# Identifying Driver Behaviors using Trajectory Features for Vehicle Navigation

arXiv: https://arxiv.org/abs/1803.00881 · HTML: https://mlsanity.com/paper/1803.00881/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2018.

## Built on by (most cited first, 5 of 5)

- [Principles and Guidelines for Evaluating Social Robot Navigation Algorithms](https://mlsanity.com/paper/2306.16740/) (2023)
- [TransDARC: Transformer-based Driver Activity Recognition with Latent Space Feature Calibration](https://mlsanity.com/paper/2203.00927/) (2022)
- [iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2306.06236/) (2023)
- [SG-LSTM: Social Group LSTM for Robot Navigation Through Dense Crowds](https://mlsanity.com/paper/2303.04320/) (2023)
- [Quantifying and Modeling Driving Styles in Trajectory Forecasting](https://mlsanity.com/paper/2503.04994/) (2025)

## Abstract (arXiv)

We present a novel approach to automatically identify driver behaviors from vehicle trajectories and use them for safe navigation of autonomous vehicles. We propose a novel set of features that can be easily extracted from car trajectories. We derive a data-driven mapping between these features and six driver behaviors using an elaborate web-based user study. We also compute a summarized score indicating a level of awareness that is needed while driving next to other vehicles. We also incorporate our algorithm into a vehicle navigation simulation system and demonstrate its benefits in terms of safer real-time navigation, while driving next to aggressive or dangerous drivers.