# TrafficNet: An Open Naturalistic Driving Scenario Library

arXiv: https://arxiv.org/abs/1708.01872 · HTML: https://mlsanity.com/paper/1708.01872/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2017.

## Built on by (most cited first, 6 of 6)

- [FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling](https://mlsanity.com/paper/2306.05381/) (2023)
- [RiskBench: A Scenario-based Benchmark for Risk Identification](https://mlsanity.com/paper/2312.01659/) (2023)
- [TransFollower: Long-Sequence Car-Following Trajectory Prediction through Transformer](https://mlsanity.com/paper/2202.03183/) (2022)
- [Modeling driver's evasive behavior during safety-critical lane changes:Two-dimensional time-to-collision and deep reinforcement learning](https://mlsanity.com/paper/2209.15133/) (2022)
- [MetaFollower: Adaptable Personalized Autonomous Car Following](https://mlsanity.com/paper/2406.16978/) (2024)
- [Automatic Identification of Driving Maneuver Patterns using a Robust Hidden Semi-Markov Models](https://mlsanity.com/paper/2311.07527/) (2023)

## Abstract (arXiv)

The enormous efforts spent on collecting naturalistic driving data in the recent years has resulted in an expansion of publicly available traffic datasets, which has the potential to assist the development of the self-driving vehicles. However, we found that many of the attempts to utilize these datasets have failed in practice due to a lack of usability concern from the organizations that host these collected data. For example, extracting data associated with certain critical conditions from naturalistic driving data organized in chronological order may not be convenient for a vehicle engineer that doesn't have big data analytics experiences. To address the general usability challenges of these publicly available traffic datasets, we propose TrafficNet, a large-scale and extensible library of naturalistic driving scenarios, aiming at bridging the gap between research datasets and practically usable information for vehicle engineers and researchers. The proposed web-based driving scenario database preprocesses massive raw traffic data collected in chronological order into an organized scenario-based dataset by applying a set of categorization algorithms to label the naturalistic driving data with six different critical driving scenarios. TrafficNet opens not only the scenario library but also the source code of these categorization methods to the public, which will foster more sophisticated and accurate scenario-based categorization algorithms to advance the intelligent transportation research. The source code and the scenario database can be accessed at https://github.com/TrafficNet.