Increasing FastSLAM accuracy for radar data by integrating the Doppler information

Lupfer, Stefanie; Rapp, Matthias; Dietmayer, Klaus; Brosseit, Peter; Lombacher, Jakob; Hahn, Markus; Dickmann, Jürgen

doi:10.1109/icmim.2017.7918867

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…The implementation of this approach in real systems is difficult due to high computation costs. Moreover, even small errors in methods that calculate relative motion between consecutive sensor readings will accumulate when summed over a longer time [ 9 ].…”

Section: Related Workmentioning

confidence: 99%

Smart Roads for Autonomous Accident Detection and Warnings

Mateen

Hanif

Khatri

et al. 2022

Sensors

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An increasing number of vehicles on the roads increases the risk of accidents. In bad weather (e.g., heavy rainfall, strong winds, storms, and fog), this risk almost doubles due to bad visibility as well as road conditions. If an accident happens, especially in bad weather, it is important to inform approaching vehicles about it. Otherwise, there might be another accident, i.e., a multiple-vehicle collision (MVC). If the Emergency Operations Center (EOC) is not informed in a timely fashion about the incident, fatalities might increase because they do not receive immediate first aid. Detecting humans or animals would undoubtedly provide us with a better answer for reducing human fatalities in traffic accidents. In this research, an accident alert light and sound (AALS) system is proposed for auto accident detection and alerts with all types of vehicles. No changes are required in non-equipped vehicles (nEVs) and EVs because the system is installed on the roadside. The idea behind this research is to make smart roads (SRs) instead of equipping each vehicle with a separate system. Wireless communication is needed only when an accident is detected. This study is based on different sensors that are used to build SRs to detect accidents. Pre-saved locations are used to reduce the time needed to find the accident’s location without the help of a global positioning system (GPS). Additionally, the proposed framework for the AALS also reduces the risk of MVCs.

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Section: Related Workmentioning

confidence: 99%

Smart Roads for Autonomous Accident Detection and Warnings

Mateen

Hanif

Khatri

et al. 2022

Sensors

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“…In [10], Lupfer et al show that the FastSLAM 2.0 algorithm benefits from the inclusion of radar data. They extend the measurement model for the landmarks to include, besides the usual angle and distance, also the Doppler information.…”

Section: B Localization and Mappingmentioning

confidence: 99%

Radar-based Automotive Localization using Landmarks in a Multimodal Sensor Graph-based Approach

Jurgens¹,

Koch

Meinecke

2020

2020 21st International Radar Symposium (IRS)

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Highly automated driving functions currently often rely on a-priori knowledge from maps for planning and prediction in complex scenarios like cities. This makes map-relative localization an essential skill.In this paper, we address the problem of localization with automotive-grade radars, using a real-time graph-based SLAM approach. The system uses landmarks and odometry information as an abstraction layer. This way, besides radars, all kind of different sensor modalities including cameras and lidars can contribute. A single, semantic landmark map is used and maintained for all sensors.We implemented our approach using C++ and thoroughly tested it on data obtained with our test vehicles, comprising cars and trucks. Test scenarios include inner cities and industrial areas like container terminals. The experiments presented in this paper suggest that the approach is able to provide a precise and stable pose in structured environments, using radar data alone. The fusion of additional sensor information from cameras or lidars further boost performance, providing reliable semantic information needed for automated mapping.

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