Evaluation of different approaches for road course estimation using imaging radar

Sarholz, Frederik; Mehnert, Jens; Klappstein, Jens; Dickmann, Juergen; Radig, Bernd

doi:10.1109/iros.2011.6094623

Cited by 19 publications

(2 citation statements)

References 5 publications

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“…In summary, the establishment of AGMs and OGMs are important representations of static environments from automotive radar data, which can be applied to lane prediction, free space description, parking detection, SLAM, and other autonomous driving tasks [ 32 , 37 , 38 ]. Compared with LIDAR, the advantages of using radar data In environmental mapping include low cost, high adaptability, and the ability to detect partially occupied objects.…”

Section: Data Models and Representations From Mmw Radarmentioning

confidence: 99%

MMW Radar-Based Technologies in Autonomous Driving: A Review

Zhou

Yang

Jiang

et al. 2020

Sensors

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With the rapid development of automated vehicles (AVs), more and more demands are proposed towards environmental perception. Among the commonly used sensors, MMW radar plays an important role due to its low cost, adaptability In different weather, and motion detection capability. Radar can provide different data types to satisfy requirements for various levels of autonomous driving. The objective of this study is to present an overview of the state-of-the-art radar-based technologies applied In AVs. Although several published research papers focus on MMW Radars for intelligent vehicles, no general survey on deep learning applied In radar data for autonomous vehicles exists. Therefore, we try to provide related survey In this paper. First, we introduce models and representations from millimeter-wave (MMW) radar data. Secondly, we present radar-based applications used on AVs. For low-level automated driving, radar data have been widely used In advanced driving-assistance systems (ADAS). For high-level automated driving, radar data is used In object detection, object tracking, motion prediction, and self-localization. Finally, we discuss the remaining challenges and future development direction of related studies.

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Section: Data Models and Representations From Mmw Radarmentioning

confidence: 99%

MMW Radar-Based Technologies in Autonomous Driving: A Review

Zhou

Yang

Jiang

et al. 2020

Sensors

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“…In recent years, the lane departure warning system, front collision warning system, adaptive cruise control system, and other automotive advanced driver assistance systems, which are based on the radar and computer vision, have become hot research topics in the international automotive safety technology [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

A Study on Maneuvering Obstacle Motion State Estimation for Intelligent Vehicle Using Adaptive Kalman Filter Based on Current Statistical Model

Han

Guan

Xin

et al. 2015

Mathematical Problems in Engineering

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The obstacle motion state estimation is an essential task in intelligent vehicle. The ASCL group has developed such a system that uses a radar and GPS/INS. When running on the road, the acceleration of the vehicle is always changing, so it is hard for constant velocity (CV) model and constant acceleration (CA) model to describe the motion state of the vehicle. This paper introduced Current Statistical (CS) model from military field, which uses the modified Rayleigh distribution to describe acceleration. The adaptive Kalman filter based on CS model was used to estimate the motion state of the target. We conducted simulation experiments and real vehicle tests, and the results showed that the estimation of position, velocity, and acceleration can be precise.

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