Awesome
A Kinematic Model for Trajectory Prediction in General Highway Scenarios
by Cyrus Anderson at UM FCAV
Introduction
This paper presents a novel kinematic model to predict vehicles' trajectories in general scenarios, including simultaneous lane changes by multiple vehicles and heavy occlusions. More details are given in the preprint at https://arxiv.org/abs/2103.16673.
Datasets
The NGSIM and highD datasets are used to evaluate the method, whose root folder should be set in utils.py
.
The default setup uses datasets
as a symbolic link:
baselines/
datasets/
|__tt_format
|__10hz
|__ngsim/
|__i80/
|__us101/
|__highd/
|__ngsim/
|__i-80/
|__us-101/
|__highd-dataset-v1.0/
where the raw datasets (NGSIM and highD) are converted to tt_format
for evaluation.
Conversion uses the tools in loading_utils/dataset_conversion.py
.
The un-formatted ngsim
folder contains the NGSIM dataset (dataset portal
and homepage).
The highd-dataset-v1.0
folder contains the highD dataset (homepage).
(Note: There may be small errors when first loading the NGSIM data due to small inconsistencies between the folder name formats/column names of the US-101 and I-80 datasets - manually changing them can solve this.)
Predict Trajectories
Predictions can be made by running
python driver.py
Dependencies
- NumPy
- SciPy
- pandas
- matplotlib