A Review Paper of Automated Driving & ADAS Technologies

Year : 2026 | Volume : 13 | Issue : 01 | Page : 1 7
    By

    Paresh m Sangadiya,

  • Mihir D Gajjar,

  • Sagar M Bechara,

  • Rishabh Makwana,

  1. Assistant Professor, Department of Automobile Engineering, Atmiya University, Rajkot, Gujarat, India
  2. Lecturer, Department of Automobile Engineering, Atmiya University, Rajkot, Gujarat, India
  3. Assistant Professor, Department of Automobile Engineering, Atmiya University, Rajkot, Gujarat, India
  4. Assistant Professor, Department of Mechanical Engineering, Atmiya University, Rajkot, Gujarat, India

Abstract

Automated driving and Advanced Driver Assistance Systems (ADAS) are transforming road mobility, promising enhanced safety, improved traffic efficiency, and greater accessibility. This review presents a comprehensive synthesis of core technologies, system architectures, sensor modalities, perception and decision-making algorithms, and evaluation methodologies underpinning contemporary ADAS and automated driving. We provide a detailed discussion of the functional components—sensors (camera, radar, LiDAR, ultrasonic), localization, perception, prediction, planning, control, and human–machine interfaces—and how these integrate within modular and end-to-end architectures. A two-page literature survey distills seminal and recent contributions across academic research and industrial deployments, highlighting breakthroughs in deep learning-based perception, sensor fusion strategies, robust localization, and simulation-based validation. Methodological approaches for developing and validating ADAS systems are summarized, including data-driven model training, closed-loop simulation, hardware-in-the-loop testing, and on-road trials. Key challenges are examined—safety assurance, handling edge-cases, sensing limitations in adverse weather, regulatory and ethical considerations, cyber security, and human factors. Finally, we outline future directions such as scalable validation frameworks, V2X integration, AI interpretability, and shared autonomy paradigms. The review aims to serve researchers, engineers, and policymakers with a structured overview and practical recommendations for advancing safe, reliable automated driving systems.

Automated Driving and Advanced Driver Assistance Systems (ADAS) represent a significant evolution in intelligent transportation, aiming to improve road safety, traffic efficiency, and driving comfort. This review paper provides a structured overview of key ADAS and automated driving technologies, including system architectures, sensor technologies, perception, decision-making, and control algorithms. It surveys major advancements in camera, radar, LiDAR-based sensing, sensor fusion, and deep learning techniques for environment understanding. Additionally, the paper discusses validation methodologies, safety challenges, human–machine interaction, and regulatory aspects. Future research directions such as V2X integration, scalable testing, and explainable AI are also highlighted.

Keywords: Automated driving; ADAS; sensor fusion; perception; planning; localization; safety assurance; V2X

[This article belongs to Journal of Automobile Engineering and Applications ]

How to cite this article:
Paresh m Sangadiya, Mihir D Gajjar, Sagar M Bechara, Rishabh Makwana. A Review Paper of Automated Driving & ADAS Technologies. Journal of Automobile Engineering and Applications. 2026; 13(01):1-7.
How to cite this URL:
Paresh m Sangadiya, Mihir D Gajjar, Sagar M Bechara, Rishabh Makwana. A Review Paper of Automated Driving & ADAS Technologies. Journal of Automobile Engineering and Applications. 2026; 13(01):1-7. Available from: https://journals.stmjournals.com/joaea/article=2026/view=235798


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Regular Issue Subscription Review Article
Volume 13
Issue 01
Received 18/12/2025
Accepted 03/01/2026
Published 20/01/2026
Publication Time 33 Days
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