# Large Car-following Data Based on Lyft level-5 Open Dataset: Following Autonomous Vehicles vs. Human-driven Vehicles

arXiv: https://arxiv.org/abs/2305.18921 · HTML: https://mlsanity.com/paper/2305.18921/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2023.

## Built on by (most cited first, 8 of 8)

- [UniUGP: Unifying Understanding, Generation, and Planing For End-to-end Autonomous Driving](https://mlsanity.com/paper/2512.09864/) (2025)
- [FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Prediction](https://mlsanity.com/paper/2411.16747/) (2024)
- [Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark](https://mlsanity.com/paper/2412.19944/) (2024)
- [Interaction Dataset of Autonomous Vehicles with Traffic Lights and Signs](https://mlsanity.com/paper/2501.12536/) (2025)
- [MonoCT: Overcoming Monocular 3D Detection Domain Shift with Consistent Teacher Models](https://mlsanity.com/paper/2503.13743/) (2025)
- [Automated Vehicles at Unsignalized Intersections: Safety and Efficiency Implications of Mixed Human and Automated Traffic](https://mlsanity.com/paper/2410.12538/) (2024)
- [Benchmarking Tesla's Traffic Light and Stop Sign Control: Field Dataset and Behavior Insights](https://mlsanity.com/paper/2512.11802/) (2025)
- [MOTOR: A Multimodal Dataset for Two-Wheeler Rider Behavior Understanding](https://mlsanity.com/paper/2605.22550/) (2026)

## Abstract (arXiv)

Car-Following (CF), as a fundamental driving behaviour, has significant influences on the safety and efficiency of traffic flow. Investigating how human drivers react differently when following autonomous vs. human-driven vehicles (HV) is thus critical for mixed traffic flow. Research in this field can be expedited with trajectory datasets collected by Autonomous Vehicles (AVs). However, trajectories collected by AVs are noisy and not readily applicable for studying CF behaviour. This paper extracts and enhances two categories of CF data, HV-following-AV (H-A) and HV-following-HV (H-H), from the open Lyft level-5 dataset. First, CF pairs are selected based on specific rules. Next, the quality of raw data is assessed by anomaly analysis. Then, the raw CF data is corrected and enhanced via motion planning, Kalman filtering, and wavelet denoising. As a result, 29k+ H-A and 42k+ H-H car-following segments are obtained, with a total driving distance of 150k+ km. A diversity assessment shows that the processed data cover complete CF regimes for calibrating CF models. This open and ready-to-use dataset provides the opportunity to investigate the CF behaviours of following AVs vs. HVs from real-world data. It can further facilitate studies on exploring the impact of AVs on mixed urban traffic.