A computer vision-based lane detection technique using gradient threshold and hue-lightness-saturation value for an autonomous vehicle

Abstract

<span lang="EN-US">Automatic lane detection for driver assistance is a significant component in developing advanced driver assistance systems and high-level application frameworks since it contributes to driver and pedestrian safety on roads and highways. However, due to several limitations that lane detection systems must rectify, such as the uncertainties of lane patterns, perspective consequences, limited visibility of lane lines, dark spots, complex background, illuminance, and light reflections, it remains a challenging task. The proposed method employs vision-based technologies to determine the lane boundary lines. We devised a system for correctly identifying lane lines on a homogeneous road surface. Lane line detection relies heavily on the gradient and hue lightness saturation (HLS) thresholding which detects the lane line in binary images. The lanes are shown, and a sliding window searching method is used to estimate the color lane. The proposed system achieved 96% accuracy in detecting lane lines on the different roads, and its performance was assessed using data from several road image databases under various illumination circumstances.</span>

Ensuring Compliance Integrity in AI ML Cloud Environments: The Role of Data Guardianship

“…Account and Service Hijacking: This threat arises from various tactics such as phishing, fraud, exploiting software vulnerabilities, and credential reuse. Attackers can illicitly acqu unauthorized access to services [55][56][57].…”

“…The severity of the repercussions depends on the level of access, as individuals with higher privileges can access sensitive data and services. Malicious insiders pose significant risks, including theft of confidential data, reputational damage, financial losses, and productivity disruptions [55][56][57].…”

“…Additionally, insecure API gateways can lead to account and service hijacking, data loss, and leakage. Vulnerabilities in API gateways can be exploited in black-box attac based AI systems, enabling adversaries to misuse insecure APIs to query ML models [55][56][57].…”

“…Authentication verifies the identity of users, imate users and adversaries, while authorization controls data access by defining the access levels for each authenticated user. Absence of these mechanisms can lead to various compromises, such as ices, loss of data privacy, and data leakage [55][56][57].…”

Ensuring Compliance Integrity in AI ML Cloud Environments: The Role of Data Guardianship

“…Account and Service Hijacking: This threat arises from various tactics such as phishing, fraud, exploiting software vulnerabilities, and credential reuse. Attackers can illicitly acquire user credentials, thus ga unauthorized access to services [55][56][57].…”