# A Cognitive-Driven Trajectory Prediction Model for Autonomous Driving in Mixed Autonomy Environment

arXiv: https://arxiv.org/abs/2404.17520 · HTML: https://mlsanity.com/paper/2404.17520/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2024.

## Built on by (most cited first, 10 of 10)

- [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)
- [AMD: Adaptive Momentum and Decoupled Contrastive Learning Framework for Robust Long-Tail Trajectory Prediction](https://mlsanity.com/paper/2507.01801/) (2025)
- [SafeCast: Risk-Responsive Motion Forecasting for Autonomous Vehicles](https://mlsanity.com/paper/2503.22541/) (2025)
- [Embodied Cognition Augmented End2End Autonomous Driving](https://mlsanity.com/paper/2511.01334/) (2025)
- [E3AD: An Emotion-Aware Vision-Language-Action Model for Human-Centric End-to-End Autonomous Driving](https://mlsanity.com/paper/2512.04733/) (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)
- [ScenePilot: Controllable Boundary-Driven Critical Scenario Generation for Autonomous Driving](https://mlsanity.com/paper/2605.21168/) (2026)

## Abstract (arXiv)

As autonomous driving technology progresses, the need for precise trajectory prediction models becomes paramount. This paper introduces an innovative model that infuses cognitive insights into trajectory prediction, focusing on perceived safety and dynamic decision-making. Distinct from traditional approaches, our model excels in analyzing interactions and behavior patterns in mixed autonomy traffic scenarios. It represents a significant leap forward, achieving marked performance improvements on several key datasets. Specifically, it surpasses existing benchmarks with gains of 16.2% on the Next Generation Simulation (NGSIM), 27.4% on the Highway Drone (HighD), and 19.8% on the Macao Connected Autonomous Driving (MoCAD) dataset. Our proposed model shows exceptional proficiency in handling corner cases, essential for real-world applications. Moreover, its robustness is evident in scenarios with missing or limited data, outperforming most of the state-of-the-art baselines. This adaptability and resilience position our model as a viable tool for real-world autonomous driving systems, heralding a new standard in vehicle trajectory prediction for enhanced safety and efficiency.