A pedestrian detection system based on thermopile and radar sensor data fusion

Linzmeier, D.T.; Skutek, Michael; Mekhaiel, Moheb; Dietmayer, Klaus

doi:10.1109/icif.2005.1592003

Cited by 10 publications

(3 citation statements)

References 11 publications

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“…Radars serve as a key component for advanced driver assistance systems and autonomous vehicles [4]. Past research in that space focused on fusing radar data with various other sensor types particularly Lidars [5] and cameras [6] for applications such as pedestrian detection [7] and tracking [8]. Radar-based SLAM approaches [9] usually model structural features in the surrounding environment and have also been investigated in the context of vehicle self-localization [10], [11].…”

Section: Related Workmentioning

confidence: 99%

Autonomous Navigation in Inclement Weather Based on a Localizing Ground Penetrating Radar

Ort

Gilitschenski

Rus

2020

IEEE Robot. Autom. Lett.

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Most autonomous driving solutions require some method of localization within their environment. Typically, onboard sensors are used to localize the vehicle precisely in a previously recorded map. However, these solutions are sensitive to ambient lighting conditions such as darkness and inclement weather. Additionally, the maps can become outdated in a rapidly changing environment and require continuous updating. While LiDAR systems don't require visible light, they are sensitive to weather such as fog or snow, which can interfere with localization. In this letter, we utilize a Ground Penetrating Radar (GPR) to obtain precise vehicle localization. By mapping and localizing using features beneath the ground, we obtain features that are both stable over time, and maintain their appearance during changing ambient weather and lighting conditions. We incorporate this solution into a full-scale autonomous vehicle and evaluate the performance on over 17 km of testing data in a variety of challenging weather conditions. We find that this novel sensing modality is capable of providing precise localization for autonomous navigation without using cameras or LiDAR sensors. Index Terms-Autonomous vehicle navigation, field robots, wheeled robots, intelligent transportation systems. I. INTRODUCTIONR OBUST localization in diverse conditions is a key challenge to enable the widespread deployment of autonomous vehicles. Since relying purely on a global navigation satellite system (GNSS), such as the Global Positioning System (GPS) does not provide sufficient precision, research and industry efforts have focused primarily on utilizing cameras and laser scanners for the navigation task. These systems typically use Simultaneous Localization and Mapping (SLAM) algorithms [1] for creating and maintaining maps of the environment that allow for highly precise localization and navigation.Using vision and laser sensors as localization sources comes with its own set of challenges. For instance, localization from purely visual maps needs to account for the fact that, even in the absence of occlusions, the appearance of the environment strongly varies depending on weather, season, time of day, and a potentially changing environment [2]. While laser scanners

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Section: Related Workmentioning

confidence: 99%

Autonomous Navigation in Inclement Weather Based on a Localizing Ground Penetrating Radar

Ort

Gilitschenski

Rus

2020

IEEE Robot. Autom. Lett.

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“…They then perform classification using a shape model for either the VS or the TIR spectrum images. Linzmeier et al [136] also exploit radar, but combine it with thermopile, steering angle, and ambient temperature sensors. In this case, fusion can be done at a low level (ROI generation combining radar and thermopile) and a high level (ROIs independently generated by all the sensors).…”

Section: Reviewmentioning

confidence: 99%

Survey of Pedestrian Detection for Advanced Driver Assistance Systems

Gerónimo

López

Sappa

et al. 2010

IEEE Trans. Pattern Anal. Mach. Intell.

870

435

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Abstract-Advanced driver assistance systems (ADASs), and particularly pedestrian protection systems (PPSs), have become an active research area aimed at improving traffic safety. The major challenge of PPSs is the development of reliable on-board pedestrian detection systems. Due to the varying appearance of pedestrians (e.g., different clothes, changing size, aspect ratio, and dynamic shape) and the unstructured environment, it is very difficult to cope with the demanded robustness of this kind of system. Two problems arising in this research area are the lack of public benchmarks and the difficulty in reproducing many of the proposed methods, which makes it difficult to compare the approaches. As a result, surveying the literature by enumerating the proposals one-after-another is not the most useful way to provide a comparative point of view. Accordingly, we present a more convenient strategy to survey the different approaches. We divide the problem of detecting pedestrians from images into different processing steps, each with attached responsibilities. Then, the different proposed methods are analyzed and classified with respect to each processing stage, favoring a comparative viewpoint. Finally, discussion of the important topics is presented, putting special emphasis on the future needs and challenges.

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“…The small radar cross section (RCS) of the pedestrians and the clutter make pedestrian detection challenging. Two main tracks exist in the literature to mitigate the pedestrian detection problem: (i) improving the characterization of reflected signals by pedestrians by measurements [4]- [8] and (ii) radar signal processing for better detection [9]- [14].…”

Section: Introductionmentioning

confidence: 99%

Improved Pedestrian Detection under Mutual Interference by FMCW Radar Communications

Avdogdu

Garcia

Wymeersch

2018

2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

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The widespread of automotive radars leads to increase of mutual interference, which in turn degrades road safety. The effect of mutual interference with a focus on detection of pedestrians is investigated. It is shown that detection of pedestrians degrades in the presence of mutual interference. A joint radar communication solution is proposed that increases pedestrian detection probability with negligible impact in the ranging error.

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A pedestrian detection system based on thermopile and radar sensor data fusion

Cited by 10 publications

References 11 publications

Autonomous Navigation in Inclement Weather Based on a Localizing Ground Penetrating Radar

Autonomous Navigation in Inclement Weather Based on a Localizing Ground Penetrating Radar

Survey of Pedestrian Detection for Advanced Driver Assistance Systems

Improved Pedestrian Detection under Mutual Interference by FMCW Radar Communications

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