# A Random Finite Set Approach for Dynamic Occupancy Grid Maps with Real-Time Application

arXiv: https://arxiv.org/abs/1605.02406 · HTML: https://mlsanity.com/paper/1605.02406/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2016.

## Built on by (most cited first, 17 of 17)

- [MotionSC: Data Set and Network for Real-Time Semantic Mapping in Dynamic Environments](https://mlsanity.com/paper/2203.07060/) (2022)
- [Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2303.01212/) (2023)
- [Dynamics-Aware Spatiotemporal Occupancy Prediction in Urban Environments](https://mlsanity.com/paper/2209.13172/) (2022)
- [Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges](https://mlsanity.com/paper/2306.09304/) (2023)
- [Vision-aided UAV navigation and dynamic obstacle avoidance using gradient-based B-spline trajectory optimization](https://mlsanity.com/paper/2209.07003/) (2022)
- [Unsupervised 4D LiDAR Moving Object Segmentation in Stationary Settings with Multivariate Occupancy Time Series](https://mlsanity.com/paper/2212.14750/) (2022)
- [Finding Failures in High-Fidelity Simulation using Adaptive Stress Testing and the Backward Algorithm](https://mlsanity.com/paper/2107.12940/) (2021)
- [Memory Maps for Video Object Detection and Tracking on UAVs](https://mlsanity.com/paper/2303.03508/) (2023)
- [Trajectory-Prediction with Vision: A Survey](https://mlsanity.com/paper/2303.13354/) (2023)
- [Vehicle trajectory prediction in top-view image sequences based on deep learning method](https://mlsanity.com/paper/2102.01749/) (2021)
- [Fusion of neural networks, for LIDAR-based evidential road mapping](https://mlsanity.com/paper/2102.03326/) (2021)
- [Deep Radar Inverse Sensor Models for Dynamic Occupancy Grid Maps](https://mlsanity.com/paper/2305.12409/) (2023)
- [Deep Generic Dynamic Object Detection Based on Dynamic Grid Maps](https://mlsanity.com/paper/2410.14799/) (2024)
- [Near-Shore Mapping for Detection and Tracking of Vessels](https://mlsanity.com/paper/2502.18368/) (2025)
- [Cell Multi-Bernoulli (Cell-MB) Sensor Control for Multi-object Search-While-Tracking (SWT)](https://mlsanity.com/paper/2108.11236/) (2021)
- [A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles](https://mlsanity.com/paper/2505.07855/) (2025)
- [TriO: Tri-Modal Unsupervised Occupancy World Model for Anything Perception](https://mlsanity.com/paper/2609.32013/) (2026)

## Abstract (arXiv)

Grid mapping is a well established approach for environment perception in robotic and automotive applications. Early work suggests estimating the occupancy state of each grid cell in a robot's environment using a Bayesian filter to recursively combine new measurements with the current posterior state estimate of each grid cell. This filter is often referred to as binary Bayes filter (BBF). A basic assumption of classical occupancy grid maps is a stationary environment. Recent publications describe bottom-up approaches using particles to represent the dynamic state of a grid cell and outline prediction-update recursions in a heuristic manner. This paper defines the state of multiple grid cells as a random finite set, which allows to model the environment as a stochastic, dynamic system with multiple obstacles, observed by a stochastic measurement system. It motivates an original filter called the probability hypothesis density / multi-instance Bernoulli (PHD/MIB) filter in a top-down manner. The paper presents a real-time application serving as a fusion layer for laser and radar sensor data and describes in detail a highly efficient parallel particle filter implementation. A quantitative evaluation shows that parameters of the stochastic process model affect the filter results as theoretically expected and that appropriate process and observation models provide consistent state estimation results.