Dataset and code for IEEE IV21 paper Predicting Lane Change Decision Making with Compact Support
Data extraction process:
- Use extract_lane_change.py to extract lane change sequences from both dataset, and extract_lane_change_merge_after.py to extract lane change abortions.
- Use extract_sample.py and extract_sample_merge_after.py to extract the exact snapshot from the lane change sequences for the downstream prediction task.
- Use data_preprocess.py to assemble the extracted samples; split to training and testing; generate OOD samples.
| Dataset | Description | Generating program |
|---|---|---|
| us80.npz | Raw lane changes from US80, each row is one sample, with record [merge in front/after(0/1), u0, du0, du1, du2, dx0, dx1, dx2, dy0, dy1, dy2, y]. | extract_sample.py, extract_sample_merge_after.py, and preprocess_both_dataset() in data_preprocess.py |
| us101.npz | Raw lane changes from US101, each row is one sample, with record [merge in front/after(0/1), u0, du0, du1, du2, dx0, dx1, dx2, dy0, dy1, dy2, y] | extract_sample.py, extract_sample_merge_after.py, and preprocess_both_dataset() in data_preprocess.py |
| samples_relabeled_by_decrease_in_dx.npz | Samples with label based on |
extract_samples_relabel_as_change_in_distance() in extract_sample.py |
| combined_dataset.npz | Combine us80 and us101 datasets, do feature selection, and normalize them to have 0 mean and 1 std, in which f['a']=x_train, f['b'']=y_train, f['c']=x_test, f['d']=y_test, f['e']=xGenerated), each sample is of feature x = [dv0, dv1, dx0, dx1] | prepare_validate_and_generate_ood() in data_process.py |
| combined_dataset_before_feature_selection.npz | Combine us80 and us101 datasets and normalize them to have 0 mean and 1 std, in which f['a']=x_train, f['b'']=y_train, f['c']=x_test, f['d']=y_test), each sample is of feature x = [v_ego, dv0, dv1, dv2, dx0, dx1, dx2, dy0, dy1, dy2] | prepare_validate_and_feature_selection() in data_process.py |
| combined_dataset_trainUs80_testUs101.npz | us80 as training dataset, us101 as testing dataset. Normalize samples to have 0 mean and 1 std, in which f['a']=x_train, f['b'']=y_train, f['c']=x_test, f['d']=y_test, f['e']=xGenerated), each sample is of feature x = [dv0, dv1, dx0, dx1] | prepare_validate_and_generate_ood_trainUs80_testUs101() in data_process.py |
| combined_dataset_trainUs101_testUs80.npz | us101 as training dataset, us80 as testing dataset. Normalize samples to have 0 mean and 1 std, in which f['a']=x_train, f['b'']=y_train, f['c']=x_test, f['d']=y_test, f['e']=xGenerated), each sample is of feature x = [dv0, dv1, dx0, dx1] | prepare_validate_and_generate_ood_trainUs80_testUs101() in data_process.py |