Minimising missed and false alarms: a vehicle spacing based approach to conflict detection

This study is presented at the 2024 IEEE Intelligent Vehicles Symposium (IV) and accessible at https://doi.org/10.1109/IV55156.2024.10588396.

Highlights

• Conflict detection involves a trade‐off between missed and false alarms.
• Probabilities of missed and false alarms are estimated from spacing distributions.
• Critical spacing is optimised to minimise missed and false alarms.
• Validation on synthetic and real-world conflicts confirms superior performance.
• Collision warning can be adaptive in varying traffic contexts and driver preferences.

In order to repeat the experiments:

Dependencies

jupyter notebook, numpy, pandas, pytables, tqdm, glob, matplotlib, scipy

Data

• Raw data
  Apply for the dataset CitySim, download and put the subset of FreewayB in the folder ./localdata/rawdata.
• Test data
  We have processed and saved the 100Car NDS data in the folder ./localdata/inputdata/. The readers are still encouraged to explore the raw data with the code in the repository if interested.
• Results
  Resulting data, i.e., a zipped file of the ./localdata/ folder, can be downloaded from https://doi.org/10.4121/252a79e7-d9ff-4181-a9e4-842ea7845a77.

Step-by-step instructions

• Step 1 Preprocess data

  • Step 1.1 Run ./Pre-processing/FreewayB_preprocessing.py to preprocess the CitySim FreewayB data.
  • Step 1.2 Run ./Pre-processing/HundredCar_preprocessing.py to preprocess the 100Car NDS data.

• Step 2 Run the experiments

  • Step 2.1 Run ./ConflictDetection/Sampling.py to determine conflicts and sample data for spacing inferences.
  • Step 2.2 Run ./ConflictDetection/Computing.py to compute pma and pfa at each time moment.

• Step 3 Produce and visualise results

  • Step 3.1 Use ./ResultsVisualisation/IEEE IV.ipynb to give results and visualise them for method validation.

Citation

@inproceedings{Jiao2024,
  title = {Minimising Missed and False Alarms: A Vehicle Spacing based Approach to Conflict Detection},
  doi = {10.1109/iv55156.2024.10588396},
  booktitle = {2024 IEEE Intelligent Vehicles Symposium (IV)},
  pages={1982-1987},
  author = {Jiao,  Yiru and Calvert,  Simeon C. and van Lint,  Hans},
  year = {2024},
  month = jun,
  address = {Jeju Island, Republic of Korea}
}