A unified lightweight ADAS architecture integrating traffic sign recognition, forward collision warning, and bimodal distillation for autonomous perception

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In an autonomous driving vehicle, the onboard perception system is essential for understanding a dynamic, complex road scene in real time, which is fundamental to autonomous driving. Perception tasks, such as object detection, classification of traffic signs, lane detection, etc., have seen significant progress in autonomous vehicles. One of the major practical challenges is designing an integrated, computationally efficient system capable of supporting multiple perception tasks without compromising detection performance or increasing the hardware cost of embedded automotive platforms. The proposed work introduces an integrated module-based Unified Advanced Driver Assistance System (ADAS) architecture. The first is a Traffic Sign Recognition (TSR) subsystem using the lightweight YOLOv8n backbone for detecting and classifying regulatory, warning, and informational traffic signs under a variety of illumination and environmental conditions. The second module focuses on the Forward Collision Warning (FCW) subsystem, which is trained to detect vehicles, pedestrians, bicyclists, and traffic control signals in dense urban and highway situations. The third module is a novel bimodal distillation fusion module, which bundles the knowledge from both perception streams into a single student model efficient inference pipeline using the principles of knowledge distillation, compressing the knowledge of two specialist detectors into an efficient student model. The architecture has been trained and tested with domain-specific datasets that include realistic noise on the weather, illumination, and complexity. For all the major object and sign categories, significant quantitative evaluation, such as the Precision-Recall curve, the F1-confidence analysis, the confusion matrix, and the Mean Average Precision (mAP) measurement, reveals satisfactory detection performance. Achieved with the TSR module, the mAP@0.5 score is approximately 0.994, and for primary vehicle classes, the mAP@0.5 score is above 0.81 with the FCW module. In addition to the static regulatory information used in fusion, the scenario utilizes dynamic information from the environment, significantly increasing the completeness of the result, which creates a flatter confidence distribution for subsequent ADAS decision logic. The proposed lightweight ADAS architecture supports Sustainable Development Goal (SDG) 9 (Industry, Innovation and Infrastructure) by enabling computationally efficient AI-based intelligent transportation technologies, while also contributing to SDG 11 (Sustainable Cities and Communities) through improved road safety and intelligent mobility.