High Resolution Radar-based Occupancy Grid Mapping and Free
Space Detection

Li, Mingkang; Feng, Zhaofei; Stolz, Martin; Kunert, Martin; Henze, Roman; Küçükay, Ferit

doi:10.5220/0006667300700081

Cited by 25 publications

(20 citation statements)

References 18 publications

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“…Grid maps are divided into amplitude grid maps and the much more common occupancy grid maps [101], which represent the probabilistic cell states of the environment. This means occupancy grid maps allow for a distinction between free space, obstacles, and an unknown area, as shown in the example in Fig 22. In contrast to feature-based approaches, the free space and the occupied space is directly visible in such maps and does not have to be interpolated or calculated additionally [102], [103]. Due to the probabilistic cell representation, neither ghost targets are represented as real targets nor hidden targets as free space, which enables a highly accurate and very robust map.…”

Section: Grid Mapping and Automotive Sarmentioning

confidence: 99%

Automotive Radar — From First Efforts to Future Systems

2021

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Section: Grid Mapping and Automotive Sarmentioning

confidence: 99%

Automotive Radar — From First Efforts to Future Systems

2021

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“…To build occupancy grid maps and to detect free space, we follow the state-of-the-art approach proposed by Li et al [ 40 ]. First, using an artificial neural network adopting back-propagation, we translate sensor reading into occupancy values.…”

Section: Sensors Perception and Data Fusionmentioning

confidence: 99%

Modules and Techniques for Motion Planning: An Industrial Perspective

Quer

García²

2021

Sensors

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Research on autonomous cars has become one of the main research paths in the automotive industry, with many critical issues that remain to be explored while considering the overall methodology and its practical applicability. In this paper, we present an industrial experience in which we build a complete autonomous driving system, from the sensor units to the car control equipment, and we describe its adoption and testing phase on the field. We report how we organize data fusion and map manipulation to represent the required reality. We focus on the communication and synchronization issues between the data-fusion device and the path-planner, between the CPU and the GPU units, and among different CUDA kernels implementing the core local planner module. In these frameworks, we propose simple representation strategies and approximation techniques which guarantee almost no penalty in terms of accuracy and large savings in terms of memory occupation and memory transfer times. We show how we adopt a recent implementation on parallel many-core devices, such as CUDA-based GPGPU, to reduce the computational burden of rapidly exploring random trees to explore the state space along with a given reference path. We report on our use of the controller and the vehicle simulator. We run experiments on several real scenarios, and we report the paths generated with the different settings, with their relative errors and computation times. We prove that our approach can generate reasonable paths on a multitude of standard maneuvers in real time.

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“…The free space is defined by the narrowest distance between the vehicle possible position and the border of the occupied space. Based on radar grid maps describing the static environment, free space can be further determined based on the border recognition algorithm [ 34 ]. Compared with LiDAR, occupied objects can be better detected, and a more accurate free space range can be obtained with radar due to its penetrability [ 33 ].…”

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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High Resolution Radar-based Occupancy Grid Mapping and Free Space Detection

Cited by 25 publications

References 18 publications

Automotive Radar — From First Efforts to Future Systems

Automotive Radar — From First Efforts to Future Systems

Modules and Techniques for Motion Planning: An Industrial Perspective

MMW Radar-Based Technologies in Autonomous Driving: A Review

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