# Dynamic Risk Assessment for Vehicles of Higher Automation Levels by Deep Learning

arXiv: https://arxiv.org/abs/1806.07635 · HTML: https://mlsanity.com/paper/1806.07635/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2018.

## Built on by (most cited first, 5 of 5)

- [Risk Ranked Recall: Collision Safety Metric for Object Detection Systems in Autonomous Vehicles](https://mlsanity.com/paper/2106.04146/) (2021)
- [Dynamic Risk Assessment for Autonomous Vehicles from Spatio-Temporal Probabilistic Occupancy Heatmaps](https://mlsanity.com/paper/2501.16480/) (2025)
- [Is the Rush to Machine Learning Jeopardizing Safety? Results of a Survey](https://mlsanity.com/paper/2111.14324/) (2021)
- [LADRI: LeArning-based Dynamic Risk Indicator in Automated Driving System](https://mlsanity.com/paper/2401.02199/) (2024)
- [FuzzRisk: Online Collision Risk Estimation for Autonomous Vehicles based on Depth-Aware Object Detection via Fuzzy Inference](https://mlsanity.com/paper/2411.08060/) (2024)

## Abstract (arXiv)

Vehicles of higher automation levels require the creation of situation awareness. One important aspect of this situation awareness is an understanding of the current risk of a driving situation. In this work, we present a novel approach for the dynamic risk assessment of driving situations based on images of a front stereo camera using deep learning. To this end, we trained a deep neural network with recorded monocular images, disparity maps and a risk metric for diverse traffic scenes. Our approach can be used to create the aforementioned situation awareness of vehicles of higher automation levels and can serve as a heterogeneous channel to systems based on radar or lidar sensors that are used traditionally for the calculation of risk metrics.