Identifying Driver Behaviors using Trajectory Features for Vehicle Navigation
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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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Mar 2018.
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Citing papers per year: 2022: 1; 2023: 3; 2024: 0; 2025: 1.
5 papers.
- 2023Principles and Guidelines for Evaluating Social Robot Navigation Algorithmscited by 56
- 2022TransDARC: Transformer-based Driver Activity Recognition with Latent Space Feature Calibrationcited by 25
- 2023iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learningcited by 5
- 2023SG-LSTM: Social Group LSTM for Robot Navigation Through Dense Crowdscited by 2
- 2025Quantifying and Modeling Driving Styles in Trajectory Forecastingcited by 1
- 2025Quantifying and Modeling Driving Styles in Trajectory Forecasting6 Mar 2025
- 2023Principles and Guidelines for Evaluating Social Robot Navigation Algorithms29 Jun 2023
- 2023iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning9 Jun 2023
- 2023SG-LSTM: Social Group LSTM for Robot Navigation Through Dense Crowds8 Mar 2023
- 2022TransDARC: Transformer-based Driver Activity Recognition with Latent Space Feature Calibration2 Mar 2022