Sensing-to-Actuation Architectures for Advanced Driver Assistance Systems: Modeling, Control, and Performance Evaluation of Lane Keeping, Forward Collision Warning, Blind Spot Detection, and Automated Parking Assist

Advanced Driver Assistance Systems (ADAS) rely on tightly integrated sensing, estimation, and control pipelines to enhance road safety while maintaining the human driver in the control loop. This paper presents a unified, reproducible modeling and evaluation framework for four widely deployed ADAS functions—Lane Keeping Assist (LKA), Forward Collision Warning (FCW), Blind Spot Detection (BSD), and Automated Parking Assist. For each function, we detail the sensing architecture, governing mathematical models, decision and control logic, and representative validation criteria drawn from industrial practice. The models include a kinematic bicycle formulation for lateral control, constant‑velocity and constant‑acceleration time‑to‑collision kinematics for collision‑risk estimation, polygonal zone geometry for radar‑based blind‑spot monitoring, and a two‑arc geometric construction for parking path planning. Reference Python implementations and simulation results are provided, demonstrating RMS lateral offset of 0.29 m for LKA, FCW alert latency of 50 ms, BSD zone dwell accuracy within 0.5 s, and automated parking maneuver time of 4.83 s. A Monte‑Carlo sensitivity analysis of FCW thresholds illustrates the trade‑off between early warning and nuisance‑alert rate that governs production calibration. The unified treatment offered here provides a consolidated reference for researchers and engineers developing or evaluating ADAS sensing‑to‑actuation pipelines.