Trajectory Prediction Correction

Datasets

Three datasets that were used can be downloaded from Brain4cars, CULane and NGSIM For Brain4Cars, videos are extracted into images with the fps=25 under each directory. The file name format is e.g. "image-0001.png". You can use the script extract_frames.py in datasets/annotation to extract images: Copy this file to the directory of "face_camera", and then run this script. For NGSIM, the raw data needs to be preprocessed from preprocess_data.m into .m files. For CULane, download the dataset and extract them to $CULANEROOT. Create link to data directory.

cd $CLRNET_ROOT
mkdir -p data
ln -s $CULANEROOT data/CULane

For CULane, you should have a structure like this:

$CULANEROOT/driver_xx_xxframe    # data folders x6
$CULANEROOT/laneseg_label_w16    # lane segmentation labels
$CULANEROOT/list                 # data lists

Algorithms

Lane Estimation

For training, run

python main.py [configs/path_to_your_config] --gpus [gpu_num]

For example, run

python main.py configs/clrnet/clr_resnet18_culane.py --gpus 0

For testing, run

python main.py [configs/path_to_your_config] --[test|validate] --load_from [path_to_your_model] --gpus [gpu_num]

For example, run

python main.py configs/clrnet/clr_dla34_culane.py --validate --load_from culane_dla34.pth --gpus 0

Currently, this code can output the visualization result when testing, just add --view. We will get the visualization result in work_dirs/xxx/xxx/visualization.

Intention Recognition

The dataset using 5-fold cross validaton. Run script n_fold_Brain4cars.py in directory datasets/annotation to split.

You can use the five .csv files in datasets/annotation and skip this step.

The main.py can be run on 3D-ResNet 50 to train the model. You can also continue training from the .pth in the directory. Some notes are:

root_path: path to this project.
annotation_path : path to annotation directory in this project.
video_path: path to image frames of driver videos.
pretrain_path: path to the pretrained 3D ResNet 50 model.
n_fold: is the number of the fold. Here, n_fold is from 0 to 4.
sample_duration: the length of input vidoes. Here, 16 frames.
end_second: the second before the maneuver. Here, end_second is from 1 to 5.

E.g. end_second = 3, frames which are 3 seconds (including the third second) before maneuver are given as input.

More details about other args, please refer to the opt.py.