Sensing Structure for Blind Spot Detection System in Vehicles

Bagi, Shayan Shirahmad Gale; Garakani, Hossein Gharaee; Moshiri, Behzad; Khoshnevisan, Mohammad

doi:10.1109/iccais46528.2019.9074580

Cited by 7 publications

(2 citation statements)

References 8 publications

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“…The Gale Bagi et al [ 156 ] paper discusses a BSDS combining radar and cameras for accurate vehicle detection in blind spots. Radar detects vehicles and cameras identify them.…”

Section: Discussion—methodologymentioning

confidence: 99%

Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends

Malligere Shivanna,

Guo

2023

Sensors

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Advanced driver assistance systems (ADASs) are becoming increasingly common in modern-day vehicles, as they not only improve safety and reduce accidents but also aid in smoother and easier driving. ADASs rely on a variety of sensors such as cameras, radars, lidars, and a combination of sensors, to perceive their surroundings and identify and track objects on the road. The key components of ADASs are object detection, recognition, and tracking algorithms that allow vehicles to identify and track other objects on the road, such as other vehicles, pedestrians, cyclists, obstacles, traffic signs, traffic lights, etc. This information is then used to warn the driver of potential hazards or used by the ADAS itself to take corrective actions to avoid an accident. This paper provides a review of prominent state-of-the-art object detection, recognition, and tracking algorithms used in different functionalities of ADASs. The paper begins by introducing the history and fundamentals of ADASs followed by reviewing recent trends in various ADAS algorithms and their functionalities, along with the datasets employed. The paper concludes by discussing the future of object detection, recognition, and tracking algorithms for ADASs. The paper also discusses the need for more research on object detection, recognition, and tracking in challenging environments, such as those with low visibility or high traffic density.

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“…The Gale Bagi et al [ 156 ] paper discusses a BSDS combining radar and cameras for accurate vehicle detection in blind spots. Radar detects vehicles and cameras identify them.…”

Section: Discussion—methodologymentioning

confidence: 99%

Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends

Malligere Shivanna,

Guo

2023

Sensors

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“…5 Some of the common sensors include lidar, camera and millimeter wave radar, etc. 6,7 Lidar can provide high precision and resolution, can recognize the shape and texture of objects, and generate point cloud images in 3D space, so it is widely used in the field of automotive radar. 8 However, lidar is susceptible to environmental interference, such as rain, snow, and fog, which can degrade its performance.…”

Section: Introductionmentioning

confidence: 99%

A low-complexity rear vehicle detection approach based on a dual-channel millimeter-wave radar

Guo,

Yang,

Chu

et al. 2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

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A low-complexity rear vehicle detection approach based on a dual-channel millimeter-wave radar is presented in this paper. The main task is to detect whether there are vehicles approaching on both rear sides of the vehicle, and to give an early warning when the relative distance between the two vehicles is less than the early warning range. The difficulty in accurately detecting an approaching target vehicle lies in the fact that the vehicle is driving in a complex traffic scene and there are metal roadblocks in the road environments. In this paper, firstly, in order to reduce the complexity, the cascaded distance and angle of the potential target calculation is adopted to achieve the localization of the potential target. Then, rough recognition of vehicle targets and roadblocks is performed by clustering algorithm and structural features extraction. Since the features of some of the roadblocks are similar to those of the vehicle targets, secondary recognition is required to classify the vehicle targets and roadblocks by the target motion trend and trajectory length. The experimental results show that the average detection rate is 91.13% and the average false detection rate is 0.48% in different traffic scenarios.

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Blind Spot Detection System in Vehicles Using Fusion of Radar Detections and Camera Verification

et al. 2021

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Sensing Structure for Blind Spot Detection System in Vehicles

Cited by 7 publications

References 8 publications

Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends

Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends

A low-complexity rear vehicle detection approach based on a dual-channel millimeter-wave radar

Blind Spot Detection System in Vehicles Using Fusion of Radar Detections and Camera Verification

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