Design and simulation of a hybrid PLC-AI automotive control module for adaptive lighting and honking

Excessive honking and improper use of the headlamp beam significantly impact driving safety at night through visual impairment caused by headlamp beam glare, driver discomfort and noise pollution in urban areas. Current adaptive lighting technologies are mostly aimed at controlling individual headlamps, and are not connected to the rest of the vehicle's response. To overcome this, this paper presents a hybrid control architecture, ALHCV (Automated Light and Honking Control for Vehicles), that integrates both Programmable Logic Controller (PLC) based control and Artificial Intelligence (AI) based perception for adaptive lighting and honking based on context. The system closely simulates various vehicle parameters such as speed, steering angle, turn rate and stability duration to perform real-time switching of the beam, alert signal at high speeds and modulation of the honk by PLC ladder logic. The vision module uses an AI algorithm to analyze live camera data to detect vehicles approaching the intersection, and to determine silence-sensitive areas, which are then translated into supervisory constraints that dynamically modify or override deterministic decisions as needed. A 33 rung PLC ladder program is used to implement the control strategy and validated using simulation and HIL testing. Experimental results show excellent performance with 99.3% beam switching accuracy, 96.8% glare prevention accuracy and 97.4% silent-zone honking compliance, with end-to-end latency of less than 60ms. The findings demonstrate the potential of the proposed hybrid PLC-AI framework for next-generation intelligent automotive systems, which prioritize safety, adaptability, and environmental sustainability, while maintaining scalability and practicality.