What question did this study set out to answer?

To design and evaluate an autonomous driving framework that integrates computer vision, deep learning, and multi-sensor fusion for real-time environment perception and vehicle control.

August 15, 2026Open Access

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Authors

DODr. Harold OrjiUniversity of Cross River State OIOhaeri IgnatiusUniversity of Tabriz UEUdoudom Emem EtimHassan Usman Katsina Polytechnic

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Implication

Evaluation study demonstrates high user acceptance and perceived safety for an AI-driven autonomous vehicle framework, suggesting strong potential for intelligent transportation systems.

Key Points

To design and evaluate an autonomous driving framework that integrates computer vision, deep learning, and multi-sensor fusion for real-time environment perception and vehicle control.
Integrated inputs from cameras, LiDAR, radar, and ultrasonic devices with machine learning algorithms for object detection, lane recognition, and automated control.
Assessed system performance, usability, trust, and perceived safety using a structured questionnaire based on a 5-point Likert scale.
User evaluation demonstrated high acceptance across all metrics on a 5-point scale: safety and reliability achieved a mean score of 4.31, system performance 4.29, usability 4.19, and trust level 4.04.
System performance decreased under complex weather conditions and unpredictable traffic scenarios due to environmental and sensor constraints.

Cite This Study

Orji et al. (2026) studied this question.

synapsesocial.com/papers/6a8019a975c2e31742c85aeb https://doi.org/10.5281/zenodo.21915375

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Implication

Evaluation study demonstrates high user acceptance and perceived safety for an AI-driven autonomous vehicle framework, suggesting strong potential for intelligent transportation systems.

Concept of Autonomouse Car

Discussion

Member takes

Implication

Key Points

Cite This Study

Concept of Autonomouse Car

Discussion

Member takes

Implication

Key Points

Cite This Study

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