# Human-Based Risk Model for Improved Driver Support in Interactive Driving Scenarios

arXiv: https://arxiv.org/abs/2410.03774 · HTML: https://mlsanity.com/paper/2410.03774/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis](https://mlsanity.com/paper/2008.12085/) (2020)
- [Computer vision in automated parking systems: Design, implementation and challenges](https://mlsanity.com/paper/2104.12537/) (2021)
- [Human-Centered Autonomous Vehicle Systems: Principles of Effective Shared Autonomy](https://mlsanity.com/paper/1810.01835/) (2018)
- [Online and Predictive Warning System for Forced Lane Changes using Risk Maps](https://mlsanity.com/paper/2303.08458/) (2023)
- [Probabilistic Uncertainty-Aware Risk Spot Detector for Naturalistic Driving](https://mlsanity.com/paper/2303.07181/) (2023)
- [Considering Human Factors in Risk Maps for Robust and Foresighted Driver Warning](https://mlsanity.com/paper/2306.03849/) (2023)

## Abstract (arXiv)

This paper addresses the problem of human-based driver support. Nowadays, driver support systems help users to operate safely in many driving situations. Nevertheless, these systems do not fully use the rich information that is available from sensing the human driver. In this paper, we therefore present a human-based risk model that uses driver information for improved driver support. In contrast to state of the art, our proposed risk model combines a) the current driver perception based on driver errors, such as the driver overlooking another vehicle (i.e., notice error), and b) driver personalization, such as the driver being defensive or confident. In extensive simulations of multiple interactive driving scenarios, we show that our novel human-based risk model achieves earlier warning times and reduced warning errors compared to a baseline risk model not using human driver information.