# Human-Vehicle Cooperation on Prediction-Level: Enhancing Automated Driving with Human Foresight

arXiv: https://arxiv.org/abs/2104.03019 · HTML: https://mlsanity.com/paper/2104.03019/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2021.

## Built on by (most cited first, 6 of 6)

- [Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions](https://mlsanity.com/paper/2112.11561/) (2021)
- [CoCAtt: A Cognitive-Conditioned Driver Attention Dataset](https://mlsanity.com/paper/2111.10014/) (2021)
- [Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmap](https://mlsanity.com/paper/2404.00019/) (2024)
- [Explainable deep learning improves human mental models of self-driving cars](https://mlsanity.com/paper/2411.18714/) (2024)
- [Navigating the Edge with the State-of-the-Art Insights into Corner Case Identification and Generation for Enhanced Autonomous Vehicle Safety](https://mlsanity.com/paper/2503.00077/) (2025)
- [CORTEX-AVD: A Framework for CORner Case Testing and EXploration in Autonomous Vehicle Development](https://mlsanity.com/paper/2504.03989/) (2025)

## Abstract (arXiv)

To maximize safety and driving comfort, autonomous driving systems can benefit from implementing foresighted action choices that take different potential scenario developments into account. While artificial scene prediction methods are making fast progress, an attentive human driver may still be able to identify relevant contextual features which are not adequately considered by the system or for which the human driver may have a lack of trust into the system's capabilities to treat them appropriately. We implement an approach that lets a human driver quickly and intuitively supplement scene predictions to an autonomous driving system by gaze. We illustrate the feasibility of this approach in an existing autonomous driving system running a variety of scenarios in a simulator. Furthermore, a Graphical User Interface (GUI) was designed and integrated to enhance the trust and explainability of the system. The utilization of such cooperatively augmented scenario predictions has the potential to improve a system's foresighted driving abilities and make autonomous driving more trustable, comfortable and personalized.