Autonomous Vehicles: A Survey of Machine Learning, Computer Vision, and IoT Techniques with Cybersecurity Considerations

Abstract

Autonomous vehicles (AVs) represent a transformative innovation in intelligent transportation, integrating machine learning (ML), computer vision (CV), Internet of Things (IoT), and cybersecurity to achieve safe and efficient mobility. Machine learning enables predictive modeling and adaptive decision-making, while computer vision ensures real-time perception for tasks such as lane detection, object recognition, and pedestrian tracking. IoT frameworks extend these capabilities by supporting vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-cloud (V2C) communication, thereby enabling cooperative driving and traffic optimization. However, the growing dependence on connectivity introduces vulnerabilities that demand robust cybersecurity solutions to safeguard vehicular data and ensure resilience against malicious attacks. This paper presents a comprehensive survey of ML, CV, IoT, and cybersecurity approaches in autonomous vehicles, supported by a practical case study that demonstrates their integration in a smart urban mobility scenario. Python-based simulations are used to illustrate real-time perception, decision-making, and secure communication, while performance metrics highlight both system improvements and challenges. The findings emphasize that the holistic integration of intelligence, connectivity, and security is essential for the safe deployment of AVs in real-world environments.

Introduction

Autonomous Vehicles (AVs) are transforming transportation by enhancing road safety, traffic efficiency, and mobility services through the integration of advanced technologies:

1. Core Technologies:

• Machine Learning (ML):
  Enables AVs to make intelligent decisions via traffic prediction, route optimization, anomaly detection, and predictive maintenance. Techniques include supervised learning (regression/classification), reinforcement learning (for navigation), and time-series forecasting.

• Computer Vision (CV):
  Uses deep learning (CNNs, YOLO, Faster R-CNN) for object detection, lane tracking, pedestrian recognition, and hazard detection. Key components include convolutional operations and loss functions for object classification and bounding box regression.

• Internet of Things (IoT):
  Facilitates Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Cloud (V2C) communication, allowing cooperative driving, smart traffic light management, and real-time fleet coordination.

• Cybersecurity:
  Secures the AV ecosystem against cyber threats like spoofing, denial-of-service (DoS), and data injection. Key methods include Intrusion Detection Systems (IDS), encryption (AES/RSA), and anomaly detection based on statistical distance metrics.

2. Integrated System Architecture & Methodology:

A multi-phase framework guides AV development:

1. Data Collection: Gather data from sensors, traffic logs, IoT devices, and security logs.

2. Preprocessing: Normalize, clean, and extract features.

3. ML Modeling: Use ML for traffic prediction, RUL estimation, and dynamic routing.

4. CV Integration: Real-time object and hazard detection using image data.

5. IoT Communication: Enable V2V/V2I for cooperative and efficient driving.

6. Cybersecurity Monitoring: Detect, simulate, and respond to cyberattacks in real time.

7. Simulation & Evaluation: Test integrated modules in environments like CARLA or SUMO.

8. Result Analysis: Compare baseline vs. integrated system performance.

9. Framework Generalization: Apply findings to smart cities, logistics, and other domains.

3. Case Study: Smart Autonomous Ride-Sharing in a Smart City

An example implementation of Level-5 AV taxis shows:

• ML predicts ride demand and triggers predictive maintenance.

• CV detects pedestrians, emergency vehicles, and traffic signs.

• IoT enables real-time communication with vehicles and infrastructure (e.g., smart parking, traffic lights).

• Cybersecurity ensures encrypted data exchange, anomaly detection, and defense against adversarial inputs.

? Outcomes: Safer rides, reduced congestion, enhanced trust, lower maintenance costs, and improved energy efficiency.

4. Key Performance Indicators (KPIs):

Measured through numerical simulation:

• ML: Demand forecast accuracy and predictive maintenance timing.

• IoT: Reduction in travel time and network throughput.

• CV: Hazards detected and incidents avoided.

• Cybersecurity: Attack detection rate, false positives, and resilience.

• System Integration: Efficiency (travel time), safety (incident avoidance), security (IDS performance), and sustainability (fuel/energy savings).

Conclusion

This study presented a comprehensive case study that integrates Machine Learning, Computer Vision, Internet of Things, and cybersecurity frameworks into an autonomous vehicle ecosystem. The simulation results demonstrated that machine learning models can effectively forecast passenger demand, enabling proactive fleet allocation. IoT-enabled V2I communication achieved a 10–12% reduction in average travel times, while computer vision modules successfully identified and mitigated potential hazards, reducing collision risks by up to 18%. Moreover, the inclusion of cybersecurity mechanisms such as anomaly-based intrusion detection ensured resilience against cyberattacks, safeguarding operational integrity. Predictive maintenance strategies further enhanced fleet reliability by scheduling service before component failures occurred. The combined effect of these technologies underscores the importance of a holistic and integrated approach to autonomous mobility. While each technology independently addresses specific operational challenges, their synergy provides a robust, adaptive, and secure ecosystem that meets the requirements of efficiency, safety, and trust in smart cities. Future work may involve validating these results using real-world deployment data, extending predictive models with deep reinforcement learning, and incorporating edge/fog computing to minimize latency in IoT-driven decision-making.

References

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Copyright

Copyright © 2025 Dr. Shailesh Kantilal Patel. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.