Integrating Multiscale Deformable Part Models and Convolutional Networks for Pedestrian Detection

Chen, Wenhui; Kuan, Chi-Wei; Chiang, Chuan-Cho

doi:10.5220/0009459000002550

Cited by 1 publication

(1 citation statement)

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“…Pedestrian detection was another major issue for ADAS, and previous studies focused on the framework of intention recognition [48]. For example, studies were proposed to deal with the problem that the pedestrians on the roads were not sensitive enough for the sensors to detect them when they were partially obstructed [9,13]. Other vehicles around the ego vehicle also influenced the ADAS performance.…”

Section: Adas Solutionsmentioning

confidence: 99%

Cause-and-Effect Analysis of ADAS: A Comparison Study between Literature Review and Complaint Data

Ayoub

Wang

et al. 2022

Proceedings of the 14th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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Advanced driver assistance systems (ADAS) are designed to improve vehicle safety. However, it is difficult to achieve such benefits without understanding the causes and limitations of the current ADAS and their possible solutions. This study 1) investigated the limitations and solutions of ADAS through a literature review, 2) identified the causes and effects of ADAS through consumer complaints using natural language processing models, and 3) compared the major differences between the two. These two lines of research identified similar categories of ADAS causes, including human factors, environmental factors, and vehicle factors. However, academic research focused more on human factors of ADAS issues and proposed advanced algorithms to mitigate such issues while drivers complained more of vehicle factors of ADAS failures, which led to associated top consequences. The findings from these two sources tend to complement each other and provide important implications for the improvement of ADAS in the future.

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Section: Adas Solutionsmentioning

confidence: 99%