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Review
. 2020 Nov 15;20(22):6532.
doi: 10.3390/s20226532.

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

Abdul Sajeed Mohammed  1 , Ali Amamou  1 , Follivi Kloutse Ayevide  1 , Sousso Kelouwani  1 , Kodjo Agbossou  1 , Nadjet Zioui  1
Affiliations
Review

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

Abdul Sajeed Mohammed et al. Sensors (Basel). .

Abstract

Perception is a vital part of driving. Every year, the loss in visibility due to snow, fog, and rain causes serious accidents worldwide. Therefore, it is important to be aware of the impact of weather conditions on perception performance while driving on highways and urban traffic in all weather conditions. The goal of this paper is to provide a survey of sensing technologies used to detect the surrounding environment and obstacles during driving maneuvers in different weather conditions. Firstly, some important historical milestones are presented. Secondly, the state-of-the-art automated driving applications (adaptive cruise control, pedestrian collision avoidance, etc.) are introduced with a focus on all-weather activity. Thirdly, the most involved sensor technologies (radar, lidar, ultrasonic, camera, and far-infrared) employed by automated driving applications are studied. Furthermore, the difference between the current and expected states of performance is determined by the use of spider charts. As a result, a fusion perspective is proposed that can fill gaps and increase the robustness of the perception system.

Keywords: advanced driver assistance systems; autonomous vehicles; infrared camera; lidar; radar; road safety; sensor; sensor fusion; ultrasonic sensor; weather conditions.

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Conflict of interest statement

The authors declare no conflict of interest and the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Electromagnetic spectrum according to ISO 20473 [57].
Figure 2
Figure 2
Mid-range radar from Bosch [68].
Figure 3
Figure 3
Performance gap for radar sensor based on the scores gathered.
Figure 4
Figure 4
Velodyne HDL 64 lidar [99].
Figure 5
Figure 5
The performance gaps in lidar sensors are illustrated using a spider chart, based on performance scores.
Figure 6
Figure 6
Ultrasonic surround sensor from Bosch [126].
Figure 7
Figure 7
Ultrasonic sensor spider chart, showing performance gap.
Figure 8
Figure 8
Intelligent mono-camera by Mobileye [154].
Figure 9
Figure 9
Camera sensor spider chart. The difference between the red dashed line and the solid blue line is the performance gap.
Figure 10
Figure 10
Thermal Vision FLIR ADK by FLIR Systems [170].
Figure 11
Figure 11
Far-infrared sensor spider chart. The difference between the red dashed line and the solid blue line is the performance gap.
See this image and copyright information in PMC

References

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