Abstract
Through constant improvements in recent years radar sensors have become a viable alternative to lidar as the main distancing sensor of an autonomous vehicle. Although robust and with the possibility to directly measure the radial velocity, it brings it's own set of challenges, for which existing algorithms need to be adapted. One core algorithm of a perception system is dynamic occupancy grid mapping, which has traditionally relied on lidar. In this paper we present a dual-weight particle filter as an extension for a bayesian occupancy grid mapping framework to allow to operate it with radar as its main sensors. It uses two separate particle weights that are computed differently to compensate that a radial velocity measurement in many situations is not able to capture the actual velocity of an object. We evaluate the method extensively with simulated data and show the advantages over existing single weight solutions.
Original language | English |
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Title of host publication | Proceedings - 2023 IEEE International Conference on Mobility, Operations, Services and Technologies, MOST 2023 |
Publisher | IEEE |
Pages | 187-192 |
Number of pages | 6 |
ISBN (Electronic) | 9798350319958 |
DOIs | |
Publication status | Published - 2023 |
Event | 1st IEEE International Conference on Mobility, Operations, Services and Technologies: MOST 2023 - Detroit, United States Duration: 17 May 2023 → 19 May 2023 |
Conference
Conference | 1st IEEE International Conference on Mobility, Operations, Services and Technologies |
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Abbreviated title | MOST 2023 |
Country/Territory | United States |
City | Detroit |
Period | 17/05/23 → 19/05/23 |
Keywords
- autonomous driving
- dynamic occupancy grid mapping
- particle filter
- radar
ASJC Scopus subject areas
- Modelling and Simulation
- Artificial Intelligence
- Computer Networks and Communications
- Computer Science Applications
- Hardware and Architecture
- Information Systems
- Information Systems and Management