# Human Observation-Inspired Trajectory Prediction for Autonomous Driving in Mixed-Autonomy Traffic Environments

arXiv: https://arxiv.org/abs/2402.04318 · HTML: https://mlsanity.com/paper/2402.04318/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2024.

## Built on by (most cited first, 13 of 13)

- [A Cognitive-Based Trajectory Prediction Approach for Autonomous Driving](https://mlsanity.com/paper/2402.19251/) (2024)
- [MFTraj: Map-Free, Behavior-Driven Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/2405.01266/) (2024)
- [NEST: A Neuromodulated Small-world Hypergraph Trajectory Prediction Model for Autonomous Driving](https://mlsanity.com/paper/2412.11682/) (2024)
- [Minds on the Move: Decoding Trajectory Prediction in Autonomous Driving with Cognitive Insights](https://mlsanity.com/paper/2502.20084/) (2025)
- [Hypergraph-based Motion Generation with Multi-modal Interaction Relational Reasoning](https://mlsanity.com/paper/2409.11676/) (2024)
- [DRIVE: Dependable Robust Interpretable Visionary Ensemble Framework in Autonomous Driving](https://mlsanity.com/paper/2409.10330/) (2024)
- [Beyond Patterns: Harnessing Causal Logic for Autonomous Driving Trajectory Prediction](https://mlsanity.com/paper/2505.06856/) (2025)
- [SafeCast: Risk-Responsive Motion Forecasting for Autonomous Vehicles](https://mlsanity.com/paper/2503.22541/) (2025)
- [Addressing Corner Cases in Autonomous Driving: A World Model-based Approach with Mixture of Experts and LLMs](https://mlsanity.com/paper/2510.21867/) (2025)
- [Less is More: Efficient Brain-Inspired Learning for Autonomous Driving Trajectory Prediction](https://mlsanity.com/paper/2407.07020/) (2024)
- [Characterized Diffusion Networks for Enhanced Autonomous Driving Trajectory Prediction](https://mlsanity.com/paper/2411.16457/) (2024)
- [Towards Human-Like Trajectory Prediction for Autonomous Driving: A Behavior-Centric Approach](https://mlsanity.com/paper/2505.21565/) (2025)
- [SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving](https://mlsanity.com/paper/2607.09741/) (2026)

## Abstract (arXiv)

In the burgeoning field of autonomous vehicles (AVs), trajectory prediction remains a formidable challenge, especially in mixed autonomy environments. Traditional approaches often rely on computational methods such as time-series analysis. Our research diverges significantly by adopting an interdisciplinary approach that integrates principles of human cognition and observational behavior into trajectory prediction models for AVs. We introduce a novel "adaptive visual sector" mechanism that mimics the dynamic allocation of attention human drivers exhibit based on factors like spatial orientation, proximity, and driving speed. Additionally, we develop a "dynamic traffic graph" using Convolutional Neural Networks (CNN) and Graph Attention Networks (GAT) to capture spatio-temporal dependencies among agents. Benchmark tests on the NGSIM, HighD, and MoCAD datasets reveal that our model (GAVA) outperforms state-of-the-art baselines by at least 15.2%, 19.4%, and 12.0%, respectively. Our findings underscore the potential of leveraging human cognition principles to enhance the proficiency and adaptability of trajectory prediction algorithms in AVs. The code for the proposed model is available at our Github.