Test and Evaluation of Systems to Warn Pedestrians of Turning Buses

Pecheux, Kelley Klaver; Strathman, James G.; Kennedy, Jason F

doi:10.3141/2539-18

Cited by 1 publication

(2 citation statements)

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“…At the same time, buses operate in a complicated environment where a significant number of unintended obstacles such as pulling out from bus stops, passengers unloading, pedestrians crossing in front of buses, and bus stop structures, etc. [15][16][17], are present. Additionally, buses have higher chances of side collisions due to constrained spaces and maneuverability [15].…”

Section: Introductionmentioning

confidence: 99%

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Blind-Spot Collision Detection System for Commercial Vehicles Using Multi Deep CNN Architecture

Muzammel

Yusoff

Saad

et al. 2022

Sensors

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Buses and heavy vehicles have more blind spots compared to cars and other road vehicles due to their large sizes. Therefore, accidents caused by these heavy vehicles are more fatal and result in severe injuries to other road users. These possible blind-spot collisions can be identified early using vision-based object detection approaches. Yet, the existing state-of-the-art vision-based object detection models rely heavily on a single feature descriptor for making decisions. In this research, the design of two convolutional neural networks (CNNs) based on high-level feature descriptors and their integration with faster R-CNN is proposed to detect blind-spot collisions for heavy vehicles. Moreover, a fusion approach is proposed to integrate two pre-trained networks (i.e., Resnet 50 and Resnet 101) for extracting high level features for blind-spot vehicle detection. The fusion of features significantly improves the performance of faster R-CNN and outperformed the existing state-of-the-art methods. Both approaches are validated on a self-recorded blind-spot vehicle detection dataset for buses and an online LISA dataset for vehicle detection. For both proposed approaches, a false detection rate (FDR) of 3.05% and 3.49% are obtained for the self recorded dataset, making these approaches suitable for real time applications.

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Section: Introductionmentioning

confidence: 99%

“…Additionally, buses have higher chances of side collisions due to constrained spaces and maneuverability [15]. Especially at turns, researchers found that the task demand on bus drivers is very high [16,17].…”

Section: Introductionmentioning

confidence: 99%