Traffic Sign Recognition to Automatic Speed Control in IoT-Enabled Electric Vehicles

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Traffic sign recognition has been used as an input for managing speed of electric vehicles that are connected with their systems. This paper discusses how to integrate a visual recognition system with embedded propulsion control and IoT monitoring. Fifteen full-text studies published from 2021 to 2025 were reviewed. Literature contains deep learning techniques to detect and classify traffic signs; Indian roads dataset; IoT speed monitoring architecture; prototype-level speed control system. Recent detection studies show that end-to-end deep models can identify signs under complex conditions; for example, a refined Mask R-CNN study on 87 Indian sign categories reported 97.08% precision, while a YOLOv5 comparison reported 97.70% mAP@0.5 on its dataset. Control literature shows that pulse-width modulation (PWM) motor control, sensor feedback, and cloud logging can convert a known speed limit to an observable and traceable vehicle response. A low-cost prototype architecture is synthesized from the reviewed evidence: an ESP32-CAM captures signals, an ESP32 coordinates decisions and connectivity, an L298N applies pulse-width modulation to a DC traction motor, and a display/cloud service provides driver and supervisor feedback. The review finds that the limiting issue is no longer recognition accuracy alone: safe deployment requires confidence-aware decisions, low end-to-end latency, fail-safe override, locally representative data, and cybersecurity-aware IoT design. This model can serve as an academic and practical basis to develop an IoT-based electric vehicle speed control system. Keywords— traffic sign recognition; electric vehicles; Internet of Things; automatic speed control; convolutional neural networks; intelligent transportation systems.