py. It uses 5 different data augmentation techniques on the original training data: horizontal flipping, gaussian noise, color jitter, random rotation, and random brightness increase/decrease. When splitting the data, the scripts assumes that all regions of interest (ROIs) have equal amount of data to be splitted so it can produce same splits for each ROI size.

Models

The details about the implemented models can be found in models.py file inside the models directory. The directory also contains a file helper_functions.py with useful functions used for the training and testing of the models. Four different models were implemented: R(2+1)D [2], MC4 [2], S3D[3] and ViViT [1].

The trained model weights and full dataset can be accessed through weights/model_weights.md.

Experiments

All the done experiments and their results with the implemented models can be found in notebooks.

References

[1] Anurag Arnab et al. "ViViT: A Video Vision Transformer". 2021. arXiv: 2103.15691.

[2] Du Tran et al. “A Closer Look at Spatiotemporal Convolutions for Action Recognition”. In: June 2018, pp. 6450–6459. DOI: 10.1109/CVPR.2018.00675.

[3] Saining Xie et al. "Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification". 2018. arXiv: 1712.04851.