# Online and Predictive Warning System for Forced Lane Changes using Risk Maps

arXiv: https://arxiv.org/abs/2303.08458 · HTML: https://mlsanity.com/paper/2303.08458/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2023.

## Built on by (most cited first, 4 of 4)

- [Considering Human Factors in Risk Maps for Robust and Foresighted Driver Warning](https://mlsanity.com/paper/2306.03849/) (2023)
- [Introducing Risk Shadowing For Decisive and Comfortable Behavior Planning](https://mlsanity.com/paper/2307.10714/) (2023)
- [Reducing Warning Errors in Driver Support with Personalized Risk Maps](https://mlsanity.com/paper/2410.02148/) (2024)
- [Human-Based Risk Model for Improved Driver Support in Interactive Driving Scenarios](https://mlsanity.com/paper/2410.03774/) (2024)

## Abstract (arXiv)

The survival analysis of driving trajectories allows for holistic evaluations of car-related risks caused by collisions or curvy roads. This analysis has advantages over common Time-To-X indicators, such as its predictive and probabilistic nature. However, so far, the theoretical risks have not been demonstrated in real-world environments. In this paper, we therefore present Risk Maps (RM) for online warning support in situations with forced lane changes, due to the end of roads. For this purpose, we first unify sensor data in a Relational Local Dynamic Map (R-LDM). RM is afterwards able to be run in real-time and efficiently probes a range of situations in order to determine risk-minimizing behaviors. Hereby, we focus on the improvement of uncertainty-awareness and transparency of the system. Risk, utility and comfort costs are included in a single formula and are intuitively visualized to the driver. In the conducted experiments, a low-cost sensor setup with a GNSS receiver for localization and multiple cameras for object detection are leveraged. The final system is successfully applied on two-lane roads and recommends lane change advices, which are separated in gap and no-gap indications. These results are promising and present an important step towards interpretable safety.