Secure Vision-Based Lane Detection for Autonomous Shuttles in Crowd-Rich Environments
Main Article Content
Abstract
Self-driving shuttle systems are being envisaged with the context of shared pedestrian-rich streets where the challenge of the accessibility and dependability of accountable lane perception is vexed by moving crowds, optical defence, and increased caution demands. Lane detection vision is a central element of the perception stack of these platforms because of its affordability and high semantic accuracy; nevertheless, its functionality and capability in crowd-saturated working conditions have not been studied in a systematic manner. This article is a peer-reviewed systematic review of 74 peer-reviewed articles/papers regarding vision-based lane detection in autonomous systems, specifically crowd-aware perception and robustness of the perception layer. The literature review will be evaluated based on the methodology, sensor setup, metrics of evaluation, and the degree to which the dynamics of crowds and security are considered. The findings show significant dependence on deep learning-based and vision focused perception model, little explicit modelling of dense pedestrian environment and limited explicit security threats of the perception layer. There is great variance in evaluation practices which also makes cross-study comparison more difficult. The results reveal a grave discrepancy between the existing research implementation and the manoeuvrability requirements of autonomous shuttles in a shared environment, and they inspire necessity of all-encompassing, security-attentive, and crowd-sensitive lane sight systems backed by standardized examination systems.
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