# Deep, spatially coherent Inverse Sensor Models with Uncertainty Incorporation using the evidential Framework

arXiv: https://arxiv.org/abs/1904.00842 · HTML: https://mlsanity.com/paper/1904.00842/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2019.

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

- [A Comprehensive Survey on Evidential Deep Learning and Its Applications](https://mlsanity.com/paper/2409.04720/) (2024)
- [Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction](https://mlsanity.com/paper/2211.08701/) (2022)
- [PolarNet: Accelerated Deep Open Space Segmentation Using Automotive Radar in Polar Domain](https://mlsanity.com/paper/2103.03387/) (2021)
- [Deep Radar Inverse Sensor Models for Dynamic Occupancy Grid Maps](https://mlsanity.com/paper/2305.12409/) (2023)

## Abstract (arXiv)

To perform high speed tasks, sensors of autonomous cars have to provide as much information in as few time steps as possible. However, radars, one of the sensor modalities autonomous cars heavily rely on, often only provide sparse, noisy detections. These have to be accumulated over time to reach a high enough confidence about the static parts of the environment. For radars, the state is typically estimated by accumulating inverse detection models (IDMs). We employ the recently proposed evidential convolutional neural networks which, in contrast to IDMs, compute dense, spatially coherent inference of the environment state. Moreover, these networks are able to incorporate sensor noise in a principled way which we further extend to also incorporate model uncertainty. We present experimental results that show This makes it possible to obtain a denser environment perception in fewer time steps.