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Adaptive Cruise Control in Autonomous Vehicles: Challenges, Gaps, Comprehensive Review, and, Future Directions

Shradha Bavalatti, Yash Kangralkar, Santosh Pattar, Veena P Badiger

TL;DR

This paper addresses the role of Adaptive Cruise Control (ACC) within Autonomous Vehicles (AVs) as a cornerstone for safety and traffic efficiency, and it critically surveys the literature to identify gaps and future directions. It introduces a seven-category taxonomy to organize ACC research, with a focus on Model Predictive Control (MPC) and related approaches, and provides a comprehensive review across safety, techniques, security, V2X, energy conservation, human factors, and machine learning models. The authors synthesize insights from the literature to highlight overreliance on simulations, gaps in real-world validation, and the need for robust human-centric design, reliable ML, and secure communications. They propose concrete future directions—real-world data integration, driver customization, ML reliability, cybersecurity, and resilient V2X frameworks—to guide the development of fault-resilient, sustainable urban transport systems.

Abstract

The development of Autonomous Vehicles (AVs) has redefined the way of transportation by eliminating the need for human intervention in driving. This revolution is fueled by rapid advancements in adaptive cruise control (ACC), which make AVs capable of interpreting their surroundings and responding intelligently. While AVs offer significant advantages, such as enhanced safety and improved traffic efficiency, they also face several challenges that need to be addressed. Existing survey papers often lack a comprehensive analysis of these challenges and their potential solutions. Our paper stands out by meticulously identifying these gaps in current ACC research and offering impactful future directions to guide researchers in designing next-generation ACC systems. Our survey provides a detailed and systematic review, addressing the limitations of previous studies and proposing innovative approaches to achieve sustainable and fault-resilient urban transportation.

Adaptive Cruise Control in Autonomous Vehicles: Challenges, Gaps, Comprehensive Review, and, Future Directions

TL;DR

This paper addresses the role of Adaptive Cruise Control (ACC) within Autonomous Vehicles (AVs) as a cornerstone for safety and traffic efficiency, and it critically surveys the literature to identify gaps and future directions. It introduces a seven-category taxonomy to organize ACC research, with a focus on Model Predictive Control (MPC) and related approaches, and provides a comprehensive review across safety, techniques, security, V2X, energy conservation, human factors, and machine learning models. The authors synthesize insights from the literature to highlight overreliance on simulations, gaps in real-world validation, and the need for robust human-centric design, reliable ML, and secure communications. They propose concrete future directions—real-world data integration, driver customization, ML reliability, cybersecurity, and resilient V2X frameworks—to guide the development of fault-resilient, sustainable urban transport systems.

Abstract

The development of Autonomous Vehicles (AVs) has redefined the way of transportation by eliminating the need for human intervention in driving. This revolution is fueled by rapid advancements in adaptive cruise control (ACC), which make AVs capable of interpreting their surroundings and responding intelligently. While AVs offer significant advantages, such as enhanced safety and improved traffic efficiency, they also face several challenges that need to be addressed. Existing survey papers often lack a comprehensive analysis of these challenges and their potential solutions. Our paper stands out by meticulously identifying these gaps in current ACC research and offering impactful future directions to guide researchers in designing next-generation ACC systems. Our survey provides a detailed and systematic review, addressing the limitations of previous studies and proposing innovative approaches to achieve sustainable and fault-resilient urban transportation.

Paper Structure

This paper contains 20 sections, 4 figures, 9 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Introduction to the ADAS
  4. Key Terms in Advanced Driver Assistance Systems (ADAS)
  5. Adaptive Cruise Control
  6. Architecture of Adaptive Cruise Control
  7. Challenges of Adaptive Cruise Control
  8. A REVIEW OF RESEARCH EFFORTS
  9. Overview
  10. A Review of Existing Works
  11. Safety
  12. Techniques
  13. Security
  14. V2X
  15. Energy Conservation
  16. ...and 5 more sections

Figures (4)

  • Figure 1: Different Components of the ADAS System
  • Figure 2: Illustrations of key ADAS terms
  • Figure 3: Architectural Components of the ACC
  • Figure 4: Proposed Classification Taxonomy of the Recent ACC Publications.